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
AN IMPROVE OBJECT-ORIENTED APPROACH FOR MULTI-OBJECTIVE FLEXIBLE JOB-SHOP SCH...ijcsit
Flexible manufacturing systems are not easy to control and it is difficult to generate controlling systems for this problem domain. Flexible job-shop scheduling problem (FJSP) is one of the instances in this domain. It is a problem which acquires the job-shop scheduling problems (JSP). FJSP has additional routing subproblem in addition to JSP. In routing sub-problem each task is assigned to a machine out of a set of capable machines. In scheduling sub-problem, the sequence of assigned operations is obtained while optimizing the objective function(s). In this work an object-oriented (OO) approach with simulated annealing algorithm is used to simulate multi-objective FJSP. Solution approaches provided in the literature generally use two-string encoding scheme to represent this problem. However, OO analysis, design and programming methodology helps to present this problem on a single encoding scheme effectively which result in a practical integration of the problem solution to manufacturing control systems where OO paradigm is frequently used. Three parameters are considered in this paper: maximum completion time, workload of the most loaded machine and total workload of all machines which are the benchmark used to show the propose system achieve effective result.
This document discusses various solution methods for solving the unit commitment problem in power systems. It describes dynamic programming, Lagrangian relaxation, mixed integer programming, Benders decomposition, and heuristic methods. The key strengths and weaknesses of each method are outlined. The document concludes that a comprehensive algorithm combining the strengths of different methods while overcoming their individual weaknesses would be a suitable approach for solving real-world unit commitment problems.
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.
Acm Tech Talk - Decomposition Paradigms for Large Scale SystemsVinayak Hegde
This document discusses decomposition approaches for solving large-scale systems problems. It begins with an overview of general decomposition strategies and approaches to decomposition. It then discusses specific decomposition paradigms like model coordination and goal coordination. It provides examples of how decomposition can be applied to problems in areas like optimization, identification, and control. The document concludes with some illustrative examples and case studies.
A COMBINATION OF PALMER ALGORITHM AND GUPTA ALGORITHM FOR SCHEDULING PROBLEM ...ijfls
The apparel industry is a class of textile industry. Generally, the production scheduling problem in the apparel industry belongs to Flow Shop Scheduling Problems (FSSP). There are many algorithms/techniques/heuristics for solving FSSP. Two of them are the Palmer Algorithm and the Gupta Algorithm. Hyper-heuristic is a class of heuristics that enables to combine of some heuristics to produce a new heuristic. GPHH is a hyper-heuristic that is based on genetic programming that is proposed to solve FSSP [1]. This paper presents the development of a computer program that implements the GPHH. Some experiments have been conducted for measuring the performance of GPHH. From the experimental results, GPHH has shown a better performance than the Palmer Algorithm and Gupta Algorithm.
A hybrid fuzzy ann approach for software effort estimationijfcstjournal
This document presents a study that develops a software effort estimation model using an Adaptive Neuro Fuzzy Inference System (ANFIS). The study evaluates the proposed ANFIS model using COCOMO81 datasets and compares its performance to an Artificial Neural Network (ANN) model and the intermediate COCOMO model. The results show that the ANFIS model provides better estimates than the ANN and COCOMO models, with lower values for metrics like the Root Mean Square Error and Magnitude of Relative Error.
This document describes using Quality Function Deployment (QFD) and the Theory of Inventive Problem Solving (TRIZ) to optimize the mathematical modeling of components for virtual durability simulation. It presents a case study where QFD's House of Quality matrix was used to determine the best way to attach components in the model based on customer needs. TRIZ was then used to resolve conflicts between modeling approaches. The best approach found was to use square elements for meshing and attach components via RBE3 elements. QFD and TRIZ provided systematic methods to evaluate alternatives and resolve issues in developing the computational simulation model.
Operations research (OR) is an interdisciplinary approach for decision-making that uses mathematical modeling and analytical methods to arrive at optimal or near-optimal solutions to complex decision problems. OR was first applied during World War II to solve logistics and operations problems. It involves breaking problems down into components, representing them mathematically, and using analytical methods like linear programming to solve problems. The goal of OR is to determine the best solution to a problem by quantifying variables and using mathematical techniques and computer modeling.
AN IMPROVE OBJECT-ORIENTED APPROACH FOR MULTI-OBJECTIVE FLEXIBLE JOB-SHOP SCH...ijcsit
Flexible manufacturing systems are not easy to control and it is difficult to generate controlling systems for this problem domain. Flexible job-shop scheduling problem (FJSP) is one of the instances in this domain. It is a problem which acquires the job-shop scheduling problems (JSP). FJSP has additional routing subproblem in addition to JSP. In routing sub-problem each task is assigned to a machine out of a set of capable machines. In scheduling sub-problem, the sequence of assigned operations is obtained while optimizing the objective function(s). In this work an object-oriented (OO) approach with simulated annealing algorithm is used to simulate multi-objective FJSP. Solution approaches provided in the literature generally use two-string encoding scheme to represent this problem. However, OO analysis, design and programming methodology helps to present this problem on a single encoding scheme effectively which result in a practical integration of the problem solution to manufacturing control systems where OO paradigm is frequently used. Three parameters are considered in this paper: maximum completion time, workload of the most loaded machine and total workload of all machines which are the benchmark used to show the propose system achieve effective result.
This document discusses various solution methods for solving the unit commitment problem in power systems. It describes dynamic programming, Lagrangian relaxation, mixed integer programming, Benders decomposition, and heuristic methods. The key strengths and weaknesses of each method are outlined. The document concludes that a comprehensive algorithm combining the strengths of different methods while overcoming their individual weaknesses would be a suitable approach for solving real-world unit commitment problems.
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.
Acm Tech Talk - Decomposition Paradigms for Large Scale SystemsVinayak Hegde
This document discusses decomposition approaches for solving large-scale systems problems. It begins with an overview of general decomposition strategies and approaches to decomposition. It then discusses specific decomposition paradigms like model coordination and goal coordination. It provides examples of how decomposition can be applied to problems in areas like optimization, identification, and control. The document concludes with some illustrative examples and case studies.
A COMBINATION OF PALMER ALGORITHM AND GUPTA ALGORITHM FOR SCHEDULING PROBLEM ...ijfls
The apparel industry is a class of textile industry. Generally, the production scheduling problem in the apparel industry belongs to Flow Shop Scheduling Problems (FSSP). There are many algorithms/techniques/heuristics for solving FSSP. Two of them are the Palmer Algorithm and the Gupta Algorithm. Hyper-heuristic is a class of heuristics that enables to combine of some heuristics to produce a new heuristic. GPHH is a hyper-heuristic that is based on genetic programming that is proposed to solve FSSP [1]. This paper presents the development of a computer program that implements the GPHH. Some experiments have been conducted for measuring the performance of GPHH. From the experimental results, GPHH has shown a better performance than the Palmer Algorithm and Gupta Algorithm.
