This paper considers a flow shop scheduling problem. The flow shop scheduling is
characterized “n” jobs and “m” machines in series with unidirectional flow of work with
variety of jobs being processed sequentially in a single pass manner. Most real world
problems are NP-hard in nature. The essential complexity of the problem necessitates the
use of meta-heuristics for solving flow shop scheduling problem. The paper addresses the
flow shop scheduling problem to minimize the makespan time with specific batch size
using Particle Swarm Optimization (PSO) and comparing the results with an real time
company production sequence.
A Discrete Firefly Algorithm for the Multi-Objective Hybrid Flowshop Scheduli...Xin-She Yang
The document describes a discrete firefly algorithm proposed to solve hybrid flowshop scheduling problems with two objectives: minimizing makespan and mean flow time. Hybrid flowshop scheduling problems involve scheduling jobs through multiple stages with parallel machines in some stages, and are known to be NP-hard. The proposed discrete firefly algorithm adapts the continuous firefly algorithm to the discrete problem by using a smallest position value rule to map continuous firefly positions to discrete job permutations. Computational experiments show the proposed algorithm outperforms other metaheuristics for hybrid flowshop scheduling problems.
Novel Heuristics for no-Wait Two Stage Multiprocessor Flow Shop with Probable...IRJET Journal
This document presents a study on scheduling algorithms for a two-stage manufacturing flow shop problem with rework and sequence-dependent setup times. It introduces novel heuristic algorithms like discrete particle swarm optimization, adapted imperialist competitive algorithm, and adapted invasive weed optimization. These algorithms are evaluated based on their ability to minimize the makespan (total completion time) for a given set of jobs in the flow shop. The adapted invasive weed optimization approach achieved superior performance compared to the other algorithms in computational experiments on this scheduling problem.
Job-shop manufacturing environment requires planning of schedules for the systems of low-volume having numerous variations. For a job-shop scheduling, ‘k’ number of operations and ‘n’ number of jobs on ‘m’ number of machines processed through an assured objective function to be minimized (makespan). This paper presents a capable genetic algorithm for the job-shop scheduling problems among operating parameters such as random population generation with a population size of 50, operation based chromosome structure, tournament selection as selection scheme, 2-point random crossover with probability 80%, 2-point mutation with probability 20%, elitism, repairing of chromosomes and no. of iteration is 1000. An algorithm is programmed for job shop scheduling problem using MATLAB 2009 a 7.8. The proposed genetic algorithm with certain operating parameters is applied to the two case studies taken from literature. The results also show that genetic algorithm is the best optimization technique for solving the scheduling problems of job shop manufacturing systems evolving shortest processing time and transportation time due to its implications to more practical and integrated problems.
A Hybrid Evolutionary Optimization Model for Solving Job Shop Scheduling Prob...iosrjce
The heuristic optimization techniques were commonly used in solving several optimization
problems. The present work aims to develop a hybrid algorithm to solve the scheduling optimization problem of
JSSP. There are different variants of these algorithms that were addressed in several previous works. The
impacts of these two kinds (Genetic Algorithm (GA) and Simulated Annealing (SA) based optimization model)
of initial condition on the performance of these two algorithms were studied using the convergence curve and
the achieved makespan. Even though genetic algorithm performed better than other evolutionary algorithms, it
has some weakness. During running GA, sometimes, it will produce same result without any improvement. SA
has a mechanism to overcome from that situation. During SA, if same result will be repeated, then it is rapidly
changing the change in temperature variable and re-initiates another random search. By using this feature of
SA, it has been implemented a hybrid based evolutionary model for solving JSSP by improving GA.
Comparison has been made with the performance of the proposed SA-GA-Hybrid model with GA as well as SA.
An efficient simulated annealing algorithm for a No-Wait Two Stage Flexible F...ijait
In this paper, no wait two stage flexible flow shop scheduling problem (FFSSP) is solved using two metaheuristic algorithms. This problem with minimum makespan performance measure is NP-Hard. The proposed algorithms are Simulated Annealing and Genetic Algorithm. The results are analyzed in terms of Relative Percentage Deviation of Makespan. The performance of the proposed algorithms are studied and compared with that of MDA algorithm. For this propose a number of problems in different sizes are solved. The results of the studies proposes the effective algorithm. This is followed by describing the
outline of the study, concluding remarks and suggesting potential areas for further researches
IMPROVED WORKLOAD BALANCING OF THE SCHEDULING JOBS WITH THE RELEASE DATES IN ...IJCSEA Journal
This paper presents the identical parallel machine’s scheduling problem when the jobs are submitted over time. This problem consists of assigning N various jobs to M identical parallel machines to reduce the workload imponderables among the different machines. We generalized the mixed-integer linear programming approach to decrease the workload imbalance between the different machines, and that is done by converting the problem to the mathematical model. The studied cases are presented for different problems, and it indicates to an online system, and this system does not know the arrival times of the jobs before and reduce Makespan criterion is not well appropriate to describe the utilization for this online problem. The obtained results proved good solutions for the scheduling problem compared with standard algorithms.
AN EFFICIENT HEURISTIC ALGORITHM FOR FLEXIBLE JOB SHOP SCHEDULING WITH MAINTE...mathsjournal
This document summarizes a heuristic algorithm for solving the flexible job shop scheduling problem with preventive maintenance constraints. The objectives are to minimize makespan, total workload, and maximum machine workload.
The algorithm uses a constructive procedure to sequentially assign each operation (job or maintenance) to machines based on a calculated total cost (TC) value. TC considers factors like processing time, workload balance, and maintenance window compliance. Multiple parameter settings are tested to generate initial solutions. Computational results on benchmark problems show the heuristic finds good quality solutions very quickly, making it suitable for practical applications.
Modified heuristic time deviation Technique for job sequencing and Computatio...ijcsit
The document discusses job sequencing and scheduling techniques. It begins by defining job sequencing as the arrangement of tasks to be performed in a specified order. It then discusses several algorithms and methods for solving job sequencing problems, including tabu search, fuzzy TOPSIS, genetic algorithms, Johnson's rule, and mathematical modeling approaches. It also covers classification of jobs, performance metrics, and discusses modified time deviation as a heuristic technique to arrange job sequences to minimize total elapsed time.
A Discrete Firefly Algorithm for the Multi-Objective Hybrid Flowshop Scheduli...Xin-She Yang
The document describes a discrete firefly algorithm proposed to solve hybrid flowshop scheduling problems with two objectives: minimizing makespan and mean flow time. Hybrid flowshop scheduling problems involve scheduling jobs through multiple stages with parallel machines in some stages, and are known to be NP-hard. The proposed discrete firefly algorithm adapts the continuous firefly algorithm to the discrete problem by using a smallest position value rule to map continuous firefly positions to discrete job permutations. Computational experiments show the proposed algorithm outperforms other metaheuristics for hybrid flowshop scheduling problems.
Novel Heuristics for no-Wait Two Stage Multiprocessor Flow Shop with Probable...IRJET Journal
This document presents a study on scheduling algorithms for a two-stage manufacturing flow shop problem with rework and sequence-dependent setup times. It introduces novel heuristic algorithms like discrete particle swarm optimization, adapted imperialist competitive algorithm, and adapted invasive weed optimization. These algorithms are evaluated based on their ability to minimize the makespan (total completion time) for a given set of jobs in the flow shop. The adapted invasive weed optimization approach achieved superior performance compared to the other algorithms in computational experiments on this scheduling problem.
Job-shop manufacturing environment requires planning of schedules for the systems of low-volume having numerous variations. For a job-shop scheduling, ‘k’ number of operations and ‘n’ number of jobs on ‘m’ number of machines processed through an assured objective function to be minimized (makespan). This paper presents a capable genetic algorithm for the job-shop scheduling problems among operating parameters such as random population generation with a population size of 50, operation based chromosome structure, tournament selection as selection scheme, 2-point random crossover with probability 80%, 2-point mutation with probability 20%, elitism, repairing of chromosomes and no. of iteration is 1000. An algorithm is programmed for job shop scheduling problem using MATLAB 2009 a 7.8. The proposed genetic algorithm with certain operating parameters is applied to the two case studies taken from literature. The results also show that genetic algorithm is the best optimization technique for solving the scheduling problems of job shop manufacturing systems evolving shortest processing time and transportation time due to its implications to more practical and integrated problems.
A Hybrid Evolutionary Optimization Model for Solving Job Shop Scheduling Prob...iosrjce
The heuristic optimization techniques were commonly used in solving several optimization
problems. The present work aims to develop a hybrid algorithm to solve the scheduling optimization problem of
JSSP. There are different variants of these algorithms that were addressed in several previous works. The
impacts of these two kinds (Genetic Algorithm (GA) and Simulated Annealing (SA) based optimization model)
of initial condition on the performance of these two algorithms were studied using the convergence curve and
the achieved makespan. Even though genetic algorithm performed better than other evolutionary algorithms, it
has some weakness. During running GA, sometimes, it will produce same result without any improvement. SA
has a mechanism to overcome from that situation. During SA, if same result will be repeated, then it is rapidly
changing the change in temperature variable and re-initiates another random search. By using this feature of
SA, it has been implemented a hybrid based evolutionary model for solving JSSP by improving GA.
Comparison has been made with the performance of the proposed SA-GA-Hybrid model with GA as well as SA.
An efficient simulated annealing algorithm for a No-Wait Two Stage Flexible F...ijait
In this paper, no wait two stage flexible flow shop scheduling problem (FFSSP) is solved using two metaheuristic algorithms. This problem with minimum makespan performance measure is NP-Hard. The proposed algorithms are Simulated Annealing and Genetic Algorithm. The results are analyzed in terms of Relative Percentage Deviation of Makespan. The performance of the proposed algorithms are studied and compared with that of MDA algorithm. For this propose a number of problems in different sizes are solved. The results of the studies proposes the effective algorithm. This is followed by describing the
outline of the study, concluding remarks and suggesting potential areas for further researches
IMPROVED WORKLOAD BALANCING OF THE SCHEDULING JOBS WITH THE RELEASE DATES IN ...IJCSEA Journal
This paper presents the identical parallel machine’s scheduling problem when the jobs are submitted over time. This problem consists of assigning N various jobs to M identical parallel machines to reduce the workload imponderables among the different machines. We generalized the mixed-integer linear programming approach to decrease the workload imbalance between the different machines, and that is done by converting the problem to the mathematical model. The studied cases are presented for different problems, and it indicates to an online system, and this system does not know the arrival times of the jobs before and reduce Makespan criterion is not well appropriate to describe the utilization for this online problem. The obtained results proved good solutions for the scheduling problem compared with standard algorithms.
AN EFFICIENT HEURISTIC ALGORITHM FOR FLEXIBLE JOB SHOP SCHEDULING WITH MAINTE...mathsjournal
This document summarizes a heuristic algorithm for solving the flexible job shop scheduling problem with preventive maintenance constraints. The objectives are to minimize makespan, total workload, and maximum machine workload.
