1) The document describes a queuing model developed to analyze customer impatience in service systems using concepts from queuing theory and Markov processes.
2) The model considers a multi-server queuing system with finite capacity and incorporates reverse balking and reneging behaviors of impatient customers.
3) Steady-state solutions are derived for key performance measures like expected system size, average reneging rate, and average reverse balking rate. Numerical examples and sensitivity analysis are presented.
This document summarizes a study that used various machine learning techniques to classify customers as either 2G or 3G network users based on their usage data. It discusses preprocessing the data, building models using decision trees, lazy classifiers, and boosting, and combining the models' predictions. The best performing technique was boosting decision trees, which correctly classified 88.58% of customers through 10-fold cross-validation. While imperfect, combining multiple models led to more reliable classification than any single model.
Planning and Scheduling of a Corrugated Cardboard Manufacturing Process in IoTijtsrd
The automation has revolutionized the traditional product development scheme by using advanced design and manufacturing technologies such as computer aided design, process planning, and scheduling. However, research in this field was still based mostly on experimentation, as most manufacturing companies did not use simulation techniques in the implementation of their manufacturing planning and scheduling process. In order to address this problem, software developers have put simulation software tools in the market such as Enterprise Resource Planning ERP , Advanced Planning and Scheduling APS , and Risk based Planning and Scheduling RPS systems. In this paper, a methodology to model high degree of accuracy for the production floor, the planning and scheduling of corrugated cardboard manufacturing process through RPS simulation in Internet of Things IoT environment is established. The RPS model is able to generate a deterministic schedule without randomness, create a risk analysis of the planning and scheduling, and handle the uncertainty. Bruno Kemen | Sarhan M. Musa "Planning and Scheduling of a Corrugated Cardboard Manufacturing Process in IoT" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd37965.pdf Paper URL : https://www.ijtsrd.com/engineering/electrical-engineering/37965/planning-and-scheduling-of-a-corrugated-cardboard-manufacturing-process-in-iot/bruno-kemen
IRJET- The Machine Learning: The method of Artificial IntelligenceIRJET Journal
This document discusses machine learning and its role in artificial intelligence. It begins with an abstract that explains machine learning is widely used in artificial intelligence to enable systems to learn and make decisions without being explicitly programmed. It then provides an introduction to machine learning, explaining it allows software to learn from data and improve predictions without being explicitly programmed. The document also discusses related work from other researchers on topics like supervised learning, unsupervised learning, and evaluating different machine learning methods. It describes problems that can occur during the learning process like bias, noise, and pattern recognition. Finally, it provides algorithms for hierarchical clustering and k-means clustering as examples of unsupervised learning methods.
Graphical Analysis of Simulated Financial Data Using RIRJET Journal
This document summarizes research analyzing simulated financial data using the R programming language. The researchers developed tools to calculate basic financial ratios using publicly available company data to help investors determine a company's true market value. They performed DuPont analysis on real data from a London company and ANOVA analysis on simulated data generated in large amounts. The document describes the analytical techniques used, including the assumptions and methodology for two-way ANOVA testing. The researchers concluded their study of simulated data and DuPont analysis helped develop an understanding of how financial ratios behave in both efficient and inefficient markets.
SUPPLY CHAIN MODELING WITH UNCERTAINTY IN DEMAND AND SUPPLY USING FUZZY LOGICIAEME Publication
In this paper, we have analyzed a serial operational level Supply Chain
Performance like fill rate, Build –To - Stock etc., under an uncertainty environment
for the optimality using a stochastic customer order. It evaluates minimum cost in
supply chain, simultaneously optimize quantitative decision variables and illustrates
the significance of capacity variable. Numerical examples are presented to illustrate
the benefit of the proposed strategies and the effects of changes on the cost and
parameters are studied.
IRJET- Matrix Multiplication using Strassen’s MethodIRJET Journal
1) The document discusses matrix multiplication and Strassen's algorithm for matrix multiplication. Strassen's algorithm improves upon the normal O(n^3) matrix multiplication algorithm by reducing the time complexity to O(n^2.81).
2) It provides code to implement basic matrix multiplication in C and explains Strassen's divide and conquer approach which uses 7 matrix operations rather than 8 to multiply 2x2 matrices.