A hybrid fuzzy ann approach for software effort estimationijfcstjournal
This document presents a study that develops a software effort estimation model using an Adaptive Neuro Fuzzy Inference System (ANFIS). The study evaluates the proposed ANFIS model using COCOMO81 datasets and compares its performance to an Artificial Neural Network (ANN) model and the intermediate COCOMO model. The results show that the ANFIS model provides better estimates than the ANN and COCOMO models, with lower values for metrics like the Root Mean Square Error and Magnitude of Relative Error.
This document describes using Quality Function Deployment (QFD) and the Theory of Inventive Problem Solving (TRIZ) to optimize the mathematical modeling of components for virtual durability simulation. It presents a case study where QFD's House of Quality matrix was used to determine the best way to attach components in the model based on customer needs. TRIZ was then used to resolve conflicts between modeling approaches. The best approach found was to use square elements for meshing and attach components via RBE3 elements. QFD and TRIZ provided systematic methods to evaluate alternatives and resolve issues in developing the computational simulation model.
Operations research (OR) is an interdisciplinary approach for decision-making that uses mathematical modeling and analytical methods to arrive at optimal or near-optimal solutions to complex decision problems. OR was first applied during World War II to solve logistics and operations problems. It involves breaking problems down into components, representing them mathematically, and using analytical methods like linear programming to solve problems. The goal of OR is to determine the best solution to a problem by quantifying variables and using mathematical techniques and computer modeling.
Optimization of Patrol Manpower Allocation Using Goal Programming Approach -A...IJERA Editor
One of the most difficult tasks of the patrol administrators is allocation of manpower; i.e. determining the. most
effective level of operational manpower for patrol tasks. Typically, administrators resolve the allocation
problem by relying on prior statistical data and by employing subjective analysis. In general, only limited
systematic analyses have been applied to the problem. This thesis presents a non linear goal programming model
for allocating patrolmen to road segments within a patrol region. The model is demonstrated via a case example
of the East section of Visakhapatnam. The results of the model are valuable to the patrol administrator for
considering departmental goals and priority structure, in addition to available historical data, in the assignment
of patrol manpower for a given urban area.
ESTIMATING HANDLING TIME OF SOFTWARE DEFECTScsandit
The problem of accurately predicting handling time for software defects is of great practical
importance. However, it is difficult to suggest a practical generic algorithm for such estimates,
due in part to the limited information available when opening a defect and the lack of a uniform
standard for defect structure. We suggest an algorithm to address these challenges that is
implementable over different defect management tools. Our algorithm uses machine learning
regression techniques to predict the handling time of defects based on past behaviour of similar
defects. The algorithm relies only on a minimal set of assumptions about the structure of the
input data. We show how an implementation of this algorithm predicts defect handling time with
promising accuracy results
Modelling of Training Simulator for Steam Cracker OperatorsRisman Hatibi
This document describes the development of an operator training simulator for a steam cracker plant. A dynamic simulation model was created using process simulation software to represent the virtual plant. The model was developed based on plant design specifications and tested to validate its behavior matched the real plant. An operator training simulator was then created using the dynamic model. It was found that the simulator improved operator performance for handling abnormal situations compared to traditional training methods and could help reduce operational errors that result in losses. The simulator provided a safe, realistic environment for training new and experienced operators.
EFFICIENT SCHEDULING STRATEGY USING COMMUNICATION AWARE SCHEDULING FOR PARALL...ijdpsjournal
In the area of Computer Science, Parallel job scheduling is an important field of research. Finding a best
suitable processor on the high performance or cluster computing for user submitted jobs plays an
important role in measuring system performance. A new scheduling technique called communication aware
scheduling is devised and is capable of handling serial jobs, parallel jobs, mixed jobs and dynamic jobs.
This work focuses the comparison of communication aware scheduling with the available parallel job
scheduling techniques and the experimental results show that communication aware scheduling performs
better when compared to the available parallel job scheduling techniques.
Operation Sequencing and Machining Parameter Selection in CAPP for Cylindrica...IRJET Journal
The document describes a hybrid genetic algorithm-expert system approach for operation sequencing and machining parameter selection in computer-aided process planning (CAPP) for cylindrical parts. It involves using a genetic algorithm to generate feasible sequences of machining operations based on a precedence cost matrix. An expert system is then used to select optimal machining parameters for turning, boring, and facing operations based on the part material. The hybrid approach aims to reduce computational time and generate alternative optimal sequences for improving production flexibility in changing manufacturing environments. A case study application involving 8 operations on a carbon steel part is presented to validate the proposed approach.
The document compares a memetic algorithm and particle swarm optimization (PSO) for optimizing multi-job shop scheduling problems. It proposes a memetic algorithm that combines a customized genetic algorithm with local search. The genetic algorithm uses customized selection, crossover and mutation operators. Experimental results on benchmark problems show the memetic algorithm performs better than a simple genetic algorithm and PSO in finding optimal solutions.
This presentation is trying to explain the Linear Programming in operations research. There is a software called "Gipels" available on the internet which easily solves the LPP Problems along with the transportation problems. This presentation is co-developed with Sankeerth P & Aakansha Bajpai.
By:-
Aniruddh Tiwari
Linkedin :- http://in.linkedin.com/in/aniruddhtiwari
Evaluation of Process Capability Using Fuzzy Inference SystemIOSR Journals
In many industrial instances product quality depends on a multitude of dependent characteristics and as a consequence, attention on capability indices shifts from univariate domain to multivariate domain. In this research fuzzy inference system is used to determine the process capability index. Fuzzy sets can represent imprecise quantities as well as linguistic terms. Fuzzy inference system (FIS) is a method, based on the fuzzy theory, which maps the input values to the output values. The mapping mechanism is based on some set of rules, a list of if-then statements. In this research Mamdani fuzzy inference system is used to derive the overall output process capability when subjected to six crisp input and one output. This paper deals with a novel approach to evaluating process capability based on readily available information using fuzzy inference system.
1. The document presents a mathematical model for scheduling jobs in a three-stage flow shop to minimize total elapsed time and rental costs under constraints.
2. It considers processing times, setup times, transportation times between machines, and machine breakdown intervals.
3. The key concepts are: starting machines at optimal times to keep completion times and total elapsed time unchanged while minimizing machine rental costs.
IRJET- Optimization of Plastic Injection MoldingIRJET Journal
The document discusses optimization of plastic injection molding process parameters using Taguchi methods and Moldflow simulation software. It analyzes the effect of melt temperature, cooling time, injection pressure, and mold temperature on the warpage of a honey cup part made of polypropylene. An L9 orthogonal array experiment is designed using the Taguchi approach to study these four parameters at three levels each. The results show that melt temperature has the most significant effect on warpage, followed by mold temperature, injection pressure, and cooling time. The optimal parameter settings found to minimize warpage are a melt temperature of 180°C, cooling time of 15 seconds, injection pressure of 90MPa, and mold temperature of 50°C.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
Linear Programming Module- A Conceptual FrameworkSasquatch S
This document provides an overview of linear programming and how to formulate and solve linear programming problems. Key points:
- Linear programming involves optimizing an objective function subject to constraints, where all relationships are linear. It can be used to solve problems like resource allocation.