The algorithm uses a constructive procedure to sequentially assign each operation (job or maintenance) to machines based on a calculated total cost (TC) value. TC considers factors like processing time, workload balance, and maintenance window compliance. Multiple parameter settings are tested to generate initial solutions. Computational results on benchmark problems show the heuristic finds good quality solutions very quickly, making it suitable for practical applications.
Modified heuristic time deviation Technique for job sequencing and Computatio...ijcsit
The document discusses job sequencing and scheduling techniques. It begins by defining job sequencing as the arrangement of tasks to be performed in a specified order. It then discusses several algorithms and methods for solving job sequencing problems, including tabu search, fuzzy TOPSIS, genetic algorithms, Johnson's rule, and mathematical modeling approaches. It also covers classification of jobs, performance metrics, and discusses modified time deviation as a heuristic technique to arrange job sequences to minimize total elapsed time.
Solving Multi-level, Multi-product and Multi-period Lot Sizing and Scheduling...Editor IJCATR
In this paper, a new model of capacitated lot sizing and scheduling in a permutation flow shop is developed. In this model
demand can be totally backlogged. Setups can be carryover and are sequence-dependent. It is well-known from literatures that
capacitated lot sizing problem in permutation flow shop systems are NP-hard. This means the model is solved in polynomial time and
metaheuristics algorithms are capable of solving these problems within reasonable computing load. Metaheuristic algorithms find more
applications in recent researches. On this concern this paper proposes two evolutionary algorithms, one of the most popular namely,
Genetic Algorithm (GA) and one of the most powerful population base algorithms namely, Imperialist Competitive Algorithm (ICA).
The proposed algorithms are calibrate by Taguchi method and be compared against a presented lower bound. Some numerical
examples are solved by both the algorithms and the lower bound. The quality of solution obtained by the proposed algorithm showed
superiority of ICA to GA.
A Model for Optimum Scheduling Of Non-Resumable Jobs on Parallel Production M...IOSR Journals
This document presents a mathematical model for optimizing the scheduling of non-resumable jobs on parallel production machines that are subject to multiple fixed periods of unavailability. The problem is formulated as an integer linear programming model to minimize the makespan. Two algorithms are proposed to solve the problem - the first uses the longest processing time rule to generate an initial solution and upper bound, and the second is a greedy algorithm that iteratively achieves an optimal solution by backtracking. The algorithms are implemented in software and validated using numerical examples and industrial case studies, showing makespan reductions of 49% and 40% compared to actual industry scheduling.
Machiwal, D. y Jha, MK (2012). Modelado estocástico de series de tiempo. En A...SandroSnchezZamora
This document provides an overview of stochastic modelling and different stochastic processes that are commonly used, including:
- Purely random (white noise) processes where data points are independent and identically distributed
- Autoregressive (AR) processes where each data point is modeled as a linear combination of previous data points plus noise
- Moving average (MA) processes where each data point is modeled as a linear combination of previous noise terms plus a constant
- Autoregressive moving average (ARMA) processes which combine AR and MA processes
- Autoregressive integrated moving average (ARIMA) processes which explicitly include differencing to make time series stationary
Manager’s Preferences Modeling within Multi-Criteria Flowshop Scheduling Prob...Waqas Tariq
This paper proposes a metaheuristic to solve the permutation flow shop scheduling problem where several criteria are to be considered, such as: the makespan, total flowtime and total tardiness of jobs. The proposed metaheuristic is based on tabu search algorithm. The Compromise Programming model and the concept of satisfaction functions are utilized to integrate explicitly the Manager’s preferences. The proposed approach has been tested through a computational experiment. This approach can be useful for large scale scheduling problems and the Manager can consider additional scheduling criteria.
Designing Unbalanced Assembly Lines: A Simulation Analysis to Evaluate Impact...IJERA Editor
The document summarizes a study that uses simulation to evaluate the impacts of unbalanced assembly lines on work-in-process inventory levels and line throughput. The simulation models a 5-workstation assembly line with different levels of imbalance, represented by variations in workstation cycle times. The results show that higher levels of imbalance, using a "bowl shape" configuration where cycle times decrease from the beginning to the middle and then increase again, can increase throughput while maintaining lower or similar levels of work-in-process inventory compared to a balanced line. Inverted bowl configurations, where the bottleneck is in the center, also perform well but require tighter buffer management to control inventory levels.
In industries, the completion time of job problems in the manufacturing unit has risen significantly. In several types of current study, the job's completion time, or makespan, is reduced by taking straight paths, which is time-consuming. In this paper, we used an Improved Ant Colony Optimization and Tabu Search (ACOTS) algorithm to solve this problem by precisely defining the fault occurrence location in order to rollback. We have used a short-term memory-based rollback recovery strategy to minimise the job's completion time by rolling back to its own short-term memory. The recent movements in Tabu quest are visited using short term memory. As compared to the ACO algorithm, our proposed ACOTS-Cmax solution is more efficient and takes less time to complete.
Hybrid Genetic Algorithm with Multiparents Crossover for Job Shop Scheduling ...CHUNG SIN ONG
The job shop scheduling problem (JSSP) is one of the well-known hard combinatorial scheduling problems. This paper proposes a hybrid genetic algorithm withmultiparents crossover for JSSP.Themultiparents crossover operator known as extended precedence preservative crossover (EPPX) is able to recombine more than two parents to generate a single new offspring distinguished from common crossover operators that recombine only two parents. This algorithm also embeds a schedule generation procedure to generate full-active schedule that satisfies precedence constraints in order to reduce the search space. Once a schedule is obtained, a neighborhood search is applied to exploit the search space for better solutions and to enhance the GA. This hybrid genetic algorithm is simulated on a set of benchmarks from the literatures and the results are compared with other approaches to ensure the sustainability of this algorithm in solving JSSP. The results suggest that the implementation of multiparents crossover produces competitive results.
This document discusses simulation techniques to assist in process design and optimization. It covers analyzing the degrees of freedom in a system to determine the number of independent equations and decision variables. Sequential modular flowsheeting is described as a technique to solve processes with recycle loops by tearing one stream and iteratively guessing its values. Methods for partitioning a process into groups of units that must be solved together and determining the precedence ordering of these groups are also presented.
This document presents a parallel GRASP algorithm for solving the job shop scheduling problem. The GRASP algorithm uses a restricted candidate list and local search to iteratively find feasible solutions. OpenMP directives are used to parallelize the main loop across multiple threads. Benchmark tests on different hardware show near-linear speedup as threads are increased, with larger problem instances taking longer to converge to the best solution.
Phenomenological Decomposition Heuristics for Process Design Synthesis of Oil...Alkis Vazacopoulos
The processing of a raw material is a phenomenon that varies its quantity and quality along a specific network and logics and logistics to transform it into final products. To capture the production framework in a mathematical programming model, a full space formulation integrating discrete design variables and quantity-quality relations gives rise to large scale non-convex mixed-integer nonlinear models, which are often difficult to solve. In order to overcome this problem, we propose a phenomenological decomposition heuristic to solve separately in a first stage the quantity and logic variables in a mixed-integer linear model, and in a second stage the quantity and quality variables in a nonlinear programming formulation. By considering different fuel demand scenarios, the problem becomes a two-stage stochastic programming model, where nonlinear models for each demand scenario are iteratively restricted by the process design results. Two examples demonstrate the tailor-made decomposition scheme to construct the complex oil-refinery process design in a quantitative manner.
The document summarizes job shop scheduling by:
1) Describing the job shop environment with m machines and n jobs that each follow predetermined routes on the machines that may differ between jobs and allow recirculation.
2) Formulating the problem as a network with nodes representing job operations and arcs representing precedence constraints, with the objective to minimize makespan.
3) Explaining how to construct feasible schedules by selecting disjunctive arcs from the network to create an acyclic graph and correspond to a machine sequence.
This document discusses various methods for selecting optimal input-output pairings for multivariable control systems. It begins with an introduction to the challenges of controlling multivariable systems and the importance of proper input-output pairing. It then reviews several pairing methods including the relative gain array (RGA), relative omega array, dynamic relative gain array, normalized RGA, and relative normalized gain array. It also discusses necessary conditions for decentralized integral controllability and presents rules for eliminating undesirable pairings to achieve this. Overall, the document provides an overview of established and newer techniques for analyzing interactions and selecting input-output pairs for multivariable processes.
Use Fuzzy Midrange Transformation Method to Construction Fuzzy Control Charts...CSCJournals
Statistical Process Control (SPC) is approach that uses statistical techniques to monitor the process. The techniques of quality control are widely used in controlling any kinds of processes. The widely used control charts are R X ? and S X ? charts. These are called traditional variable control chart, which consists of three horizontal lines called Centre Line (CL), Upper Control Limit (UCL) and Lower Control Limit (UCL) are represented by numeric values. The center line in a control chart denotes the average value of the quality characteristic under study. A process is either "in control" or "out of control" depending on numeric observation values. In the consideration of real production process, it is assumed that there are no doubts about observations and their values. But when these observations include human judgments, evaluations and decisions, a continuous random variable (xi) of a production process should include the variability caused by human subjectivity or measurement devices, or environmental conditions. So, linguistic terms can be used instead of an exact value of continuous random variable. In this context fuzzy set theory is useful tool to handle this uncertainty. Numeric control limits can be transformed to fuzzy control limits by using membership function, therefore; the concept of fuzzy control charts with ? cuts by using ? -level fuzzy midrange with trapezoidal fuzzy number (TRN) is proposed. The fuzzy control charts for arithmetic mean ( X ~ ), and range (R ~ ) are developed. Fuzzy control limits provide a more accurate and flexible evaluation. In this paper through a real illustrative data from Sulaimani Company for Cement in the city of Sulaimani, shows the designing of fuzzy control chart for process average of quality.
This chapter discusses process and measurement system capability analysis. It defines process capability as the uniformity of a process output. Process capability analysis estimates how well a process output will meet specification limits using metrics like Cp, Cpk, and Cpm. These metrics assume the output follows a normal distribution and the process is in statistical control. The chapter also discusses using histograms, probability plots, and control charts to analyze process capability, as well as designing experiments to determine sources of variability. It concludes by discussing measuring and accounting for measurement system variability separately from process variability.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
The document summarizes research in the field of stochastic scheduling. It discusses three broad categories of stochastic scheduling models: 1) models for scheduling a batch of stochastic jobs, 2) multi-armed bandit models, and 3) models for scheduling queueing systems. For many models in these categories, relatively simple priority-index rules have been shown to optimize important performance objectives like expected flowtime. However, more complex models generally lack tractable optimal solutions. Recent research thus focuses on designing near-optimal heuristic policies.