3) Adopting Strassen's algorithm can help reduce time complexity compared to the normal matrix multiplication approach.
A M ULTI -O BJECTIVE B ASED E VOLUTIONARY A LGORITHM AND S OCIAL N ETWOR...IJCI JOURNAL
In this paper, a multi-objective based NSGA-II algo
rithm is proposed for dynamic job-shop scheduling
problem (DJSP) with random job arrivals and machine
breakdowns. In DJSP schedules are usually
inevitable due to various unexpected disruptions. T
o handle this problem, it is necessary to select
appropriate key machines at the beginning of the si
mulation instead of random selection. Thus, this pa
per
seeks to address on approach called social network
analysis method to identify the key machines of the
addressed DJSP. With identified key machines, the e
ffectiveness and stability of scheduling i.e., make
span
and starting time deviations of the computational c
omplex NP-hard problem has been solved with propose
d
multi-objective based hybrid NSGA-ll algorithm. Sev
eral experiments studies have been conducted and
comparisons have been made to demonstrate the effic
iency of the proposed approach with classical multi
-
objective based NSGA-II algorithm. The experimental
results illustrate that the proposed method is ver
y
effective in various shop floor conditions
Building & Evaluating Predictive model: Supermarket Business CaseSiddhanth Chaurasiya
- The document describes building predictive models using decision tree and regression modeling to predict which customers are likely to purchase new organic products being introduced by a supermarket.
- Both decision tree and logistic regression models were created, with the decision tree models performing slightly better based on various evaluation metrics such as cumulative lift, ROC curve, and average square error.
- The top variables influencing the likelihood of a customer purchasing organics according to the models were gender, age, and affluence level.
This document summarizes a study that used various machine learning techniques to classify customers as either 2G or 3G network users based on their usage data. It discusses preprocessing the data, building models using decision trees, lazy classifiers, and boosting, and combining the models' predictions. The best performing technique was boosting decision trees, which correctly classified 88.58% of customers through 10-fold cross-validation. While imperfect, combining multiple models led to more reliable classification than any single model.
Planning and Scheduling of a Corrugated Cardboard Manufacturing Process in IoTijtsrd
The automation has revolutionized the traditional product development scheme by using advanced design and manufacturing technologies such as computer aided design, process planning, and scheduling. However, research in this field was still based mostly on experimentation, as most manufacturing companies did not use simulation techniques in the implementation of their manufacturing planning and scheduling process. In order to address this problem, software developers have put simulation software tools in the market such as Enterprise Resource Planning ERP , Advanced Planning and Scheduling APS , and Risk based Planning and Scheduling RPS systems. In this paper, a methodology to model high degree of accuracy for the production floor, the planning and scheduling of corrugated cardboard manufacturing process through RPS simulation in Internet of Things IoT environment is established. The RPS model is able to generate a deterministic schedule without randomness, create a risk analysis of the planning and scheduling, and handle the uncertainty. Bruno Kemen | Sarhan M. Musa "Planning and Scheduling of a Corrugated Cardboard Manufacturing Process in IoT" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd37965.pdf Paper URL : https://www.ijtsrd.com/engineering/electrical-engineering/37965/planning-and-scheduling-of-a-corrugated-cardboard-manufacturing-process-in-iot/bruno-kemen
IRJET- The Machine Learning: The method of Artificial IntelligenceIRJET Journal
This document discusses machine learning and its role in artificial intelligence. It begins with an abstract that explains machine learning is widely used in artificial intelligence to enable systems to learn and make decisions without being explicitly programmed. It then provides an introduction to machine learning, explaining it allows software to learn from data and improve predictions without being explicitly programmed. The document also discusses related work from other researchers on topics like supervised learning, unsupervised learning, and evaluating different machine learning methods. It describes problems that can occur during the learning process like bias, noise, and pattern recognition. Finally, it provides algorithms for hierarchical clustering and k-means clustering as examples of unsupervised learning methods.
Graphical Analysis of Simulated Financial Data Using RIRJET Journal
This document summarizes research analyzing simulated financial data using the R programming language. The researchers developed tools to calculate basic financial ratios using publicly available company data to help investors determine a company's true market value. They performed DuPont analysis on real data from a London company and ANOVA analysis on simulated data generated in large amounts. The document describes the analytical techniques used, including the assumptions and methodology for two-way ANOVA testing. The researchers concluded their study of simulated data and DuPont analysis helped develop an understanding of how financial ratios behave in both efficient and inefficient markets.