- To formulate a problem, you identify decision variables, write the objective function and constraints in terms of the variables, and specify non-negativity.
- Graphical methods can solve small 2-variable problems by finding the optimal point in the feasible region bounded by the constraint lines. Larger problems use computer solutions like the simplex method.
- To solve in Excel, you set up the model with decision variables, objective function
The Cloud computing becomes an important topic
in the area of high performance distributed computing. On the
other hand, task scheduling is considered one the most significant
issues in the Cloud computing where the user has to pay for the
using resource based on the time. Therefore, distributing the
cloud resource among the users' applications should maximize
resource utilization and minimize task execution Time. The goal
of task scheduling is to assign tasks to appropriate resources that
optimize one or more performance parameters (i.e., completion
time, cost, resource utilization, etc.). In addition, the scheduling
belongs to a category of a problem known as an NP-complete
problem. Therefore, the heuristic algorithm could be applied to
solve this problem. In this paper, an enhanced dependent task
scheduling algorithm based on Genetic Algorithm (DTGA) has
been introduced for mapping and executing an application’s
tasks. The aim of this proposed algorithm is to minimize the
completion time. The performance of this proposed algorithm has
been evaluated using WorkflowSim toolkit and Standard Task
Graph Set (STG) benchmark.
This document summarizes a research paper that formulates a two-stage stochastic integer program to optimally schedule jobs that require multiple classes of constrained resources under uncertain conditions. The formulation models uncertainty in processing times, due dates, resource consumption and availability. It allows temporary resource capacity expansion for a penalty cost. The authors develop an exact solution method using Benders decomposition for problems with a moderate number of scenarios, and also propose a sampling-based method for large-scale problems.
The document discusses operations research and linear programming. It defines operations research as a scientific approach to determine the optimal solution to decision problems with limited resources. Linear programming is then introduced as a type of mathematical modeling where the objective function and constraints are linear. The key aspects of a linear programming problem are defined as the decision variables, objective function to maximize or minimize, and constraints. Graphical solutions and examples of linear programming problems are also provided.
IRJET - Model Driven Methodology for JAVAIRJET Journal
This document proposes a model-driven methodology to automatically convert standard Java applications into real-time Java applications using the Real-Time Specification for Java (RTSJ). It describes applying the principles of model-driven engineering to develop real-time systems in Java. The methodology defines a series of model transformation steps to convert existing time-sharing Java code into code that complies with RTSJ and supports real-time features like scheduling, memory management, and asynchronous event handling. The methodology aims to reduce the cost and errors of developing real-time applications by automating the conversion process using modeling standards.
A robust multi criteria optimization approachPhuong Dx
This document presents a novel robust multi-criteria optimization (RMCO) approach that integrates multi-objective optimization concepts with statistical robust design techniques. The RMCO approach obtains Pareto-optimal robust design solution sets using design of experiment setups and ANOVA results to quantify dominance and significance of design factors. The approach addresses limitations of previous methods in handling constraints and multiple objectives. It is presented as a more effective method for product design optimization under variability.
ROOT CAUSE ANALYSIS OF CONTROL ROD FRICTION PROBLEM IN DFIP USING SHAININ® A...IAEME Publication
The two major challenges that industries are facing today are continuous improvement in productivity and quality of the product. Bosch Production System (BPS) - one of the leading manufacturers in Diesel system equipments, has been successfully employing the efficient statistical tool ‘Shainin®
’ for the root cause identification and Design of Experiments (DOE) techniques for analysis and optimization of the quality related issues. Shainin® is popular being a simplest and effective tool to employ in solving the manufacturing related problems. The present paper deals with one of the quality issues resolved by using Shainin® methodology in Diesel systems plant, Bosch Ltd., Bengaluru.
This document discusses various optimization techniques used in pharmaceutical development. It begins with defining optimization and providing an outline of topics to be covered, including key terms, parameters, experimental designs, applied methods, and references. Experimental designs discussed include factorial, response surface, central composite, Box-Behnken, Plackett-Burman, and Taguchi designs. Applied optimization methods include classic optimization techniques using calculus as well as statistical methods like EVOP. The objective of pharmaceutical optimization is to develop the optimal formulation while reducing costs through fewer experiments.
This document summarizes a study assessing groundwater quality in rural areas near Vijayawada, India. The study analyzed physicochemical parameters like pH, turbidity, conductivity, alkalinity, hardness, nitrates and more from groundwater samples from four villages. Most parameters were within permissible limits, except for higher hardness levels. The highest conductivity, pH, chlorides, alkalinity were found in samples from Nidamanuru village, possibly due to agricultural and industrial pollution from nearby areas entering the groundwater. The study aims to evaluate groundwater quality for drinking and other uses in the region.
This document proposes integrating iris recognition with RFID cards to develop a high-security access environment. It discusses:
1) How iris recognition works, including iris segmentation, normalization, feature extraction using wavelets, and identification by comparing templates.
2) Details of the RFID card used, including its microcontroller and memory, and the design of an RFID card programmer.
3) The proposed method of integrating iris recognition by storing the extracted iris features and a signature in the RFID card, and comparing them during authentication.
4) Preliminary test results comparing combinations of wavelet coefficients to find the best approach. Performance metrics like reading time, writing time, and memory utilization are evaluated.
This document summarizes a study on the generalized thermoelastic problem of a semi-infinite thin rod subjected to a step in strain. The study obtained solutions for temperature distribution, strain, and stress for small time values using the Laplace transform method. The study formulated the problem using the equations of motion, energy, and constitutive relations for an isotropic linear elastic solid. Non-dimensional variables were introduced to simplify the governing equations.
Optimization of Patrol Manpower Allocation Using Goal Programming Approach -A...IJERA Editor
One of the most difficult tasks of the patrol administrators is allocation of manpower; i.e. determining the. most
effective level of operational manpower for patrol tasks. Typically, administrators resolve the allocation
problem by relying on prior statistical data and by employing subjective analysis. In general, only limited
systematic analyses have been applied to the problem. This thesis presents a non linear goal programming model
for allocating patrolmen to road segments within a patrol region. The model is demonstrated via a case example
of the East section of Visakhapatnam. The results of the model are valuable to the patrol administrator for
considering departmental goals and priority structure, in addition to available historical data, in the assignment
of patrol manpower for a given urban area.
ESTIMATING HANDLING TIME OF SOFTWARE DEFECTScsandit
The problem of accurately predicting handling time for software defects is of great practical
importance. However, it is difficult to suggest a practical generic algorithm for such estimates,
due in part to the limited information available when opening a defect and the lack of a uniform
standard for defect structure. We suggest an algorithm to address these challenges that is
implementable over different defect management tools. Our algorithm uses machine learning
regression techniques to predict the handling time of defects based on past behaviour of similar
defects. The algorithm relies only on a minimal set of assumptions about the structure of the
input data. We show how an implementation of this algorithm predicts defect handling time with
promising accuracy results
Modelling of Training Simulator for Steam Cracker OperatorsRisman Hatibi
This document describes the development of an operator training simulator for a steam cracker plant. A dynamic simulation model was created using process simulation software to represent the virtual plant. The model was developed based on plant design specifications and tested to validate its behavior matched the real plant. An operator training simulator was then created using the dynamic model. It was found that the simulator improved operator performance for handling abnormal situations compared to traditional training methods and could help reduce operational errors that result in losses. The simulator provided a safe, realistic environment for training new and experienced operators.