Comparative Analysis of Metaheuristic Approaches for Makespan Minimization fo...IJECEIAES
This paper provides comparative analysis of various metaheuristic approaches for m-machine no wait flow shop scheduling (NWFSS) problem with makespan as an optimality criterion. NWFSS problem is NP hard and brute force method unable to find the solutions so approximate solutions are found with metaheuristic algorithms. The objective is to find out the scheduling sequence of jobs to minimize total completion time. In order to meet the objective criterion, existing metaheuristic techniques viz. Tabu Search (TS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are implemented for small and large sized problems and effectiveness of these techniques are measured with statistical metric.
A review on non traditional algorithms for job shop schedulingiaemedu
The document provides a review of non-traditional algorithms that have been used for job shop scheduling problems. It discusses how job shop scheduling is an NP-hard problem and researchers have focused on hybrid methods and metaheuristics. The review covers various techniques including tabu search, genetic algorithms, simulated annealing, ant colony optimization, and iterative local search methods. It also includes tables summarizing different approximation algorithms and literature on job shop scheduling using techniques like priority dispatch rules, insertion algorithms, artificial intelligence methods, and local search methods.
In some industries as foundries, it is not technically feasible to interrupt a processor between jobs. This restriction gives rise to a scheduling problem called no-idle scheduling. This paper deals with scheduling of no-idle open shops to minimize maximum completion time of jobs, called makespan. The problem is first mathematically formulated by three different mixed integer linear programming models. Since open shop scheduling problems are NP-hard, only small instances can be solved to optimality using these models. Thus, to solve large instances, two meta-heuristics based on simulated annealing and genetic algorithms are developed. A complete numerical experiment is conducted and the developed models and algorithms are compared. The results show that genetic algorithm outperforms simulated annealing.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
This document discusses using a genetic algorithm to solve the job shop scheduling problem. It begins with an abstract that introduces using genetic algorithms for job shop scheduling. It then provides more details on the problem and discusses using genetic algorithms with modifications like generating the initial population using priority rules and incorporating critical block and disjunctive graph distance in the crossover and mutation operations. The document outlines the genetic algorithm approach with sections on chromosome representation and decoding, data structures, generating the initial population, crossover and mutation operations. The goal is to minimize the makespan value for job shop scheduling.
Solving Multi-level, Multi-product and Multi-period Lot Sizing and Scheduling...Editor IJCATR
In this paper, a new model of capacitated lot sizing and scheduling in a permutation flow shop is developed. In this model
demand can be totally backlogged. Setups can be carryover and are sequence-dependent. It is well-known from literatures that
capacitated lot sizing problem in permutation flow shop systems are NP-hard. This means the model is solved in polynomial time and
metaheuristics algorithms are capable of solving these problems within reasonable computing load. Metaheuristic algorithms find more
applications in recent researches. On this concern this paper proposes two evolutionary algorithms, one of the most popular namely,
Genetic Algorithm (GA) and one of the most powerful population base algorithms namely, Imperialist Competitive Algorithm (ICA).
The proposed algorithms are calibrate by Taguchi method and be compared against a presented lower bound. Some numerical
examples are solved by both the algorithms and the lower bound. The quality of solution obtained by the proposed algorithm showed
superiority of ICA to GA.
A Model for Optimum Scheduling Of Non-Resumable Jobs on Parallel Production M...IOSR Journals
This document presents a mathematical model for optimizing the scheduling of non-resumable jobs on parallel production machines that are subject to multiple fixed periods of unavailability. The problem is formulated as an integer linear programming model to minimize the makespan. Two algorithms are proposed to solve the problem - the first uses the longest processing time rule to generate an initial solution and upper bound, and the second is a greedy algorithm that iteratively achieves an optimal solution by backtracking. The algorithms are implemented in software and validated using numerical examples and industrial case studies, showing makespan reductions of 49% and 40% compared to actual industry scheduling.
Machiwal, D. y Jha, MK (2012). Modelado estocástico de series de tiempo. En A...SandroSnchezZamora
This document provides an overview of stochastic modelling and different stochastic processes that are commonly used, including:
- Purely random (white noise) processes where data points are independent and identically distributed
- Autoregressive (AR) processes where each data point is modeled as a linear combination of previous data points plus noise
- Moving average (MA) processes where each data point is modeled as a linear combination of previous noise terms plus a constant
- Autoregressive moving average (ARMA) processes which combine AR and MA processes
- Autoregressive integrated moving average (ARIMA) processes which explicitly include differencing to make time series stationary
Manager’s Preferences Modeling within Multi-Criteria Flowshop Scheduling Prob...Waqas Tariq
This paper proposes a metaheuristic to solve the permutation flow shop scheduling problem where several criteria are to be considered, such as: the makespan, total flowtime and total tardiness of jobs. The proposed metaheuristic is based on tabu search algorithm. The Compromise Programming model and the concept of satisfaction functions are utilized to integrate explicitly the Manager’s preferences. The proposed approach has been tested through a computational experiment. This approach can be useful for large scale scheduling problems and the Manager can consider additional scheduling criteria.
Designing Unbalanced Assembly Lines: A Simulation Analysis to Evaluate Impact...IJERA Editor
The document summarizes a study that uses simulation to evaluate the impacts of unbalanced assembly lines on work-in-process inventory levels and line throughput. The simulation models a 5-workstation assembly line with different levels of imbalance, represented by variations in workstation cycle times. The results show that higher levels of imbalance, using a "bowl shape" configuration where cycle times decrease from the beginning to the middle and then increase again, can increase throughput while maintaining lower or similar levels of work-in-process inventory compared to a balanced line. Inverted bowl configurations, where the bottleneck is in the center, also perform well but require tighter buffer management to control inventory levels.
In industries, the completion time of job problems in the manufacturing unit has risen significantly. In several types of current study, the job's completion time, or makespan, is reduced by taking straight paths, which is time-consuming. In this paper, we used an Improved Ant Colony Optimization and Tabu Search (ACOTS) algorithm to solve this problem by precisely defining the fault occurrence location in order to rollback. We have used a short-term memory-based rollback recovery strategy to minimise the job's completion time by rolling back to its own short-term memory. The recent movements in Tabu quest are visited using short term memory. As compared to the ACO algorithm, our proposed ACOTS-Cmax solution is more efficient and takes less time to complete.
Hybrid Genetic Algorithm with Multiparents Crossover for Job Shop Scheduling ...CHUNG SIN ONG
The job shop scheduling problem (JSSP) is one of the well-known hard combinatorial scheduling problems. This paper proposes a hybrid genetic algorithm withmultiparents crossover for JSSP.Themultiparents crossover operator known as extended precedence preservative crossover (EPPX) is able to recombine more than two parents to generate a single new offspring distinguished from common crossover operators that recombine only two parents. This algorithm also embeds a schedule generation procedure to generate full-active schedule that satisfies precedence constraints in order to reduce the search space. Once a schedule is obtained, a neighborhood search is applied to exploit the search space for better solutions and to enhance the GA. This hybrid genetic algorithm is simulated on a set of benchmarks from the literatures and the results are compared with other approaches to ensure the sustainability of this algorithm in solving JSSP. The results suggest that the implementation of multiparents crossover produces competitive results.
This document discusses simulation techniques to assist in process design and optimization. It covers analyzing the degrees of freedom in a system to determine the number of independent equations and decision variables. Sequential modular flowsheeting is described as a technique to solve processes with recycle loops by tearing one stream and iteratively guessing its values. Methods for partitioning a process into groups of units that must be solved together and determining the precedence ordering of these groups are also presented.
This document presents a parallel GRASP algorithm for solving the job shop scheduling problem. The GRASP algorithm uses a restricted candidate list and local search to iteratively find feasible solutions. OpenMP directives are used to parallelize the main loop across multiple threads. Benchmark tests on different hardware show near-linear speedup as threads are increased, with larger problem instances taking longer to converge to the best solution.
Phenomenological Decomposition Heuristics for Process Design Synthesis of Oil...Alkis Vazacopoulos
The processing of a raw material is a phenomenon that varies its quantity and quality along a specific network and logics and logistics to transform it into final products. To capture the production framework in a mathematical programming model, a full space formulation integrating discrete design variables and quantity-quality relations gives rise to large scale non-convex mixed-integer nonlinear models, which are often difficult to solve. In order to overcome this problem, we propose a phenomenological decomposition heuristic to solve separately in a first stage the quantity and logic variables in a mixed-integer linear model, and in a second stage the quantity and quality variables in a nonlinear programming formulation. By considering different fuel demand scenarios, the problem becomes a two-stage stochastic programming model, where nonlinear models for each demand scenario are iteratively restricted by the process design results. Two examples demonstrate the tailor-made decomposition scheme to construct the complex oil-refinery process design in a quantitative manner.
The document summarizes job shop scheduling by:
1) Describing the job shop environment with m machines and n jobs that each follow predetermined routes on the machines that may differ between jobs and allow recirculation.
2) Formulating the problem as a network with nodes representing job operations and arcs representing precedence constraints, with the objective to minimize makespan.
3) Explaining how to construct feasible schedules by selecting disjunctive arcs from the network to create an acyclic graph and correspond to a machine sequence.
This document discusses various methods for selecting optimal input-output pairings for multivariable control systems. It begins with an introduction to the challenges of controlling multivariable systems and the importance of proper input-output pairing. It then reviews several pairing methods including the relative gain array (RGA), relative omega array, dynamic relative gain array, normalized RGA, and relative normalized gain array. It also discusses necessary conditions for decentralized integral controllability and presents rules for eliminating undesirable pairings to achieve this. Overall, the document provides an overview of established and newer techniques for analyzing interactions and selecting input-output pairs for multivariable processes.
Use Fuzzy Midrange Transformation Method to Construction Fuzzy Control Charts...CSCJournals
Statistical Process Control (SPC) is approach that uses statistical techniques to monitor the process. The techniques of quality control are widely used in controlling any kinds of processes. The widely used control charts are R X ? and S X ? charts. These are called traditional variable control chart, which consists of three horizontal lines called Centre Line (CL), Upper Control Limit (UCL) and Lower Control Limit (UCL) are represented by numeric values. The center line in a control chart denotes the average value of the quality characteristic under study. A process is either "in control" or "out of control" depending on numeric observation values. In the consideration of real production process, it is assumed that there are no doubts about observations and their values. But when these observations include human judgments, evaluations and decisions, a continuous random variable (xi) of a production process should include the variability caused by human subjectivity or measurement devices, or environmental conditions. So, linguistic terms can be used instead of an exact value of continuous random variable. In this context fuzzy set theory is useful tool to handle this uncertainty. Numeric control limits can be transformed to fuzzy control limits by using membership function, therefore; the concept of fuzzy control charts with ? cuts by using ? -level fuzzy midrange with trapezoidal fuzzy number (TRN) is proposed. The fuzzy control charts for arithmetic mean ( X ~ ), and range (R ~ ) are developed. Fuzzy control limits provide a more accurate and flexible evaluation. In this paper through a real illustrative data from Sulaimani Company for Cement in the city of Sulaimani, shows the designing of fuzzy control chart for process average of quality.