SUPPLY CHAIN MODELING WITH UNCERTAINTY IN DEMAND AND SUPPLY USING FUZZY LOGICIAEME Publication
In this paper, we have analyzed a serial operational level Supply Chain
Performance like fill rate, Build –To - Stock etc., under an uncertainty environment
for the optimality using a stochastic customer order. It evaluates minimum cost in
supply chain, simultaneously optimize quantitative decision variables and illustrates
the significance of capacity variable. Numerical examples are presented to illustrate
the benefit of the proposed strategies and the effects of changes on the cost and
parameters are studied.
IRJET- Matrix Multiplication using Strassen’s MethodIRJET Journal
1) The document discusses matrix multiplication and Strassen's algorithm for matrix multiplication. Strassen's algorithm improves upon the normal O(n^3) matrix multiplication algorithm by reducing the time complexity to O(n^2.81).
2) It provides code to implement basic matrix multiplication in C and explains Strassen's divide and conquer approach which uses 7 matrix operations rather than 8 to multiply 2x2 matrices.
3) Adopting Strassen's algorithm can help reduce time complexity compared to the normal matrix multiplication approach.
A M ULTI -O BJECTIVE B ASED E VOLUTIONARY A LGORITHM AND S OCIAL N ETWOR...IJCI JOURNAL
In this paper, a multi-objective based NSGA-II algo
rithm is proposed for dynamic job-shop scheduling
problem (DJSP) with random job arrivals and machine
breakdowns. In DJSP schedules are usually
inevitable due to various unexpected disruptions. T
o handle this problem, it is necessary to select
appropriate key machines at the beginning of the si
mulation instead of random selection. Thus, this pa
per
seeks to address on approach called social network
analysis method to identify the key machines of the
addressed DJSP. With identified key machines, the e
ffectiveness and stability of scheduling i.e., make
span
and starting time deviations of the computational c
omplex NP-hard problem has been solved with propose
d
multi-objective based hybrid NSGA-ll algorithm. Sev
eral experiments studies have been conducted and
comparisons have been made to demonstrate the effic
iency of the proposed approach with classical multi
-
objective based NSGA-II algorithm. The experimental
results illustrate that the proposed method is ver
y
effective in various shop floor conditions
Building & Evaluating Predictive model: Supermarket Business CaseSiddhanth Chaurasiya
- The document describes building predictive models using decision tree and regression modeling to predict which customers are likely to purchase new organic products being introduced by a supermarket.
- Both decision tree and logistic regression models were created, with the decision tree models performing slightly better based on various evaluation metrics such as cumulative lift, ROC curve, and average square error.
- The top variables influencing the likelihood of a customer purchasing organics according to the models were gender, age, and affluence level.
Artificial Neural Networks can achieve high degree of computation rates by
employing a massive number of simple processing elements with a high degree of
connectivity between elements. In this paper an attempt is made to present a Constraint
Satisfaction Adaptive Neural Network (CSANN) to solve the generalized job-shop
scheduling problem and it shows how to map a difficult constraint satisfaction job-shop
scheduling problem onto a simple neural net, where the number of neural processors equals
the number of operations, and the number of interconnections grows linearly with the total
number of operations. The proposed neural network can be easily constructed and can adjust
its weights of connections based on the sequence and resource constraints of the job-shop
scheduling problem during its processing. Simulation studies have shown that the proposed
neural network produces better solutions to job-shop scheduling problem.
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.
11.fuzzy logic analysis based on inventory considering demand and stock quant...Alexander Decker
This document summarizes a research article that proposes using fuzzy logic to model inventory control systems. The approach considers demand levels and current stock quantities to determine appropriate inventory actions. Membership functions and fuzzy rules are developed based on expert knowledge. The model is tuned using training data to better match expert decisions. Results show the fuzzy logic approach can effectively approximate experienced managers' inventory control judgments. The approach does not require complex mathematical modeling and can be adapted over time as new data is obtained.