EFFICIENT SCHEDULING STRATEGY USING COMMUNICATION AWARE SCHEDULING FOR PARALL...ijdpsjournal
In the area of Computer Science, Parallel job scheduling is an important field of research. Finding a best
suitable processor on the high performance or cluster computing for user submitted jobs plays an
important role in measuring system performance. A new scheduling technique called communication aware
scheduling is devised and is capable of handling serial jobs, parallel jobs, mixed jobs and dynamic jobs.
This work focuses the comparison of communication aware scheduling with the available parallel job
scheduling techniques and the experimental results show that communication aware scheduling performs
better when compared to the available parallel job scheduling techniques.
Operation Sequencing and Machining Parameter Selection in CAPP for Cylindrica...IRJET Journal
The document describes a hybrid genetic algorithm-expert system approach for operation sequencing and machining parameter selection in computer-aided process planning (CAPP) for cylindrical parts. It involves using a genetic algorithm to generate feasible sequences of machining operations based on a precedence cost matrix. An expert system is then used to select optimal machining parameters for turning, boring, and facing operations based on the part material. The hybrid approach aims to reduce computational time and generate alternative optimal sequences for improving production flexibility in changing manufacturing environments. A case study application involving 8 operations on a carbon steel part is presented to validate the proposed approach.
The document compares a memetic algorithm and particle swarm optimization (PSO) for optimizing multi-job shop scheduling problems. It proposes a memetic algorithm that combines a customized genetic algorithm with local search. The genetic algorithm uses customized selection, crossover and mutation operators. Experimental results on benchmark problems show the memetic algorithm performs better than a simple genetic algorithm and PSO in finding optimal solutions.
This presentation is trying to explain the Linear Programming in operations research. There is a software called "Gipels" available on the internet which easily solves the LPP Problems along with the transportation problems. This presentation is co-developed with Sankeerth P & Aakansha Bajpai.
By:-
Aniruddh Tiwari
Linkedin :- http://in.linkedin.com/in/aniruddhtiwari
Evaluation of Process Capability Using Fuzzy Inference SystemIOSR Journals
In many industrial instances product quality depends on a multitude of dependent characteristics and as a consequence, attention on capability indices shifts from univariate domain to multivariate domain. In this research fuzzy inference system is used to determine the process capability index. Fuzzy sets can represent imprecise quantities as well as linguistic terms. Fuzzy inference system (FIS) is a method, based on the fuzzy theory, which maps the input values to the output values. The mapping mechanism is based on some set of rules, a list of if-then statements. In this research Mamdani fuzzy inference system is used to derive the overall output process capability when subjected to six crisp input and one output. This paper deals with a novel approach to evaluating process capability based on readily available information using fuzzy inference system.
1. The document presents a mathematical model for scheduling jobs in a three-stage flow shop to minimize total elapsed time and rental costs under constraints.
2. It considers processing times, setup times, transportation times between machines, and machine breakdown intervals.
3. The key concepts are: starting machines at optimal times to keep completion times and total elapsed time unchanged while minimizing machine rental costs.
IRJET- Optimization of Plastic Injection MoldingIRJET Journal
The document discusses optimization of plastic injection molding process parameters using Taguchi methods and Moldflow simulation software. It analyzes the effect of melt temperature, cooling time, injection pressure, and mold temperature on the warpage of a honey cup part made of polypropylene. An L9 orthogonal array experiment is designed using the Taguchi approach to study these four parameters at three levels each. The results show that melt temperature has the most significant effect on warpage, followed by mold temperature, injection pressure, and cooling time. The optimal parameter settings found to minimize warpage are a melt temperature of 180°C, cooling time of 15 seconds, injection pressure of 90MPa, and mold temperature of 50°C.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
Linear Programming Module- A Conceptual FrameworkSasquatch S
This document provides an overview of linear programming and how to formulate and solve linear programming problems. Key points:
- Linear programming involves optimizing an objective function subject to constraints, where all relationships are linear. It can be used to solve problems like resource allocation.
- To formulate a problem, you identify decision variables, write the objective function and constraints in terms of the variables, and specify non-negativity.
- Graphical methods can solve small 2-variable problems by finding the optimal point in the feasible region bounded by the constraint lines. Larger problems use computer solutions like the simplex method.
- To solve in Excel, you set up the model with decision variables, objective function
The Cloud computing becomes an important topic
in the area of high performance distributed computing. On the
other hand, task scheduling is considered one the most significant
issues in the Cloud computing where the user has to pay for the
using resource based on the time. Therefore, distributing the
cloud resource among the users' applications should maximize
resource utilization and minimize task execution Time. The goal
of task scheduling is to assign tasks to appropriate resources that
optimize one or more performance parameters (i.e., completion
time, cost, resource utilization, etc.). In addition, the scheduling
belongs to a category of a problem known as an NP-complete
problem. Therefore, the heuristic algorithm could be applied to
solve this problem. In this paper, an enhanced dependent task
scheduling algorithm based on Genetic Algorithm (DTGA) has
been introduced for mapping and executing an application’s
tasks. The aim of this proposed algorithm is to minimize the
completion time. The performance of this proposed algorithm has
been evaluated using WorkflowSim toolkit and Standard Task
Graph Set (STG) benchmark.
This document summarizes a research paper that formulates a two-stage stochastic integer program to optimally schedule jobs that require multiple classes of constrained resources under uncertain conditions. The formulation models uncertainty in processing times, due dates, resource consumption and availability. It allows temporary resource capacity expansion for a penalty cost. The authors develop an exact solution method using Benders decomposition for problems with a moderate number of scenarios, and also propose a sampling-based method for large-scale problems.
The document discusses operations research and linear programming. It defines operations research as a scientific approach to determine the optimal solution to decision problems with limited resources. Linear programming is then introduced as a type of mathematical modeling where the objective function and constraints are linear. The key aspects of a linear programming problem are defined as the decision variables, objective function to maximize or minimize, and constraints. Graphical solutions and examples of linear programming problems are also provided.
IRJET - Model Driven Methodology for JAVAIRJET Journal
This document proposes a model-driven methodology to automatically convert standard Java applications into real-time Java applications using the Real-Time Specification for Java (RTSJ). It describes applying the principles of model-driven engineering to develop real-time systems in Java. The methodology defines a series of model transformation steps to convert existing time-sharing Java code into code that complies with RTSJ and supports real-time features like scheduling, memory management, and asynchronous event handling. The methodology aims to reduce the cost and errors of developing real-time applications by automating the conversion process using modeling standards.
A robust multi criteria optimization approachPhuong Dx
This document presents a novel robust multi-criteria optimization (RMCO) approach that integrates multi-objective optimization concepts with statistical robust design techniques. The RMCO approach obtains Pareto-optimal robust design solution sets using design of experiment setups and ANOVA results to quantify dominance and significance of design factors. The approach addresses limitations of previous methods in handling constraints and multiple objectives. It is presented as a more effective method for product design optimization under variability.