This chapter discusses process and measurement system capability analysis. It defines process capability as the uniformity of a process output. Process capability analysis estimates how well a process output will meet specification limits using metrics like Cp, Cpk, and Cpm. These metrics assume the output follows a normal distribution and the process is in statistical control. The chapter also discusses using histograms, probability plots, and control charts to analyze process capability, as well as designing experiments to determine sources of variability. It concludes by discussing measuring and accounting for measurement system variability separately from process variability.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
The document summarizes research in the field of stochastic scheduling. It discusses three broad categories of stochastic scheduling models: 1) models for scheduling a batch of stochastic jobs, 2) multi-armed bandit models, and 3) models for scheduling queueing systems. For many models in these categories, relatively simple priority-index rules have been shown to optimize important performance objectives like expected flowtime. However, more complex models generally lack tractable optimal solutions. Recent research thus focuses on designing near-optimal heuristic policies.
Comparative Analysis of Metaheuristic Approaches for Makespan Minimization fo...IJECEIAES
This paper provides comparative analysis of various metaheuristic approaches for m-machine no wait flow shop scheduling (NWFSS) problem with makespan as an optimality criterion. NWFSS problem is NP hard and brute force method unable to find the solutions so approximate solutions are found with metaheuristic algorithms. The objective is to find out the scheduling sequence of jobs to minimize total completion time. In order to meet the objective criterion, existing metaheuristic techniques viz. Tabu Search (TS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are implemented for small and large sized problems and effectiveness of these techniques are measured with statistical metric.
A review on non traditional algorithms for job shop schedulingiaemedu
The document provides a review of non-traditional algorithms that have been used for job shop scheduling problems. It discusses how job shop scheduling is an NP-hard problem and researchers have focused on hybrid methods and metaheuristics. The review covers various techniques including tabu search, genetic algorithms, simulated annealing, ant colony optimization, and iterative local search methods. It also includes tables summarizing different approximation algorithms and literature on job shop scheduling using techniques like priority dispatch rules, insertion algorithms, artificial intelligence methods, and local search methods.
In some industries as foundries, it is not technically feasible to interrupt a processor between jobs. This restriction gives rise to a scheduling problem called no-idle scheduling. This paper deals with scheduling of no-idle open shops to minimize maximum completion time of jobs, called makespan. The problem is first mathematically formulated by three different mixed integer linear programming models. Since open shop scheduling problems are NP-hard, only small instances can be solved to optimality using these models. Thus, to solve large instances, two meta-heuristics based on simulated annealing and genetic algorithms are developed. A complete numerical experiment is conducted and the developed models and algorithms are compared. The results show that genetic algorithm outperforms simulated annealing.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
This document discusses using a genetic algorithm to solve the job shop scheduling problem. It begins with an abstract that introduces using genetic algorithms for job shop scheduling. It then provides more details on the problem and discusses using genetic algorithms with modifications like generating the initial population using priority rules and incorporating critical block and disjunctive graph distance in the crossover and mutation operations. The document outlines the genetic algorithm approach with sections on chromosome representation and decoding, data structures, generating the initial population, crossover and mutation operations. The goal is to minimize the makespan value for job shop scheduling.
This document presents a mathematical model for optimizing the scheduling of non-resumable jobs on parallel production machines that are subject to multiple fixed periods of unavailability. The problem is formulated as an integer linear programming model to minimize the makespan. Two algorithms are proposed to solve the problem - the first uses the longest processing time rule to generate an initial solution and upper bound, and the second is a greedy algorithm that iteratively achieves an optimal solution by backtracking. The algorithms are implemented in software and validated using numerical examples and industrial case studies, showing makespan reductions of 49% and 40% compared to actual industry scheduling.
Effective Hybrid Algorithms for No-Wait Flowshop Scheduling ProblemIRJET Journal
This document describes a study that proposes hybrid genetic algorithms to solve no-wait flowshop scheduling problems. The goal is to minimize the total makespan (completion time). A genetic algorithm and three hybrid genetic algorithms that incorporate local search methods are presented. The hybrid algorithms are shown to outperform the basic genetic algorithm, particularly for larger problem instances, by finding solutions with lower total makespan values. Computational results on benchmark problems support that the hybrid algorithms that combine two local search methods perform the best overall.
Design and Implementation of a Multi-Agent System for the Job Shop Scheduling...CSCJournals
This document proposes an agent-based parallel genetic algorithm for solving the job shop scheduling problem. The approach divides the genetic algorithm population into multiple subpopulations that are evolved independently in parallel on different hosts. Agents are used to create the initial populations in parallel and execute the genetic algorithm operations. The genetic algorithm runs in execution phases where subpopulations evolve independently, and migration phases where the subpopulations exchange solutions. Experimental results showed this parallel approach improves the performance over a non-parallel genetic algorithm.
This hybrid evolutionary optimization model combines genetic algorithm and simulated annealing to solve job shop scheduling problems. It initializes a population of solutions and then iterates through generations. In each generation, genetic operations like crossover and mutation are applied to perturb the solutions. The new solutions are evaluated and sorted by fitness. The best solution is then accepted or replaced using a simulated annealing mechanism that considers solution quality and temperature parameter. This allows acceptance of worse solutions to avoid local optima. The process repeats with temperature reduction until termination. The model is proposed to improve on genetic algorithm alone by incorporating simulated annealing's ability to escape local optima.
An efficient simulated annealing algorithm for a No-Wait Two Stage Flexible F...ijait
The document summarizes a study that proposes two metaheuristic algorithms - simulated annealing and genetic algorithm - to solve a no-wait two stage flexible flow shop scheduling problem. The problem involves scheduling a set of jobs to be processed on parallel machines across two stages with the objective of minimizing makespan (completion time). The algorithms generate job sequences stochastically and use a constructive heuristic to assign jobs to machines. The performance of the proposed algorithms is evaluated by solving test problems of different sizes and their results are compared to an existing Minimum Deviation Algorithm.
This document discusses scheduling optimization for flexible manufacturing systems using genetic algorithms. It begins with an introduction to flexible manufacturing systems and some of the challenges in scheduling their operations. The author then reviews previous research on scheduling in flexible manufacturing systems using various methods like heuristics, mathematical programming, and metaheuristics. The paper goes on to present a genetic algorithm approach for scheduling a flexible manufacturing system with the goals of minimizing machine idle time and penalty costs for missed deadlines. Software is developed to obtain an optimal schedule. The genetic algorithm finds the global optimum schedule after 1700 generations.
Discrete penguins search optimization algorithm to solve flow shop schedulin...IJECEIAES
Flow shop scheduling problem is one of the most classical NP-hard optimization problem. Which aims to find the best planning that minimizes the makespan (total completion time) of a set of tasks in a set of machines with certain constraints. In this paper, we propose a new nature inspired metaheuristic to solve the flow shop scheduling problem (FSSP), called penguins search optimization algorithm (PeSOA) based on collaborative hunting strategy of penguins.The operators and parameter values of PeSOA redefined to solve this problem. The performance of the penguins search optimization algorithm is tested on a set of benchmarks instances of FSSP from OR-Library, The results of the tests show that PeSOA is superior to some other metaheuristics algorithms, in terms of the quality of the solutions found and the execution time.
IMPROVED MUSIC BASED HARMONY SEARCH (IMBHS) FOR SOLVING JOB SHOP SCHEDULING P...ijpla
The document describes an Improved Music Based Harmony Search (IMBHS) algorithm for solving Job Shop Scheduling Problems (JSSPs). The traditional Music Based Harmony Search (MBHS) algorithm uses fixed parameters that can limit its performance. The IMBHS algorithm improves on MBHS by adjusting the Pitch Adjustment Rate (PAR) and bandwidth (bw) parameters during the search process - using larger PAR and smaller bw in later iterations to fine tune solutions. The authors apply both MBHS and IMBHS to benchmark JSSP instances and find that IMBHS often finds better quality solutions than MBHS and benchmark solutions. The results suggest IMBHS is an effective new approach for solving JSSPs.
Solving Assembly Line Balancing Problem Using A Hybrid Genetic Algorithm With...inventionjournals
In this paper, we propose a hybrid genetic algorithm to solve assembly line balancing problem. We put into the optimization framework of maximizing assembly line efficiency and minimizing total idle time simultaneously. The model is able to deal with more realistic situation of assembly line balancing problem such as zoning constraints. The genetic algorithm may lack the capability of exploring the solution space effectively, so we aim to provide its exploring capability by sequentially hybridizing the well-known assignment rules heuristics with genetic algorithm.
Solving Assembly Line Balancing Problem Using A Hybrid Genetic Algorithm With...inventionjournals
In this paper, we propose a hybrid genetic algorithm to solve assembly line balancing problem. We put into the optimization framework of maximizing assembly line efficiency and minimizing total idle time simultaneously. The model is able to deal with more realistic situation of assembly line balancing problem such as zoning constraints. The genetic algorithm may lack the capability of exploring the solution space effectively, so we aim to provide its exploring capability by sequentially hybridizing the well-known assignment rules heuristics with genetic algorithm
A MULTI-POPULATION BASED FROG-MEMETIC ALGORITHM FOR JOB SHOP SCHEDULING PROBLEMacijjournal
The Job Shop Scheduling Problem (JSSP) is a well known practical planning problem in the
manufacturing sector. We have considered the JSSP with an objective of minimizing makespan. In this
paper, we develop a three-stage hybrid approach called JSFMA to solve the JSSP. In JSFMA,
considering a method similar to Shuffled Frog Leaping algorithm we divide the population in several sub
populations and then solve the problem using a Memetic algorithm. The proposed approach have been
compared with other algorithms for the Job Shop Scheduling and evaluated with satisfactory results on a
set of the JSSP instances derived from classical Job Shop Scheduling benchmarks. We have solved 20
benchmark problems from Lawrence’s datasets and compared the results obtained with the results of the
algorithms established in the literature. The experimental results show that JSFMA could gain the best
known makespan in 17 out of 20 problems.
Differential Evolution and Simulated Annealing Technique for Design and Devel...IRJET Journal
This document discusses using differential evolution (DE) and simulated annealing (SA) algorithms to optimize scheduling for simultaneous machine and automated guided vehicle (AGV) systems in a flexible manufacturing system (FMS). The objectives are to minimize make span and mean tardiness. Benchmark problems from literature involving multiple machines, jobs, layouts, and AGVs are modeled and solved using DE and SA. Results show DE finds better solutions with lower make spans than SA and other algorithms from literature. Convergence graphs also indicate DE converges to optimal solutions faster for the benchmark problems considered. The authors conclude DE is more efficient and effective than SA and other algorithms for optimizing simultaneous scheduling in FMSs.