Fuzzy logic analysis based on inventory considering demand and stock quantity...Alexander Decker
This document presents a fuzzy logic approach to inventory control. It considers demand and stock quantity on hand as inputs and proposes inventory actions as the output. A fuzzy model is constructed using if-then rules defined based on expert knowledge. Gaussian membership functions are used to define linguistic variables for the inputs and output. The model is tuned using training data to better approximate expert decisions. Results show the fuzzy logic approach can provide appropriate inventory actions for given demand and stock levels. This approach does not require complex mathematical modeling and can be useful for inventory management.
Operation research ppt chapter two mitkumitku assefa
The document discusses linear programming, which involves optimizing an objective function subject to constraints. It provides examples of formulating linear programming problems from descriptions of resource allocation scenarios. Linear programming can be used to maximize profits or minimize costs by determining the optimal allocation of limited resources among competing activities. The key components of a linear programming model are decision variables, constraints, and an objective function. Graphical and algebraic (simplex) methods can be used to solve linear programming problems. Special cases like multiple optimal solutions, unbounded solutions, and infeasible solutions are also discussed.
Part I: Predictive models (Decision Tree and Regression) using SAS Enterprise Miner
Part II: Decision Tree using R.
Part III: Market-Basket Analysis using SAS miner.
Forecasting warranty returns with Wiebull FitTonda MacLeod
Analyze Wise provides a statistical analysis of warranty return data to forecast future returns using a Weibull distribution model. The analysis involves obtaining time-to-failure data from historical warranty returns, performing a regression to identify the best fitting distribution model and associated parameters, and using the model to predict return counts by time period. The forecasts can help companies plan repair resources, manage customer relationships, and evaluate warranty expenses and product performance.
IRJET- Distribution Selection for Pump Manufacturing CompaniesIRJET Journal
This document discusses distribution selection for pump manufacturing companies. It presents a mathematical model for optimizing the allocation of demand from markets to production facilities while minimizing total costs. The model considers inputs like demand from markets, factory capacities and costs of production and transportation between factories and markets. The objective is to determine the optimal quantities to ship from each factory to each market. The document also provides examples of distribution selections for four Indian pump companies based on their most demanded regions. It concludes that the mathematical model and LINGO software can help identify the best distribution selections.
SIMULATION-BASED OPTIMIZATION USING SIMULATED ANNEALING FOR OPTIMAL EQUIPMENT...Sudhendu Rai
The paper describes a software toolkit that enables the data-driven simulation-based optimization of print shops It enables quick modeling of complex print production environments under the cellular production framework. The software toolkit automates several steps of the modeling process by taking declarative inputs from the end-user and then automatically generating complex simulation models that are used to determine improved design and operating points. This paper describes the addition of another layer of automation consisting of simulation-based optimization using simulated-annealing that enables automated search of a large number of design alternatives in the presence of operational constraints to determine a cost-optimal solution. The results of the application of this approach to a real-world problem are also described.
PREDICTING BANKRUPTCY USING MACHINE LEARNING ALGORITHMSIJCI JOURNAL
This paper is written for predicting Bankruptcy using different Machine Learning Algorithms. Whether the company will go bankrupt or not is one of the most challenging and toughest question to answer in the 21st Century. Bankruptcy is defined as the final stage of failure for a firm. A company declares that it has gone bankrupt when at that present moment it does not have enough funds to pay the creditors. It is a global
problem. This paper provides a unique methodology to classify companies as bankrupt or healthy by applying predictive analytics. The prediction model stated in this paper yields better accuracy with standard parameters used for bankruptcy prediction than previously applied prediction methodologies.
Measurement and Comparison of Productivity Performance Under Fuzzy Imprecise ...Waqas Tariq
The creation of goods and services requires changing the expended resources into the output goods and services. How efficiently we transform these input resources into goods and services depends on the productivity of the transformation process. However, it has been observed there is always a vagueness or imprecision associated with the values of inputs and outputs. Therefore, it becomes hard for a productivity measurement expert to specify the amount of resources and the outputs as exact scalar numbers. The present paper, applies fuzzy set theory to measure and compare productivity performance of transformation processes when numerical data cannot be specified in exact terms. The approach makes it possible to measure and compare productivity of organizational units (including non-government and non-profit entities) when the expert inputs can not be specified as exact scalar quantities. The model has been applied to compare productivity of different branches of a company.