ROOT CAUSE ANALYSIS OF CONTROL ROD FRICTION PROBLEM IN DFIP USING SHAININ® A...IAEME Publication
The two major challenges that industries are facing today are continuous improvement in productivity and quality of the product. Bosch Production System (BPS) - one of the leading manufacturers in Diesel system equipments, has been successfully employing the efficient statistical tool ‘Shainin®
’ for the root cause identification and Design of Experiments (DOE) techniques for analysis and optimization of the quality related issues. Shainin® is popular being a simplest and effective tool to employ in solving the manufacturing related problems. The present paper deals with one of the quality issues resolved by using Shainin® methodology in Diesel systems plant, Bosch Ltd., Bengaluru.
This document discusses various optimization techniques used in pharmaceutical development. It begins with defining optimization and providing an outline of topics to be covered, including key terms, parameters, experimental designs, applied methods, and references. Experimental designs discussed include factorial, response surface, central composite, Box-Behnken, Plackett-Burman, and Taguchi designs. Applied optimization methods include classic optimization techniques using calculus as well as statistical methods like EVOP. The objective of pharmaceutical optimization is to develop the optimal formulation while reducing costs through fewer experiments.
This document summarizes a study assessing groundwater quality in rural areas near Vijayawada, India. The study analyzed physicochemical parameters like pH, turbidity, conductivity, alkalinity, hardness, nitrates and more from groundwater samples from four villages. Most parameters were within permissible limits, except for higher hardness levels. The highest conductivity, pH, chlorides, alkalinity were found in samples from Nidamanuru village, possibly due to agricultural and industrial pollution from nearby areas entering the groundwater. The study aims to evaluate groundwater quality for drinking and other uses in the region.
This document proposes integrating iris recognition with RFID cards to develop a high-security access environment. It discusses:
1) How iris recognition works, including iris segmentation, normalization, feature extraction using wavelets, and identification by comparing templates.
2) Details of the RFID card used, including its microcontroller and memory, and the design of an RFID card programmer.
3) The proposed method of integrating iris recognition by storing the extracted iris features and a signature in the RFID card, and comparing them during authentication.
4) Preliminary test results comparing combinations of wavelet coefficients to find the best approach. Performance metrics like reading time, writing time, and memory utilization are evaluated.
This document summarizes a study on the generalized thermoelastic problem of a semi-infinite thin rod subjected to a step in strain. The study obtained solutions for temperature distribution, strain, and stress for small time values using the Laplace transform method. The study formulated the problem using the equations of motion, energy, and constitutive relations for an isotropic linear elastic solid. Non-dimensional variables were introduced to simplify the governing equations.
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
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
This document discusses dispersion compensation techniques for optical code division multiple access (OCDMA) systems using dispersion compensation fiber (DCF) and fiber gratings. It compares the performance of an 8-user OCDMA system using DCF versus fiber gratings. The results show that DCF more effectively compensates for dispersion, as evidenced by a lower bit error rate and higher Q factor, making it a better dispersion compensation method for OCDMA systems compared to fiber gratings.
This document summarizes a research paper on a GSM-based power meter reading and control system. The system uses GSM technology to remotely read electricity meter readings and send the readings to users and the electricity department via SMS. It also allows remote control of power to appliances to reduce unnecessary power consumption. The system takes meter readings daily and sends them to the electricity billing system to generate accurate monthly bills without human errors. The hardware and software designs are presented along with block diagrams of the meter-side and server-side systems. The research aims to automate energy billing and enable remote power monitoring and control.
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
This document analyzes stresses in a rotating circular disc with a central hole and symmetrical array of non-central holes using finite element analysis. It investigates the effect of geometric parameters such as R2/R1 ratio of inner to outer disc radius, d/2R1 ratio of hole diameter to disc radius, Rb/(R2-R1) ratio of pitch circle radius to annular width, and number of holes N. Stress concentration factors are derived for these parameters. As number of holes increases, the stress concentration factor decreases. Results are verified against analytical solutions. Contour plots show highest stresses occur near holes.
The document presents a method for solving fuzzy assignment problems using triangular and trapezoidal fuzzy numbers. It formulates the fuzzy assignment problem into a crisp linear programming problem that can be solved using the Hungarian method. The paper also uses Robust's ranking method to transform fuzzy costs into crisp values, allowing conventional solution methods to be applied. It aims to provide a more realistic approach to assignment problems by considering costs as fuzzy numbers rather than deterministic values.
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The document describes a new architecture for a high-speed multiplier accumulator (MAC) unit. The MAC uses a modified Booth encoding algorithm to reduce the number of partial products generated during multiplication. It also uses a hybrid carry save adder structure to improve performance. Additionally, it incorporates a spurious power suppression technique (SPST) to reduce power consumption during the addition process. The MAC accumulates intermediate results as sums and carries rather than using the final adder output to improve output rate. Analysis shows the proposed MAC requires fewer hardware resources, has lower delay, and reduced power compared to previous designs.
This document describes a microcontroller based speed control system for a DC geared motor through an RS-232 interface with a PC. The system uses pulse width modulation to control the speed of the motor. It includes hardware components like a power supply module, microcontroller, LCD display, motor driver IC, and MAX232 level converter. The software development involves coding in C language, compiling the code, and burning the hex file onto the microcontroller using a programmer. The system allows variable speed control of the DC motor through a GUI on the PC. It provides a platform for industrial applications requiring precision speed control of DC motors.
This document summarizes a research paper that proposes a novel seven-level inverter for grid-connected photovoltaic systems. The inverter uses a hybrid cascaded configuration with a novel pulse width modulation technique to generate seven output voltage levels from the DC supply. Simulation results using MATLAB/Simulink are presented to validate the operation of the proposed inverter. The inverter is capable of improving power quality by reducing harmonic distortion compared to traditional two-level inverters.
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
O documento discute três gêneros de parasitas que infectam peixes: Chilodonella spp, Trichodina spp e Dactylogyrus spp. Trichodina spp se fixa nas brânquias dos peixes e se alimenta de bactérias, proliferando em ambientes eutróficos ou de má qualidade da água e causando patologias. Dactylogyrus spp e Chilodenella spp também infectam as brânquias, causando perda de apetite, respiração acelerada e morte dos
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help boost feelings of calmness and well-being.
Este documento breve presenta modelos de relojes, el Porsche Boxster 2013, la lancha eléctrica más rápida y un androide en el espacio en menos de 3 oraciones.
Este documento es un mapa conceptual sobre redes sociales y biblioteca virtual creado por Araujo Carlex para la Universidad Fermín Toro en Barquisimeto, Venezuela el 13 de mayo de 2013.