Bi criteria scheduling on parallel machines under fuzzy processing timeboujazra
This paper addresses bi-criteria scheduling on parallel machines to optimize total tardiness and weighted flow time under fuzzy processing times. Total tardiness is minimized as the primary objective, and weighted flow time is minimized as the secondary objective without violating the primary objective. Fuzzy set theory is used to represent uncertainty in job processing times using triangular membership functions. An algorithm is proposed and tested through a numerical example to find optimal job sequences.
Hybridizing guided genetic algorithm and single-based metaheuristics to solve...IAESIJAI
This paper focuses on solving unrelated parallel machine scheduling with
resource constraints (UPMR). There are j jobs, and each job needs to be
processed on one of the machines aim at minimizing the makespan. Besides
the dependence of the machine, the processing time of any job depends on the
usage of a rare renewable resource. A certain number of those resources (Rmax)
can be disseminated to jobs for the purpose of processing them at any time,
and each job j needs units of resources (rjm) when processing in machine m.
When more resources are assigned to a job, the job processing time minimizes.
However, the number of resources available is limited, and this makes the
problem difficult to solve for a good quality solution. Genetic algorithm shows
promising results in solving UPMR. However, genetic algorithm suffers from
premature convergence, which could hinder the resulting quality. Therefore,
the work hybridizes guided genetic algorithm (GGA) with a single-based
metaheuristics (SBHs) to handle the premature convergence in the genetic
algorithm with the aim to escape from the local optima and improve the
solution quality further. The single-based metaheuristics replaces the mutation
in the genetic algorithm. The evaluation of the algorithm performance was
conducted through extensive experiments.
A Hybrid Pareto Based Multi Objective Evolutionary Algorithm for a Partial Fl...IOSRJM
In this paper, A Partial flexible, open-shop scheduling problem (FOSP) is a combinatorial optimization problem. This work, with the objective of optimizing the makespan of an FOSP uses a hybrid Pareto based optimization (HPBO) approach. The problems are tested on Taillard’s benchmark problems. The consequences of Nawaz, Encore and Ham (NEH) meta heuristic are introduced to the HPBO to direct the search into a quality space. Variable neighbourhood search for (VNS) is employed to overcome the early convergence of the HPBO and helps in global search. The results are compared with standalone HPBO, traditional meta heuristics and the Taillard’s upper bounds. Five problem locate are taken from Taillard’s benchmark problems and are solved for various problem sizes. Thus a total of 35 problems is given to explain. The experimental results show that the solution quality of FOSP can be improved if the search is directed in a quality space in light of the proposed LHPBO approach (LHPBO-NEH-VNS).
Submission Deadline: 30th September 2022
Acceptance Notification: Within Three Days’ time period
Online Publication: Within 24 Hrs. time Period
Expected Date of Dispatch of Printed Journal: 5th October 2022
MODELING AND ANALYSIS OF SURFACE ROUGHNESS AND WHITE LATER THICKNESS IN WIRE-...IAEME Publication
White layer thickness (WLT) formed and surface roughness in wire electric discharge turning (WEDT) of tungsten carbide composite has been made to model through response surface methodology (RSM). A Taguchi’s standard Design of experiments involving five input variables with three levels has been employed to establish a mathematical model between input parameters and responses. Percentage of cobalt content, spindle speed, Pulse on-time, wire feed and pulse off-time were changed during the experimental tests based on the Taguchi’s orthogonal array L27 (3^13). Analysis of variance (ANOVA) revealed that the mathematical models obtained can adequately describe performance within the parameters of the factors considered. There was a good agreement between the experimental and predicted values in this study.
A STUDY ON THE REASONS FOR TRANSGENDER TO BECOME ENTREPRENEURSIAEME Publication
The study explores the reasons for a transgender to become entrepreneurs. In this study transgender entrepreneur was taken as independent variable and reasons to become as dependent variable. Data were collected through a structured questionnaire containing a five point Likert Scale. The study examined the data of 30 transgender entrepreneurs in Salem Municipal Corporation of Tamil Nadu State, India. Simple Random sampling technique was used. Garrett Ranking Technique (Percentile Position, Mean Scores) was used as the analysis for the present study to identify the top 13 stimulus factors for establishment of trans entrepreneurial venture. Economic advancement of a nation is governed upon the upshot of a resolute entrepreneurial doings. The conception of entrepreneurship has stretched and materialized to the socially deflated uncharted sections of transgender community. Presently transgenders have smashed their stereotypes and are making recent headlines of achievements in various fields of our Indian society. The trans-community is gradually being observed in a new light and has been trying to achieve prospective growth in entrepreneurship. The findings of the research revealed that the optimistic changes are taking place to change affirmative societal outlook of the transgender for entrepreneurial ventureship. It also laid emphasis on other transgenders to renovate their traditional living. The paper also highlights that legislators, supervisory body should endorse an impartial canons and reforms in Tamil Nadu Transgender Welfare Board Association.
BROAD UNEXPOSED SKILLS OF TRANSGENDER ENTREPRENEURSIAEME Publication
Since ages gender difference is always a debatable theme whether caused by nature, evolution or environment. The birth of a transgender is dreadful not only for the child but also for their parents. The pain of living in the wrong physique and treated as second class victimized citizen is outrageous and fully harboured with vicious baseless negative scruples. For so long, social exclusion had perpetuated inequality and deprivation experiencing ingrained malign stigma and besieged victims of crime or violence across their life spans. They are pushed into the murky way of life with a source of eternal disgust, bereft sexual potency and perennial fear. Although they are highly visible but very little is known about them. The common public needs to comprehend the ravaged arrogance on these insensitive souls and assist in integrating them into the mainstream by offering equal opportunity, treat with humanity and respect their dignity. Entrepreneurship in the current age is endorsing the gender fairness movement. Unstable careers and economic inadequacy had inclined one of the gender variant people called Transgender to become entrepreneurs. These tiny budding entrepreneurs resulted in economic transition by means of employment, free from the clutches of stereotype jobs, raised standard of living and handful of financial empowerment. Besides all these inhibitions, they were able to witness a platform for skill set development that ignited them to enter into entrepreneurial domain. This paper epitomizes skill sets involved in trans-entrepreneurs of Thoothukudi Municipal Corporation of Tamil Nadu State and is a groundbreaking determination to sightsee various skills incorporated and the impact on entrepreneurship.
DETERMINANTS AFFECTING THE USER'S INTENTION TO USE MOBILE BANKING APPLICATIONSIAEME Publication
The banking and financial services industries are experiencing increased technology penetration. Among them, the banking industry has made technological advancements to better serve the general populace. The economy focused on transforming the banking sector's system into a cashless, paperless, and faceless one. The researcher wants to evaluate the user's intention for utilising a mobile banking application. The study also examines the variables affecting the user's behaviour intention when selecting specific applications for financial transactions. The researcher employed a well-structured questionnaire and a descriptive study methodology to gather the respondents' primary data utilising the snowball sampling technique. The study includes variables like performance expectations, effort expectations, social impact, enabling circumstances, and perceived risk. Each of the aforementioned variables has a major impact on how users utilise mobile banking applications. The outcome will assist the service provider in comprehending the user's history with mobile banking applications.
ANALYSE THE USER PREDILECTION ON GPAY AND PHONEPE FOR DIGITAL TRANSACTIONSIAEME Publication
Technology upgradation in banking sector took the economy to view that payment mode towards online transactions using mobile applications. This system enabled connectivity between banks, Merchant and user in a convenient mode. there are various applications used for online transactions such as Google pay, Paytm, freecharge, mobikiwi, oxygen, phonepe and so on and it also includes mobile banking applications. The study aimed at evaluating the predilection of the user in adopting digital transaction. The study is descriptive in nature. The researcher used random sample techniques to collect the data. The findings reveal that mobile applications differ with the quality of service rendered by Gpay and Phonepe. The researcher suggest the Phonepe application should focus on implementing the application should be user friendly interface and Gpay on motivating the users to feel the importance of request for money and modes of payments in the application.
VOICE BASED ATM FOR VISUALLY IMPAIRED USING ARDUINOIAEME Publication
The prototype of a voice-based ATM for visually impaired using Arduino is to help people who are blind. This uses RFID cards which contain users fingerprint encrypted on it and interacts with the users through voice commands. ATM operates when sensor detects the presence of one person in the cabin. After scanning the RFID card, it will ask to select the mode like –normal or blind. User can select the respective mode through voice input, if blind mode is selected the balance check or cash withdraw can be done through voice input. Normal mode procedure is same as the existing ATM.
IMPACT OF EMOTIONAL INTELLIGENCE ON HUMAN RESOURCE MANAGEMENT PRACTICES AMONG...IAEME Publication
There is increasing acceptability of emotional intelligence as a major factor in personality assessment and effective human resource management. Emotional intelligence as the ability to build capacity, empathize, co-operate, motivate and develop others cannot be divorced from both effective performance and human resource management systems. The human person is crucial in defining organizational leadership and fortunes in terms of challenges and opportunities and walking across both multinational and bilateral relationships. The growing complexity of the business world requires a great deal of self-confidence, integrity, communication, conflict and diversity management to keep the global enterprise within the paths of productivity and sustainability. Using the exploratory research design and 255 participants the result of this original study indicates strong positive correlation between emotional intelligence and effective human resource management. The paper offers suggestions on further studies between emotional intelligence and human capital development and recommends for conflict management as an integral part of effective human resource management.
VISUALISING AGING PARENTS & THEIR CLOSE CARERS LIFE JOURNEY IN AGING ECONOMYIAEME Publication
Our life journey, in general, is closely defined by the way we understand the meaning of why we coexist and deal with its challenges. As we develop the "inspiration economy", we could say that nearly all of the challenges we have faced are opportunities that help us to discover the rest of our journey. In this note paper, we explore how being faced with the opportunity of being a close carer for an aging parent with dementia brought intangible discoveries that changed our insight of the meaning of the rest of our life journey.
A STUDY ON THE IMPACT OF ORGANIZATIONAL CULTURE ON THE EFFECTIVENESS OF PERFO...IAEME Publication
The main objective of this study is to analyze the impact of aspects of Organizational Culture on the Effectiveness of the Performance Management System (PMS) in the Health Care Organization at Thanjavur. Organizational Culture and PMS play a crucial role in present-day organizations in achieving their objectives. PMS needs employees’ cooperation to achieve its intended objectives. Employees' cooperation depends upon the organization’s culture. The present study uses exploratory research to examine the relationship between the Organization's culture and the Effectiveness of the Performance Management System. The study uses a Structured Questionnaire to collect the primary data. For this study, Thirty-six non-clinical employees were selected from twelve randomly selected Health Care organizations at Thanjavur. Thirty-two fully completed questionnaires were received.