IRJET-Handwritten Digit Classification using Machine Learning ModelsIRJET Journal
This document summarizes a research paper that compares different machine learning models for classifying handwritten digits using the MNIST dataset. It finds that a Support Vector Machine Classifier achieves the highest accuracy of 98.3% compared to 96.3% for a Random Forest Classifier and 88.97% for a Logistic Regression model. The paper preprocesses the images, implements the three classifiers, and evaluates their performance based on accuracy and other metrics like precision and recall. It concludes that the SVM Classifier is the most accurate for classifying handwritten digits from the MNIST dataset.
I have done this analysis using SAS on a dataset with 5000 records. I have used CART and Logistic regression to build a predictive model to identify customers which are likely to shift to competitors network.
Seven Basic Quality Control Tools أدوات ضبط الجودة السبعةMohamed Khaled
The 7 QC tools are fundamental instruments to improve the process and product quality. They are used to examine the production process.
► The seven basic tools are:
1- Check sheet
2- Pareto analysis
3- Cause and Effect Diagram
4- Scatter plot
5- Histogram
6- Flowchart
7- Control charts
-------------------------------------------------------------------------------------
#7_Basic_Quality_Control_Tools #Check_sheet #Pareto_analysis #Fishbone #Scatter_plot #Histogram #Flowchart #Control_charts #CFturbo #Pump_simulation_using_ANSYS #Water_Hammer #أدوات_ضبط_الجودة_السبعة #نموذج_التحقق #مخطط_باريتو #مخطط_السبب_والأثر #مخطط_التشتت #مدرج_تكراري #خرائط_التدفق #خرائط_ضبط_الجودة
This document provides a literature review on personnel scheduling problems. It begins by defining personnel scheduling and explaining why it is an important problem for many organizations. It then discusses different classifications of personnel scheduling problems, such as shift scheduling, days off scheduling, and tour scheduling. The document reviews various solution methods that have been proposed, including linear programming, integer programming, heuristics, and metaheuristics. It also discusses classifications based on personnel characteristics, the decisions being made, constraints, performance measures, solution techniques, and applications in different domains. In summary, the document surveys the landscape of research on personnel scheduling problems, including classifications of the problems and various approaches that have been developed to solve them.
Survey on Feature Selection and Dimensionality Reduction TechniquesIRJET Journal
This document discusses dimensionality reduction techniques for data mining. It begins with an introduction explaining why dimensionality reduction is important for effective machine learning and data mining. It then describes several popular dimensionality reduction algorithms, including Singular Value Decomposition (SVD), Partial Least Squares Regression (PLSR), Linear Discriminant Analysis (LDA), and Locally Linear Embedding (LLE). For each technique, it provides a brief overview of the algorithm and its applications. The document serves to analyze and compare various dimensionality reduction methods and their strengths and weaknesses.
IRJET- A Comprehensive Outline of the Types of SimulationIRJET Journal
1) Simulation is the process of imitating a real-life system to solve problems without interacting with the real system. It allows testing scenarios that may be dangerous, expensive, or impossible in reality.
2) There are two main types of simulation: deterministic and stochastic. Deterministic simulation does not involve randomness, while stochastic simulation introduces randomness.
3) Within stochastic simulation, Monte Carlo simulation is static and measures outputs over time, while discrete event simulation is dynamic and measures outputs when events occur.
Comparative Analysis of Machine Learning Algorithms for their Effectiveness i...IRJET Journal
1. The document presents a comparative analysis of machine learning algorithms for predicting customer churn in the telecom industry.
2. Logistic regression, random forest, and balanced random forest classifiers were evaluated on a dataset of 25,000 customers described by 111 variables.
3. The balanced logistic regression model that used SMOTE to address class imbalance achieved the best performance with an area under the ROC curve of 0.861, accurately predicting churn with an accuracy of 77% and recall of 76% on the test set.
IRJET- Analysis of Various Machine Learning Algorithms for Stock Value Predic...IRJET Journal
This document analyzes and compares the performance of various machine learning algorithms for stock value prediction, including linear regression, logistic regression, k-nearest neighbors (kNN), decision trees, and support vector machines (SVM). The algorithms are tested on stock market data from five companies. SVM, kNN, and decision trees are found to have the best performance based on mean squared error, with kNN and decision trees being the most accurate predictors of stock market value.