Comparison of Dynamic Scheduling Techniques in Flexible Manufacturing SystemIJERA Editor
Scheduling is an important tool in the manufacturing area since productivity is inherently linked to how well the resources are used to increase efficiency and reduce waste. The present article analyzes and provides comparison of modern techniques used for solving dynamic scheduling problem in flexible manufacturing system. These techniques are often impractical in dynamic real world environments where there are complex constraints and a variety of unexpected disruptions. This paper defines the modern techniques of dynamic scheduling and provides a literature survey of scheduling which are presented in recent few years. The principles of several dynamic scheduling techniques, namely dispatching rules, heuristics, genetic algorithms and artificial intelligence techniques are describe in details and comparison of their potential.
Feature selection in high-dimensional datasets is
considered to be a complex and time-consuming problem. To
enhance the accuracy of classification and reduce the execution
time, Parallel Evolutionary Algorithms (PEAs) can be used. In
this paper, we make a review for the most recent works which
handle the use of PEAs for feature selection in large datasets.
We have classified the algorithms in these papers into four main
classes (Genetic Algorithms (GA), Particle Swarm Optimization
(PSO), Scattered Search (SS), and Ant Colony Optimization
(ACO)). The accuracy is adopted as a measure to compare the
efficiency of these PEAs. It is noticeable that the Parallel Genetic
Algorithms (PGAs) are the most suitable algorithms for feature
selection in large datasets; since they achieve the highest accuracy.
On the other hand, we found that the Parallel ACO is timeconsuming
and less accurate comparing with other PEA.
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.
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.
The document discusses developing cell layouts in a job shop using group technology. It aims to determine the minimum machine capacity required to form inter-cellular layouts using group technology, improve machine performance measures and efficiency by eliminating unnecessary machines and implementing repetitive lots. Previous research identified formation methods but did not specify minimum capacity or performance improvement. The work to be done includes selecting machines and parts to develop a matrix, creating a new algorithm using existing notation, and designing a computational model of the new cell layout.
Timetable Generator Using Genetic AlgorithmIRJET Journal
This document discusses using a genetic algorithm to generate a university timetable. It begins with an abstract that introduces the topic and objectives. It then provides background on genetic algorithms and their use for timetable generation. The document reviews related literature on using genetic algorithms and scheduling constraints. It proposes developing a university timetable generator using a genetic algorithm with adaptive and elite traits. The methodology section outlines the genetic algorithm process, including input data, parameter settings, fitness evaluation, and constraint checking to generate an optimal timetable solution. The overall aim is to create an artificial intelligence system that can automatically generate timetables while minimizing errors.
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.
The document discusses adapting a genetic algorithm to schedule variants of manufacturing shop models. It presents two examples - a flow shop and a shop with alternative machine routing - to demonstrate how genetic algorithms can be applied to schedule jobs for more complex shop models. The genetic algorithm is implemented using standard spreadsheet software, showing how simple it is to apply genetic algorithms to production scheduling problems.
IRJET- Optimization of Riser through Genetic AlorithmIRJET Journal
This document discusses using a genetic algorithm to optimize riser design in casting processes. It begins by introducing casting and the importance of riser design in improving yield percentage and reducing defects from shrinkage. Genetic algorithms are then described as an optimization technique inspired by natural evolution that can find optimal solutions. The document outlines the methodology for using a genetic algorithm to optimize riser design, including generating an initial population, evaluating fitness, and applying genetic operators like reproduction, crossover and mutation over generations to evolve optimal riser dimensions that minimize volume while meeting constraints. The goal is to apply this approach to optimize riser design for a differential case casting and reduce shrinkage defects.
IRJET- Optimization of Riser through Genetic AlorithmIRJET Journal
This document discusses using a genetic algorithm to optimize riser design in casting processes. It begins by introducing casting and the importance of riser design in improving yield percentage. Currently, risers are designed using empirical methods which are time-consuming and inaccurate. The document then describes genetic algorithms and how they can be applied to optimize riser design by encoding potential solutions and using genetic operators like selection, crossover and mutation to evolve better designs over generations. The objective is to minimize riser volume while meeting constraints. The methodology section outlines the basic principles and steps of a genetic algorithm, including reproducing fit individuals, crossover to recombine strings, and mutation of genes to form new solutions.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IRJET- Machine Learning Techniques for Code OptimizationIRJET Journal
This document summarizes research on using machine learning techniques for code optimization. It discusses how machine learning can help address two main compiler optimization problems: optimization selection and phase ordering. It provides an overview of supervised and unsupervised machine learning approaches that have been used, including linear models, decision trees, clustering, and evolutionary algorithms. Key papers applying these techniques to problems like optimization selection, phase ordering, and code compression are summarized. The document concludes that machine learning is increasingly being applied to compiler optimization problems to develop intelligent heuristics with minimal human input.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Harnessing deep learning algorithms to predict software refactoringTELKOMNIKA JOURNAL
During software maintenance, software systems need to be modified by adding or modifying source code. These changes are required to fix errors or adopt new requirements raised by stakeholders or market place. Identifying thetargeted piece of code for refactoring purposes is considered a real challenge for software developers. The whole process of refactoring mainly relies on software developers’ skills and intuition. In this paper, a deep learning algorithm is used to develop a refactoring prediction model for highlighting the classes that require refactoring. More specifically, the gated recurrent unit algorithm is used with proposed pre-processing steps for refactoring predictionat the class level. The effectiveness of the proposed model is evaluated usinga very common dataset of 7 open source java projects. The experiments are conducted before and after balancing the dataset to investigate the influence of data sampling on the performance of the prediction model. The experimental analysis reveals a promising result in the field of code refactoring prediction
Application of Genetic Algorithm in Software Engineering: A ReviewIRJESJOURNAL
Abstract. The software engineering is comparatively new and regularly changing field. The big challenge of meeting strict project schedules with high quality software requires that the field of software engineering be automated to large extent and human resource intervention be minimized to optimum level. To achieve this goal the researcher have explored the potential of machine learning approaches as they are adaptable, have learning ability. In this paper, we take a look at how genetic algorithm (GA) can be used to build tool for software development and maintenance tasks.
Predictive job scheduling in a connection limited system using parallel genet...Mumbai Academisc
The document discusses predictive job scheduling in a connection limited system using parallel genetic algorithms. It introduces the problem of job scheduling in parallel computing systems and describes existing non-predictive greedy algorithms. The proposed approach uses genetic algorithms to develop a predictive model for job scheduling that learns from previous experiences to improve scheduling efficiency over time. The goal is to schedule jobs in a way that optimizes system metrics like utilization and throughput while minimizing user metrics like turnaround time.
Parallel and distributed genetic algorithm with multiple objectives to impro...khalil IBRAHIM
we argue that the timetabling problem reflects the problem of scheduling university courses, So you must specify the range of time periods and a group of instructors for a range of lectures to check a set of constraints and reduce the cost of other constraints ,this is the problem called NP-hard, it is a class of problems that are informally, it’s mean that necessary operations to solve the problem will increase exponentially and directly proportional to the size of the problem, The construction of timetable is the most complicated problem that was facing many universities, and increased by size of the university data and overlapping disciplines between colleges, and when a traditional algorithm (EA) is unable to provide satisfactory results, a distributed EA (dEA), which deploys the population on distributed systems, it also offers an opportunity to solve extremely high dimensional problems through distributed coevolution using a divide-and-conquer mechanism, Further, the distributed environment allows a dEA to maintain population diversity, thereby avoiding local optima and also facilitating multi-objective search, by employing different distribution models to parallelize the processing of EAs, we designed a genetic algorithm suitable for Universities environment and the constraints facing it when building timetable for lectures.