Living in 21st century in itself reminds all of us the necessity of police and its administration. As more and more we are entering into the modern society and culture, the more we require the services of the so called ‘Khaki Worthy’ men i.e., the police personnel. Whether we talk of Indian police or the other nation’s police, they all have the same recognition as they have in India. But as already mentioned, their services and requirements are different after the like 26th November, 2008 incidents, where they without saving their own lives has sacrificed themselves without any hitch and without caring about their respective family members and wards. In other words, they are like our heroes and mentors who can guide us from the darkness of fear, militancy, corruption and other dark sides of life and so on. Now the question arises, if Gandhi would have been alive today, what would have been his reaction/opinion to the police and its functioning? Would he have some thing different in his mind now what he had been in his mind before the partition or would he be going to start some Satyagraha in the form of some improvement in the functioning of the police administration? Really these questions or rather night mares can come to any one’s mind, when there is too much confusion is prevailing in our minds, when there is too much corruption in the society and when the polices working is also in the questioning because of one or the other case throughout the India. It is matter of great concern that we have to thing over our administration and our practical approach because the police personals are also like us, they are part and parcel of our society and among one of us, so why we all are pin pointing towards them.
A STUDY ON TALENT MANAGEMENT AND ITS IMPACT ON EMPLOYEE RETENTION IN SELECTED...IAEME Publication
The goal of this study was to see how talent management affected employee retention in the selected IT organizations in Chennai. The fundamental issue was the difficulty to attract, hire, and retain talented personnel who perform well and the gap between supply and demand of talent acquisition and retaining them within the firms. The study's main goals were to determine the impact of talent management on employee retention in IT companies in Chennai, investigate talent management strategies that IT companies could use to improve talent acquisition, performance management, career planning and formulate retention strategies that the IT firms could use. The respondents were given a structured close-ended questionnaire with the 5 Point Likert Scale as part of the study's quantitative research design. The target population consisted of 289 IT professionals. The questionnaires were distributed and collected by the researcher directly. The Statistical Package for Social Sciences (SPSS) was used to collect and analyse the questionnaire responses. Hypotheses that were formulated for the various areas of the study were tested using a variety of statistical tests. The key findings of the study suggested that talent management had an impact on employee retention. The studies also found that there is a clear link between the implementation of talent management and retention measures. Management should provide enough training and development for employees, clarify job responsibilities, provide adequate remuneration packages, and recognise employees for exceptional performance.
ATTRITION IN THE IT INDUSTRY DURING COVID-19 PANDEMIC: LINKING EMOTIONAL INTE...IAEME Publication
Globally, Millions of dollars were spent by the organizations for employing skilled Information Technology (IT) professionals. It is costly to replace unskilled employees with IT professionals possessing technical skills and competencies that aid in interconnecting the business processes. The organization’s employment tactics were forced to alter by globalization along with technological innovations as they consistently diminish to remain lean, outsource to concentrate on core competencies along with restructuring/reallocate personnel to gather efficiency. As other jobs, organizations or professions have become reasonably more appropriate in a shifting employment landscape, the above alterations trigger both involuntary as well as voluntary turnover. The employee view on jobs is also afflicted by the COVID-19 pandemic along with the employee-driven labour market. So, having effective strategies is necessary to tackle the withdrawal rate of employees. By associating Emotional Intelligence (EI) along with Talent Management (TM) in the IT industry, the rise in attrition rate was analyzed in this study. Only 303 respondents were collected out of 350 participants to whom questionnaires were distributed. From the employees of IT organizations located in Bangalore (India), the data were congregated. A simple random sampling methodology was employed to congregate data as of the respondents. Generating the hypothesis along with testing is eventuated. The effect of EI and TM along with regression analysis between TM and EI was analyzed. The outcomes indicated that employee and Organizational Performance (OP) were elevated by effective EI along with TM.
INFLUENCE OF TALENT MANAGEMENT PRACTICES ON ORGANIZATIONAL PERFORMANCE A STUD...IAEME Publication
By implementing talent management strategy, organizations would have the option to retain their skilled professionals while additionally working on their overall performance. It is the course of appropriately utilizing the ideal individuals, setting them up for future top positions, exploring and dealing with their performance, and holding them back from leaving the organization. It is employee performance that determines the success of every organization. The firm quickly obtains an upper hand over its rivals in the event that its employees having particular skills that cannot be duplicated by the competitors. Thus, firms are centred on creating successful talent management practices and processes to deal with the unique human resources. Firms are additionally endeavouring to keep their top/key staff since on the off chance that they leave; the whole store of information leaves the firm's hands. The study's objective was to determine the impact of talent management on organizational performance among the selected IT organizations in Chennai. The study recommends that talent management limitedly affects performance. On the off chance that this talent is appropriately management and implemented properly, organizations might benefit as much as possible from their maintained assets to support development and productivity, both monetarily and non-monetarily.
A STUDY OF VARIOUS TYPES OF LOANS OF SELECTED PUBLIC AND PRIVATE SECTOR BANKS...IAEME Publication
Banking regulations act of India, 1949 defines banking as “acceptance of deposits for the purpose of lending or investment from the public, repayment on demand or otherwise and withdrawable through cheques, drafts order or otherwise”, the major participants of the Indian financial system are commercial banks, the financial institution encompassing term lending institutions. Investments institutions, specialized financial institution and the state level development banks, non banking financial companies (NBFC) and other market intermediaries such has the stock brokers and money lenders are among the oldest of the certain variants of NBFC and the oldest market participants. The asset quality of banks is one of the most important indicators of their financial health. The Indian banking sector has been facing severe problems of increasing Non- Performing Assets (NPAs). The NPAs growth directly and indirectly affects the quality of assets and profitability of banks. It also shows the efficiency of banks credit risk management and the recovery effectiveness. NPA do not generate any income, whereas, the bank is required to make provisions for such as assets that why is a double edge weapon. This paper outlines the concept of quality of bank loans of different types like Housing, Agriculture and MSME loans in state Haryana of selected public and private sector banks. This study is highlighting problems associated with the role of commercial bank in financing Small and Medium Scale Enterprises (SME). The overall objective of the research was to assess the effect of the financing provisions existing for the setting up and operations of MSMEs in the country and to generate recommendations for more robust financing mechanisms for successful operation of the MSMEs, in turn understanding the impact of MSME loans on financial institutions due to NPA. There are many research conducted on the topic of Non- Performing Assets (NPA) Management, concerning particular bank, comparative study of public and private banks etc. In this paper the researcher is considering the aggregate data of selected public sector and private sector banks and attempts to compare the NPA of Housing, Agriculture and MSME loans in state Haryana of public and private sector banks. The tools used in the study are average and Anova test and variance. The findings reveal that NPA is common problem for both public and private sector banks and is associated with all types of loans either that is housing loans, agriculture loans and loans to SMES. NPAs of both public and private sector banks show the increasing trend. In 2010-11 GNPA of public and private sector were at same level it was 2% but after 2010-11 it increased in many fold and at present there is GNPA in some more than 15%. It shows the dark area of Indian banking sector.
EXPERIMENTAL STUDY OF MECHANICAL AND TRIBOLOGICAL RELATION OF NYLON/BaSO4 POL...IAEME Publication
An experiment conducted in this study found that BaSO4 changed Nylon 6's mechanical properties. By changing the weight ratios, BaSO4 was used to make Nylon 6. This Researcher looked into how hard Nylon-6/BaSO4 composites are and how well they wear. Experiments were done based on Taguchi design L9. Nylon-6/BaSO4 composites can be tested for their hardness number using a Rockwell hardness testing apparatus. On Nylon/BaSO4, the wear behavior was measured by a wear monitor, pinon-disc friction by varying reinforcement, sliding speed, and sliding distance, and the microstructure of the crack surfaces was observed by SEM. This study provides significant contributions to ultimate strength by increasing BaSO4 content up to 16% in the composites, and sliding speed contributes 72.45% to the wear rate
ROLE OF SOCIAL ENTREPRENEURSHIP IN RURAL DEVELOPMENT OF INDIA - PROBLEMS AND ...IAEME Publication
The majority of the population in India lives in villages. The village is the back bone of the country. Village or rural industries play an important role in the national economy, particularly in the rural development. Developing the rural economy is one of the key indicators towards a country’s success. Whether it be the need to look after the welfare of the farmers or invest in rural infrastructure, Governments have to ensure that rural development isn’t compromised. The economic development of our country largely depends on the progress of rural areas and the standard of living of rural masses. Village or rural industries play an important role in the national economy, particularly in the rural development. Rural entrepreneurship is based on stimulating local entrepreneurial talent and the subsequent growth of indigenous enterprises. It recognizes opportunity in the rural areas and accelerates a unique blend of resources either inside or outside of agriculture. Rural entrepreneurship brings an economic value to the rural sector by creating new methods of production, new markets, new products and generate employment opportunities thereby ensuring continuous rural development. Social Entrepreneurship has the direct and primary objective of serving the society along with the earning profits. So, social entrepreneurship is different from the economic entrepreneurship as its basic objective is not to earn profits but for providing innovative solutions to meet the society needs which are not taken care by majority of the entrepreneurs as they are in the business for profit making as a sole objective. So, the Social Entrepreneurs have the huge growth potential particularly in the developing countries like India where we have huge societal disparities in terms of the financial positions of the population. Still 22 percent of the Indian population is below the poverty line and also there is disparity among the rural & urban population in terms of families living under BPL. 25.7 percent of the rural population & 13.7 percent of the urban population is under BPL which clearly shows the disparity of the poor people in the rural and urban areas. The need to develop social entrepreneurship in agriculture is dictated by a large number of social problems. Such problems include low living standards, unemployment, and social tension. The reasons that led to the emergence of the practice of social entrepreneurship are the above factors. The research problem lays upon disclosing the importance of role of social entrepreneurship in rural development of India. The paper the tendencies of social entrepreneurship in India, to present successful examples of such business for providing recommendations how to improve situation in rural areas in terms of social entrepreneurship development. Indian government has made some steps towards development of social enterprises, social entrepreneurship, and social in- novation, but a lot remains to be improved.
OPTIMAL RECONFIGURATION OF POWER DISTRIBUTION RADIAL NETWORK USING HYBRID MET...IAEME Publication
Distribution system is a critical link between the electric power distributor and the consumers. Most of the distribution networks commonly used by the electric utility is the radial distribution network. However in this type of network, it has technical issues such as enormous power losses which affect the quality of the supply. Nowadays, the introduction of Distributed Generation (DG) units in the system help improve and support the voltage profile of the network as well as the performance of the system components through power loss mitigation. In this study network reconfiguration was done using two meta-heuristic algorithms Particle Swarm Optimization and Gravitational Search Algorithm (PSO-GSA) to enhance power quality and voltage profile in the system when simultaneously applied with the DG units. Backward/Forward Sweep Method was used in the load flow analysis and simulated using the MATLAB program. Five cases were considered in the Reconfiguration based on the contribution of DG units. The proposed method was tested using IEEE 33 bus system. Based on the results, there was a voltage profile improvement in the system from 0.9038 p.u. to 0.9594 p.u.. The integration of DG in the network also reduced power losses from 210.98 kW to 69.3963 kW. Simulated results are drawn to show the performance of each case.