A Non-Revisiting Genetic Algorithm for Optimizing Numeric Multi-Dimensional F...ijcsa
The document describes a non-revisiting genetic algorithm with adaptive mutation for optimizing multi-dimensional numeric functions. The proposed algorithm avoids revisiting previously evaluated solutions by replacing any duplicates with a mutated version of either the best or random individual. The mutation rate adapts over generations, starting with more exploration and ending with more exploitation. Experimental results on 9 benchmark functions in 2 and 4 dimensions show the proposed algorithm achieves better best and average fitness than a standard genetic algorithm, with improvements ranging from 10-98% depending on the function.
Parameter Estimation of Software Reliability Growth Models Using Simulated An...Editor IJCATR
The parameter estimation of Goel’s Okomotu Model is performed victimisation simulated annealing. The Goel’s Okomotu
Model is predicated on Exponential model and could be a easy non-homogeneous Poisson method (NHPP) model. Simulated
annealing could be a heuristic optimisation technique that provides a method to flee local optima. The information set is optimized
using simulated annealing technique. SA could be a random algorithmic program with higher performance than Genetic algorithmic
program (GA) that depends on the specification of the neighbourhood structure of a state area and parameter settings for its cooling
schedule.
The document discusses decision support systems for planning and scheduling. It covers application areas like manufacturing and services, system requirements, optimization techniques, and implementation issues. The key topics include common optimization techniques like dispatching rules, integer programming, and local search methods. It also discusses interface design, modular system architecture, and commercial scheduling packages.
1. E. Raj Kumar, K.Annamalai / International Journal of Engineering Research and Applications
(IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.481-484
Nature Inspired Algorithm For Computer Aided Process
Planning
E. Raj Kumar*, K.Annamalai**,
*(School of mechanical and Building science, VIT University, Vellore-14)
**(School of mechanical and Building science, VIT University, Vellore-14)
ABSTRACT
Many firms are under significant activity which is done next to product design or
pressure to produce low cost, high quality specification, which is defined in terms of the design
products that can compete in the world engineer’s terminology (eg.,size, shape, tolerance,
marketplace. It might be useful if the process plan finish and material properties/ treatment) and
could be dynamically modified to consider the transforming it into a detailed list of manufacturing
current state in the manufacturing shop floor. The instructions which are stated in terms that are useful
research mainly observes the relationship between to manufacturing personnel (eg., specifications for
numbers of product being produced, production materials, processes, sequences and machining
time, waiting time, number of item waiting and parameters).For many products there is no unique
also instantaneous use of machines in the job shop manufacturing method and many variations of the
condition. The development of process plan and process plan could provide an acceptable end
the determination of standard process time are product.
essential functions for many manufacturing
organization. These functions are time consuming II .COMPUTER AIDED PROCESS
and require significant skill or great deal of PLANNING (CAPP):
experiential knowledge. So a better process plan is In General, the manufacturing process is
need to reduce the manufacturing lead time and to planned in a static manner, whether it is prepared by
improve the overall productivity. Various human or with computer assistance. However, due to
machining operations that are involved in the the dynamic fluctuation of customer demands in the
manufacture of any component depend on the market, manufacturing enterprises are facing
facilities (machines, tools etc.,) available in the difficulties in rapidly responding to Market changes.
factory. In factory environment each of operations Thus, it might be useful if the process plan could be
can be carried out on different machines and dynamically modified to consider the current state of
tools. This combinational problem is tried non- the manufacturing system so as to study the
traditional optimization tool genetic algorithms, manufacturing performance in unpredictable
specially designed operators are used for cross situation. For the purpose of increasing the
over and mutation. Minimization of process total responsiveness of manufacturing systems to handle
time is taken as criteria for optimization. This unstable market changes, the integration of Process
paper deals with the use of genetic algorithm to planning and production scheduling concept has been
perform process planning and process time introduced[1]. There is much interest by
prediction activities. manufacturing firms in automating the task of
process planning using computer – aided process
Keywords: Process planning, Genetic algorithm, planning (CAPP) systems. The shop-trained people
processing time. who are familiar with the details of machining and
other processes are gradually retiring and these
I .INTRODUCTION people will be unavailable in the future to do process
Process planning is an engineering task that planning an alternative way of accomplishing this
determines the detailed manufacturing requirements function is needed, and CAPP systems are providing
for transforming a raw material into a completed part, this alternative. CAPP is usually considered to be a
within the available machining resources. The output part of computer aided manufacturing (CAM).
of process planning generally includes operations,
machine tools, cutting tools, fixtures, machining III.GENETIC ALGORITHM:
parameters, etc. In this approach, the process The GA’s have been developed by Holland.
planning for a component is modeled in a network by A GA is a search algorithm that is on the biological
simultaneously considering the selection of principles of selection, reproduction and mutation. It
operations, machines and operation sequence, as well uses the principles to explore and exploit the solution
as the constraints imposed by the precedence space associated with a problem. The reason behind
relationships between operations and available the using of GA in present study is given below.
machining resources. Genetic algorithm is developed It is the global search algorithm resulting in
to find the optimal plan. Process planning is the better / improved solutions to complex
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2. E. Raj Kumar, K.Annamalai / International Journal of Engineering Research and Applications
(IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.481-484
problems of various manufacturing as well 3.1.4. Fitness function:
as related fields, where analytic methods or For the minimization problems, it is an
other algorithms are either difficult to equivalent maximization problem chosen such that
formulate or solve. the optimum point remains unchanged. A number of
It is capable of providing multiple solutions such transformations are possible. The fitness
simultaneously by nature of its working. function often used is F(x) = 1 / (f(x)).This
transformation does not alter the location of the
3.1 GENETIC OPERATORS: minimum, but converts a minimization problem into
The mechanics of simple genetic algorithms an equivalent maximization problem.
are surprisingly simple, involving nothing more
complex than coping string, swapping partial strings 3.1.5. Working of Genetic Algorithm:
and bit conversion. A simple genetic algorithm that With the initial population for the next
yields good results in many practical problems is generation is to be generated which are the offspring
composed of three operators. of the current generation. The reproduction operator
Reproduction selects the fit individuals from the current population
Crossover and places them in a matting pool. Highly fit
Mutation individuals get more copies in the matting pool where
as the less fit ones get fewer copies.