APPLICATION OF FRUGAL APPROACH FOR PRODUCTIVITY IMPROVEMENT - A CASE STUDY OF...IAEME Publication
Manufacturing industries have witnessed an outburst in productivity. For productivity improvement manufacturing industries are taking various initiatives by using lean tools and techniques. However, in different manufacturing industries, frugal approach is applied in product design and services as a tool for improvement. Frugal approach contributed to prove less is more and seems indirectly contributing to improve productivity. Hence, there is need to understand status of frugal approach application in manufacturing industries. All manufacturing industries are trying hard and putting continuous efforts for competitive existence. For productivity improvements, manufacturing industries are coming up with different effective and efficient solutions in manufacturing processes and operations. To overcome current challenges, manufacturing industries have started using frugal approach in product design and services. For this study, methodology adopted with both primary and secondary sources of data. For primary source interview and observation technique is used and for secondary source review has done based on available literatures in website, printed magazines, manual etc. An attempt has made for understanding application of frugal approach with the study of manufacturing industry project. Manufacturing industry selected for this project study is Mahindra and Mahindra Ltd. This paper will help researcher to find the connections between the two concepts productivity improvement and frugal approach. This paper will help to understand significance of frugal approach for productivity improvement in manufacturing industry. This will also help to understand current scenario of frugal approach in manufacturing industry. In manufacturing industries various process are involved to deliver the final product. In the process of converting input in to output through manufacturing process productivity plays very critical role. Hence this study will help to evolve status of frugal approach in productivity improvement programme. The notion of frugal can be viewed as an approach towards productivity improvement in manufacturing industries.
A MULTIPLE – CHANNEL QUEUING MODELS ON FUZZY ENVIRONMENTIAEME Publication
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Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
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Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
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(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
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Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
2. Dr.S.Sridhar, S.Sabareesanand Dr.R.Kannan
http://www.iaeme.com/IJMET/index.asp 73 editor@iaeme.com
has been proved to be NP hard. Due to the complexity of the problem, it is difficult to develop
exact methods to solve this problem. Hence, researchers proposed different heuristics and meta-
heuristics to solve the flow shop scheduling problems. The important heuristics were developed
by Rajendran and Chaudhri (1990) and also proposed to solve the flow shop scheduling problems.
A greedy heuristic algorithm was addressed by Baraz and Mosheiov to minimize the makespan
for flow shop scheduling problems. Similarly a Solar Panel holder with clamp (SPHC) was
investigated by team members in a clamp manufacturing company. The system analysed is
composed of seven machines used for shearing, punching, and spot welding, grinding, drilling
and painting in series. The number of machines in shearing, punching, and spot welding, grinding,
and drilling stages are equal respectively (say one machine each). All jobs are performed one by
one machine in a unidirectional flow.
1.1. Objective Function
A flow shop scheduling is characterized by unidirectional flow of work with a variety of jobs
being processed sequentially in a one-pass manner. A flow shop is which ‘n’ jobs to be processed
through ‘m’ machines environment. The processing times of all the jobs are well known in
advance and all the jobs have been processed in the same order in various machines. A particular
set of jobs can be sequenced through all the machines and each sequence will have an objective
function as makespan time with the specified batch size. It is difficult to suggest a sequence,
which will optimize the makespan time. In this paper, proposed the PSO algorithm which will
optimize the sequence so as to achieve minimum value of makespan time with the specified batch
size using n number of iterations. More the iterations bring more the quality of the results.
≥ = 1,2,3 … … . (1)
Notations
Cmax – Minimization of Makespan Time
Cim – Completion time of the job (i) on Machine (m)
n – Number of jobs
1.2. Illustration of NPS
A Gantt chart developed for generating Permutation Schedules (PS) can yield solutions of good
quality in a flow shop scheduling problems. But the solutions may not be satisfactory. Because,
the job has to follow a fixed operation sequence at each machine even though there is required
operation for the job at all machines. Therefore, a better schedule performance can usually be
obtained by allowing jobs to change the operation sequence at different machines like Non
Permutation Schedule (NPS).
2. LITERATURE SURVEY
During the last three decades many research works have been done in this area. The flow shop
problems are said to problem is NP-hard. The solutions for these problems are obtained by
heuristics or meta-heuristics approach. Many heuristic algorithms have been developed to
generate optimum schedule and part releasing policies. Most of these algorithms include
enumerative procedure, mathematical programming and approximation techniques viz. Linear
programming, integer programming, goal programming, dynamic programming, transportation
and network analysis, branch and bound, lagrangian relaxation, priority rule-based heuristics,
local search algorithms [Iterative search (ITS), Tabu search (TS), Threshold Accepting (TA),
Simulated Annealing (SA)], Evolutionary Genetic algorithm (GA) etc. Among these techniques
few are specific to particular objective like makespan time and tardiness etc., few are specific to
particular problem instances with respect to computational time needed.
3. Particle Swarm Optimization Approach for Flow Shop Scheduling Problem – a Case Study
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Johnson (1954) is believed to be the first who introduced flow shop scheduling. Since then, flow
shop scheduling has become one of the most interesting topics among researchers and
practitioners these are different forms of flow shop optimization such as minimization of the
makespan which is one of the most popular one. Turner and Booth (1987) compared two famous
heuristics with a set of 350 random problems. Ponnambalam et al. (2001) compared five different
heuristics against only 21 typical test problems. Ruiz and Maroto (2005) presented a review and
comparative evaluation of heuristics and meta-heuristics for the permutation flow shop problem
with the makespan criterion. They compared 25 methods, ranging from the classical Johnson’s
algorithm to the most recent meta-heuristics. Lian et al. (2006) applied an efficient similar particle
swarm optimization algorithm (SPSOA) to the PFSS problem with the objective of minimizing
the makespan. Tasgetiren et al. (2007) solved the permutation flow shop sequencing problem
(PFSP) with a particle swarm optimization algorithm (PSO). They considered the objectives of
minimizing makespan and the total flow time of jobs. Ruiz and Stutzle (2007) presented a new
iterated greedy algorithm that applies two phases iteratively, named destruction, where some jobs
are eliminated from the incumbent solution, and construction, where the eliminated jobs are
reinserted into the sequence using the well known NEH construction heuristic. Naderi and Ruiz
(2010) studied a new generalization of the regular permutation flow shop scheduling problem
(PFSP) referred to as the distributed permutation flow shop scheduling problem or DPFSP. Under
this generalization, they assumed that there are a total of F identical factories or shops, each one
with m machines disposed in series. A set of n available jobs have to be distributed among the F
factories and then a processing sequence has to be derived for the jobs assigned to each factory.
Their optimization criterion was the minimization of the maximum completion time or makespan
among the factories. Dong et al. (2009) presented an integrated local search algorithm to solve
the permutation flow shop sequencing problem with total flow time criterion. They showed the
effectiveness and superiority of their method over three constructive heuristics, three ant-colony
algorithms and a particle swarm optimization algorithm. Vallada and Ruiz (2009) worked on a
cooperative meta-heuristic method for the permutation flow shop scheduling problem
considering two objectives separately: total tardiness and makespan. They adopted the island
model where each island runs an instance of the method and communications begin when the
islands are reached to a certain level of evolution. Farahmand Rad et al. (2009) showed five new
methods that outperform the well-known NEH heuristic as supported by careful statistical
analyses using the well-known instances of Taillard. The proposed methods attempt to counter
the excessive greediness of NEH by carrying out re-insertions of already inserted jobs at some
points in the construction of the solution. Vallada and Ruiz (2010) presented three genetic
algorithms for the permutation flow shop scheduling problem with total tardiness minimization
criterion. The algorithms include advanced techniques like path re-linking, local search and a
procedure to control the diversity of the population. Sridhar et al (2010) & (2014) investigated
stainless steel industry and Optimised company production sequence in hybrid flow shop using
simulated annealing algorithm.
In this paper, we consider a permutation flow shop scheduling problem with the objective of
minimizing the makespan. The method is then solved using a particle swarm optimization (PSO)
algorithm and it is employed to solve the problem. PSO algorithm, developed by Kennedy and J.
Eberheart (1995)and it was first intended for simulating social behaviour as a stylized
representation of the movement of organisams in a bird flock or fish school.it is an computational
method that optimises the problem by iteratively trying to improve a candidate solution with
regard to given measure of quality. It solves a problem by having a population of candidate
solutions, here dubbed particles and moving these particles around in the search-space according
to simple mathematical formulae over the particle position and velocity.
3. CASE STUDY
4. Dr.S.Sridhar, S.Sabareesanand Dr.R.Kannan
http://www.iaeme.com/IJMET/index.asp 75 editor@iaeme.com
R.N Solar Energies is one of the groups of R.N. Steels and Engineering (P) Ltd., located at
Dindigul. Their main product is supporting beams and holding clamps used to carry the
photovoltaic cell structured panels. The product concerned in this work is Clamps and Supporting
beams. The problem considered in this work is optimum sequencing & scheduling for 500
numbers of clamps and beams. The main objective is to minimize the makespan time.
The present work investigates a production system in a Manufacturing company that
manufactures various clamps and beam components. The system is composed of five
workstations (stages), shearing, filing, punching, drilling, grinding, spot welding, and painting.
At the first workstation, the shearing machine is used to perform cutting operation and followed
by filing operation with help of hand grinder or manual rough file the sharp edges are blunt in
order to avoid handling damages. At the next workstation, the punching machine is used to
perform punching operation, producing punch in required marking to exact location of drilling
tool. The next workstation to be drilling to drill through holes in the work piece and followed by
grinding operation to get a smooth surface finish performed by a surface grinder. In the next
workstation welding operation is performed by spot welding machine. The last work station is to
paint the jobs by a spray painting machine. Each workstation consists of equal number of
machines. All jobs are performed one by one in all machines as said earlier the job flow is
unidirectional for flow shop problems. In this scenario, this work proposes research on
optimisation of sequencing and scheduling in flow shop environment with the makespan
objective.
3.1. Number of Machines
The details of various types of machines and jobs which has been produced in the R.N Solar Energies
Company for manufacture of clamps and supporting beams are given in Table 1 and Table 2
Table 1 Number of machines
3.2. Data for Clamp and Supporting Beam
The details regarding various components, operations, machines, quantity, machining time and
batch size for clamp and supporting beam production, are given in Table 2.
Table 2 Data for production component
5. Particle Swarm Optimization Approach for Flow Shop Scheduling Problem – a Case Study
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3.3. Time matrix
Machinesjobs J1 J2 J3 J4 J5
M1 16 19 13 19 31
M2 10 06 05 06 36
M3 13 10 08 10 11
M4 45 21 22 21 26
M5 42 36 21 36 53
M6 14 16 09 16 0
M7 18 19 13 19 42
4. PARTICLE SWARM OPTIMIZATION
PSO is an evolutionary computation method developed by Kennedy & Eberhart (1995). It
stimulates the social behaviour of bird flocking or fish schooling. Like other non-traditional
techniques, the PSO is also a population based optimization technique. It delineates a type of
biological social system, which depicts the collective behaviour of simple individuals interacting
with their environment and one another. It is inspired by the movement and intelligence of
swarms. A swarm is a structured collection of interacting organisms such as bees, ants or birds.
Each organism in a swarm is a particle or an agent. Particles and swarms in PSO represent the
individuals and populations as in other evolutionary algorithms.
6. Dr.S.Sridhar, S.Sabareesanand Dr.R.Kannan
http://www.iaeme.com/IJMET/index.asp 77 editor@iaeme.com
The initiation of the PSO algorithm is done with a population of random solutions, denoted
as random particles and then searches for optima by updating generations. New generations are
formed by updating velocity. The potential solutions are called particles. Each particle is updated
by following two “best” values in each iterations as pbest and gbest. The particles fly through the
multi-dimensional search space and follow the current optimum particles. Each particle has
particular velocity, with which the particles are carried to new positions and are evaluated for
fitness values according to their positions. PSO does not combine the survival of the fittest
whereas all other evolutionary algorithms do. Since each particle exchanges its information with
the particles in the neighbourhood, after some number of iterations, the swarm loses its diversity
and the algorithm converges to the optimal solution. All particles in the pool are kept during the
whole run. The PSO is carried out for optimal value of the required number of iterations. The
good solution is reached among the updated generations. PSO is used as an approach that can be
used across a wide range of applications, which include function optimization, artificial neural
network, fuzzy system control, as well as for specific applications focused on a special
requirement.
4.1. Basic Elements of PSO
The basic elements of PSO algorithm are particle, population, permutation, particle velocity,
personal best, global best (and) termination criterion.
4.1.1.. Particle
Xi denotes the ith
particle in the swarm at iteration t and is represented by n number of dimensions
given as Equation
[Xi]t
= [(xi1)t
, (xi2)t
,…, (xin)t
]
Where, (xij
)t
is the position value of the ith
particle with respect to the jth
dimension (j= 1,2,...,
n).
4.1.2. Population
popt
is the set of ρ particles in the swarm at iteration t denoted in Equation
popt
= [(X1)t
,(X2)t
,…..,(X ρ)t
]
4.1.3. Permutation
It introduces a new variable (πi)t
which is a permutation of jobs implied by the particle(X1)t
. It
can be described in Equation
[πi] = [(πi1)t
,( πi2)t
)…….( πin)t
)] (3.3)
Where, (πij)t
is the assignment of job j of the particle i in the permutation at iteration t.
4.1.4. Particle velocity
[Vi]t
is the velocity of particle i at iteration t. It can be defined as Equation
[Vi]t
= [(vi1)t,
(vi2)t
…….(vin)t
]
Where, (vij)t
is the velocity of particle i at iteration t with respect to the jth
dimension.
4.1.5. Personal best
The [Pi]t
represents the best position of the particle i with the best fitness until iteration t, so the
best position associated with the best fitness value of the particle i obtained so far is called the
personal best. For each particle in the swarm, (Pi)t
can be determined and updated at each iteration
t.
7. Particle Swarm Optimization Approach for Flow Shop Scheduling Problem – a Case Study
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In a minimization problem with objective function f(πi)t
where πi)t is the corresponding
permutation of particle (Xi)t
, the personal best (Pi)t
of the ith
particle is obtained in such a manner
that f(πi)t
≤ f(πi)t-1
where πi)t
is the corresponding permutation of personal best (Pi)t
and πi)t-1
is
the corresponding permutation of personal best (Pi)t-1. To simplify, we denote the fitness
function of the personal best as (fi)pb
= f(πi1)t
. For each particle, the personal best is described as
Equation
[Pi]t
= [(pi1)t
,(pi )t
…….(pin)t
]
Where,
(pij)t
is the position value of the ith
personal best with respect dimension (j = 1,2,..., n).
4.1.6. Global best
The [G]t
denotes the best position of the globally best particle achieved so far in the whole swarm.
For this reason, the global best can be obtained such that f(π)t
≤ f(πi)t
for i = 1,2,..., π where (π)t
is the corresponding permutation of lobal best G t and (πi)t
is the corresponding permutation of
personal best [Pi]t
. To simplify, we denote the fitness function of the global best as (f)gb
= f(π)t
.
The global best is then defined as Equation
[G]t
= [(g1)t
,(g2)t
…….(gn)t
]
Where,
(gj)t
is the position value of the global best with respect to the jth
dimension (j = 1,2,..., n).
4.1.7. Termination criterion
It is a condition that the search process will be terminated. It might be maximum number of
iteration or maximum CPU time to terminate the search.
4.2. Step by Step Procedure for PSO
The basic step by step procedure of the PSO algorithm given is as follows,
Step 1: Initialization
Initialize a population of n particles randomly.
Step 2: Calculate the fitness function
Calculate the fitness value for each particle, if the fitness value is better than the best fitness
value in history (pij
t-1
).Then set current value as the new pbest
.
Step 3: Choose the best fitness value
Choose particle with the best fitness value of all the particles as the gbest
. (gj)t-1
Step 4: Calculate the particle velocity and position
For each particle, calculate velocity and position by using the equation,
[Vij]t= [(vij)t-1 + c1r1{(pij)t-1- (xij)t-1} + c2r2 {(gj)t-1- (xij)t-1}]
[Xij]t= (xij)t-1 + (vij)t
Where,
(vij)t-1
= Velocity of particle i at t-1th
iteration
(Vij)t
= Velocity of particle i at tth
iteration
(xij)t-1
= Position of particle i at t-1th
iteration
(Xij)t
= Position of particle i at tth
iteration
c1 = Acceleration factor related to pbest
c2 = Acceleration factor related to gbest
8. Dr.S.Sridhar, S.Sabareesanand Dr.R.Kannan
http://www.iaeme.com/IJMET/index.asp 79 editor@iaeme.com
r1 = Random number between 0 and 1
r2 = Random number between 0 and 1
(gj)t-1
= global best position of swarm
(pij)t-1
= local best position of particle
Step 5: Update the particle velocity and position
Each particle velocity and position is updated according to the dimensions.
Step 6: Termination of PSO
Terminate if 750 iterations is reached. Otherwise, go to Step 2.
There is a communication between the each particle delivers its information with others. A
particle exchanges its information with the particles in the neighbourhood. Therefore, after some
number of Iterations the swarm loses its diversity and the algorithm converges to the optimal
solution. Since PSO consists of simple concepts and mathematical operations with little memory
requirements it is fast and appealing in use for many optimization problems. To verify the PSO
algorithm, comparisons with simulated annealing algorithm is made. Computational results show
that the PSO algorithm is very competitive. Computational results show that the local search can
be really guided by PSO.
5. RESULTS
5.1. Computational Analysis
The PSO is used to find optimum or near optimum sequence for the problem given in the Table.
The makespan time for the best sequence obtained using PSO procedure is compared with the
makespan time for the sequence that the company currently using. The comparison reveals that
the makespan time of PSO procedure gives better solution than the makespan time of company
sequence and corresponding comparison is shown in
Table 3 Comparison of company result and PSO result
S.No PARTICULARS COMPANY RESULT PSO RESULT
1 Makespan time(sec) 19223 18878
2 Sequence 12345 42513
5.2. Results Comparison
• The proposed PSO is applied to find optimum or near optimum sequence for the
problem given in the Table 3.
• The makespan time for the best sequence obtained using PSO procedure is compared
with the makespan time for the sequence that the company currently using.
• Table represents the comparison of makespan time using PSO with that of company
existing production sequence.
• The result shows that PSO is capable of providing better solution than the existing
production sequence of the company.
• Influence of job size on execution time is significant.
5.3. Limitations of Research Work and Future Scope
• The scheduling problems considered in this work are of n jobs m machines. In this
research work is single objective have been considered. The objective like multi
objective function may be considered.
9. Particle Swarm Optimization Approach for Flow Shop Scheduling Problem – a Case Study
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• Tool set up time and job setup time are considered as zero. They may be considered
in the future work.
• Machine break down time is not considered. In future, the problems may be
considered with the machine breakdown.
• The solution may be obtained using other heuristic approaches and comparison may
be made with the current solution.
6. CONCLUSION
This work addresses a flow shop scheduling environment that manufactures the product clamp
and supporting beam.The aim is to determine optimal or near optimal schedule for ‘n’ jobs, which
processed at‘m’ machines. A PSO algorithm is proposed for get the optimum or near optimum
schedule and sequence. The makespan time for the best sequence obtained using PSO procedure
is compared with the makespan time for the sequence that the company currently using. The
comparison reveals that the PSO is capable of providing better solution than existing production
sequence. So it is concluded that the PSO proposed for the problem under consideration can very
well be applied to find better schedule.
REFERENCES
[1] Kennedy.J and Eberheart.R,(1995) “Particle Swarm Optimization”, Proceedings Of IEEE
Intenational Conference On Neural Networks pp.1942-1948
[2] Shi and Eberheart.R,(1995) “a modified Particle Swarm Optimizer”, Proceedings Of IEEE
Intenational Conference On Evolutionary computation. pp . 66-73
[3] Kennedy.J and Eberheart.R,(1995) “Particle Swarm Optimization”, Proceedings Of IEEE
Intenational Conference On Neural Networks pp.1942-1948
[4] S. M. Johnson, “Optimal two- and threestage production schedules with setup times
included”, Naval Research Logistics Quarterly, Vol. 1, No. 1, pp. 61 – 68, 1954.
[5] C. Rajendran and D. Chaudhri, “Heuristic algorithm for continuous flow-shop problem”,
Naval Research Logistics, Vol. 37, No. 5, pp. 695 – 705, 1990.
[6] A. Allahverdi and F. S. Al-Anzi, “Scheduling multi-stage parallel-processor services to
minimize average response time”, Journal of the Operational Research Society, Vol. 57, No.
1, pp. 101–110, 2006.
[7] L. Wang, Y. Xu, G. Zhou, S. Wang and M. Liu, “A novel decoding method for the hybrid
flow-shop scheduling problem with multiprocessor tasks”, International Journal of Advanced
Manufacturing Technology, Vol.59, No. 9–12, pp. 1113–1125, 2011.
[8] V. Gondek, Hybrid flow shop scheduling: Heuristic solutions and lp-based lower bounds,
Operations Research Proceedings, Vol.14, pp. 485–490, 2011.
[9] S. H. Abyaneh and M. Zandieh, Bi-objective hybrid flow shop scheduling with sequence-
dependent setup times and limited buffers, International Journal of Advanced Manufacturing
Technology, Vol.58 No. 1–4, pp. 309–325, 2011.
[10] E. Figielska, Heuristic algorithms for preemptive scheduling in a two-stage hybrid flowshop
with additional renewable resources at each stage, Computers & Industrial Engineering,
Vol.59, No. 4, pp. 509–519, 2010.