3.1.1 Reproduction:
Reproduction is a process in which
individual strings are copied according to their 3.1.6. GA applied in process planning:
function values. Coping strings according according Optimization is collective process of
to their fitness values means that strings with a higher determining a set of conditions required to achieve
fitness value have a higher probability of the best results from a given situation. The basic
contributing one or more offspring in the next concept of Genetic Algorithm (GA) is to encode a
generation. This operator, of course, is an artificial potential solution to a problem of series parameters.
version of natural selection. It is important to note A single set of parameter value is treated as the
that no new string formed in the reproduction phase. genome of an individual solution. An initial
population of individuals is generated at random or
3.1.2. Crossover: statistically. Genetic algorithm was successfully
Crossover combines a pair of “parent” applied in a robust and general way to the process
solutions to produce a pair of “children” by breaking plan optimization problem. This lead to a highly
both parent vectors at the same point and generalized extension into parallel plan optimization
reassembling the first part of one parent solution with as a new way of tackling the job- shop scheduling
the second part of the other, and vice versa. problem[2]. The application of the technique to
process plan optimization is then described in detail
further.
3.1.7. PSEUDO-CODE FOR GENETIC
ALGORITHM
Procedure for genetic algorithm:
begin
set time t := 0
Fig 1 select an initial population P(t) = {x1t, x2t, …,
xnt}
3.1.3. Mutation: while the termination condition is not met, do:
Mutation operation randomly changes the begin
offspring resulted from crossover. Mutation is evaluate fitness of each member of P(t);
intended to prevent falling of all solutions in the select the fittest members from P(t);
population into a local optimum of the solved generate off springs of the fittest pairs using
problem. genetic operators;
crossover with specific Pc and site;
mutation with specific Pm and site;
replace the weakest members of P(t) by these
offsprings;
set time t := t+1
end
end.
Fig 2
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3. E. Raj Kumar, K.Annamalai / International Journal of Engineering Research and Applications
(IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.481-484
3.1.8. Process planning for a sample component 3.1.10. Steps involved in solving the problem:
– A case study. The steps involved to obtain optimal time
The following component is considered for for the manufacturing component using GA as
optimizing the process planning. The objective follows.
function taken here is process time ( it should be STEP 1: Generate initial population randomly.
minimized). The process time includes all machining STEP 2: Evaluate the fitness function value for the
operations involved in manufacturing the component. initial population.
STEP 3: Select the parent sequence based on the
fitness value using rank based roulette wheel
selection strategy.
STEP 4: Perform crossover operation to generate
offspring’s using single point cross over technique.
STEP 5: Perform mutation operation using swapping
operator.
STEP 6: Evaluate the fitness value for the new
chromosomes and store the best solution.
STEP 7: If the termination condition is reached go to
6 else go to step 3.
STEP 8: Print the best solution.
3.1.11. GA PARAMETERS:
Fig 3 : Sample component The following parameters are fixed before
initializing the population.
3.1.9. Desired objective function: Number of solution / Chromosome / population size:
10
Number of iterations / Generations: 100
Crossover probability: 0.9
Mutation probability : 0.3.
Table 3: Initial population (operation sequence):
1 3 3 5 4 6 5
Where
1 5 2 6 5 7 3
T – Total time ( in min )
S.T – Setting time ( in min) 1 2 5 5 3 3 6
M.T – Machining Time ( in min) 1 3 5 2 6 5 7
T.T – Travel time ( in min) 1 2 3 6 5 7 5
n – No of iterations. 1 3 4 6 6 5 2
Table 1: Assumptions made 1 2 4 5 3 6 6
OPERATION OPERATIONS 1 6 3 6 4 6 4
NUMBER 1 7 4 5 5 3 3
1 FACING 1 2 4 4 5 4 5
2 DRILLING 1
3 DRILLING 2 Table 4: MACHINE SEQUENCE:
4 DRILLING 3
5 TAPER 8 10 8 10 9 8 11
6 SLOT 1 10 10 9 8 8 11 10
7 SLOT 2 8 9 11 9 10 8 11
10 9 8 11 11 9 8
Table 2: Machine Number
MACHINE MACHINE 9 8 8 9 8 10 11
NUMBER 9 11 9 9 9 10 10
8 LATHE 8 10 10 8 11 9 8
10 9 11 11 10 11 9
9 SHAPER
9 8 10 8 9 8 8
10 MILLING 8 9 9 8 8 9 11
MACHINE
11 DRILLING
MACHINE
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4. E. Raj Kumar, K.Annamalai / International Journal of Engineering Research and Applications
(IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.481-484
3.2.0. Evaluation of objective function values: integrated manufacturing”, “Vol 6, Nos 1 &
The total time taken as an objective function 2, (1993), pp 74 – 86.
and that should be minimum. From objective [5] Bhaskaran, K. “Process plan selection”.
function the sequence of operation is determined. International journal of production research,
28 (8), (1990) pp 1527-1539.
3.2.1. Illustration: [6] Kim, K., Park, K. and KO, J. “A symbiotic
152 154 143 163 164 176 178 185 190 203 evolutionary algorithm for the integration of
process planning and job shop scheduling”.
IV. Results and discussion: Computer Operation Research, 30, (2003)
The most optimal solution for the problem is pp 1151-1171.
obtained by using genetic algorithm with crossover [7] David E.Goldberg, Genetic Algorithm in
probability 0.9, mutation probability 0.3. They are as search, Optimization and machine learning
follows (Addison Wesley, New York, 1989).
[8] Carvalho, J.D.A. “An integrated approach
4.1. Operation sequence: to process planning and scheduling”.
1 2 4 6 5 7 3 Doctoral Thesis, the University of
Nottingham. 1996.
4.2. Corresponding operations: [9] Zhang.Y.F.Zhang, and A.Y.C.Nee, “Using
Facing,DrillingØ10mm,DrillingØ 16mm,Slot genetic algorithm in process planning for job
5x5mm,taper 5x45˚,Slot 7 x 8mm,Drilling Ø8 mm. shop machining” IEEE transaction on
evolutionary computation, vol., 1, no, 4,
4.3. Machine sequence: Nov 1997, pp278-289.
10 11 10 11 10 10 10 [10] G.H.Ma, F.Zhang, Y.F.Zhang, An
Automated process planning system based
V. Conclusion on Genetic Algorithm and Simulated
The manual process plan preparation for a Annealing, ASME Design Engineering
component takes lot of time. Even then the real Technical conferences & computers and
optimum may not be found since the process planner information in engineering conferences
may have made some cut short assumptions. Again it September 2002, Canada.
is highly impossible to check each and every
possibility of the process plan. But using this
developed System, the user can get various
machining sequences and finally the optimized
process plan in much less time. This paper deals the
process planning optimization using GA by
minimizing the process time. The proposed
methodology is tested using program developed in C
language. The GA has been successfully applied to
search in the process planning model. The work is
being extended to multi objective optimization and
also influence of crossover and mutation probability
of GA performance.
References
[1] Alting, L. and Zhang, H. “Computer aided
process planning: the state-of-the-art
survey”. International journal of production
research, 27 (4), (1989) pp 553-585.
[2] MS Reddy, “A genetic algorithm for job
shop scheduling, “IE (I) journal PR, Vol
85,.(2004),pp 32-35.
[3] Mark Wilhelm, Alice E.smith and Bopaya
Bidanda, “Process planning using an
integrated expert system and neural network
approach,” Department of Industrial
Engineering, University of Pittsburgh,
Pennsylvania, USA.
[4] Philip Husbands, “An ecosystem model for
integrated production planning, “computer
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