Artificial Neural Networks can achieve high degree of computation rates by
employing a massive number of simple processing elements with a high degree of
connectivity between elements. In this paper an attempt is made to present a Constraint
Satisfaction Adaptive Neural Network (CSANN) to solve the generalized job-shop
scheduling problem and it shows how to map a difficult constraint satisfaction job-shop
scheduling problem onto a simple neural net, where the number of neural processors equals
the number of operations, and the number of interconnections grows linearly with the total
number of operations. The proposed neural network can be easily constructed and can adjust
its weights of connections based on the sequence and resource constraints of the job-shop
scheduling problem during its processing. Simulation studies have shown that the proposed
neural network produces better solutions to job-shop scheduling problem.
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.
11.fuzzy logic analysis based on inventory considering demand and stock quant...Alexander Decker
This document summarizes a research article that proposes using fuzzy logic to model inventory control systems. The approach considers demand levels and current stock quantities to determine appropriate inventory actions. Membership functions and fuzzy rules are developed based on expert knowledge. The model is tuned using training data to better match expert decisions. Results show the fuzzy logic approach can effectively approximate experienced managers' inventory control judgments. The approach does not require complex mathematical modeling and can be adapted over time as new data is obtained.
Fuzzy logic analysis based on inventory considering demand and stock quantity...Alexander Decker
This document presents a fuzzy logic approach to inventory control. It considers demand and stock quantity on hand as inputs and proposes inventory actions as the output. A fuzzy model is constructed using if-then rules defined based on expert knowledge. Gaussian membership functions are used to define linguistic variables for the inputs and output. The model is tuned using training data to better approximate expert decisions. Results show the fuzzy logic approach can provide appropriate inventory actions for given demand and stock levels. This approach does not require complex mathematical modeling and can be useful for inventory management.
Operation research ppt chapter two mitkumitku assefa
The document discusses linear programming, which involves optimizing an objective function subject to constraints. It provides examples of formulating linear programming problems from descriptions of resource allocation scenarios. Linear programming can be used to maximize profits or minimize costs by determining the optimal allocation of limited resources among competing activities. The key components of a linear programming model are decision variables, constraints, and an objective function. Graphical and algebraic (simplex) methods can be used to solve linear programming problems. Special cases like multiple optimal solutions, unbounded solutions, and infeasible solutions are also discussed.
Part I: Predictive models (Decision Tree and Regression) using SAS Enterprise Miner
Part II: Decision Tree using R.
Part III: Market-Basket Analysis using SAS miner.
Forecasting warranty returns with Wiebull FitTonda MacLeod
Analyze Wise provides a statistical analysis of warranty return data to forecast future returns using a Weibull distribution model. The analysis involves obtaining time-to-failure data from historical warranty returns, performing a regression to identify the best fitting distribution model and associated parameters, and using the model to predict return counts by time period. The forecasts can help companies plan repair resources, manage customer relationships, and evaluate warranty expenses and product performance.
IRJET- Distribution Selection for Pump Manufacturing CompaniesIRJET Journal
This document discusses distribution selection for pump manufacturing companies. It presents a mathematical model for optimizing the allocation of demand from markets to production facilities while minimizing total costs. The model considers inputs like demand from markets, factory capacities and costs of production and transportation between factories and markets. The objective is to determine the optimal quantities to ship from each factory to each market. The document also provides examples of distribution selections for four Indian pump companies based on their most demanded regions. It concludes that the mathematical model and LINGO software can help identify the best distribution selections.
SIMULATION-BASED OPTIMIZATION USING SIMULATED ANNEALING FOR OPTIMAL EQUIPMENT...Sudhendu Rai
The paper describes a software toolkit that enables the data-driven simulation-based optimization of print shops It enables quick modeling of complex print production environments under the cellular production framework. The software toolkit automates several steps of the modeling process by taking declarative inputs from the end-user and then automatically generating complex simulation models that are used to determine improved design and operating points. This paper describes the addition of another layer of automation consisting of simulation-based optimization using simulated-annealing that enables automated search of a large number of design alternatives in the presence of operational constraints to determine a cost-optimal solution. The results of the application of this approach to a real-world problem are also described.
PREDICTING BANKRUPTCY USING MACHINE LEARNING ALGORITHMSIJCI JOURNAL
This paper is written for predicting Bankruptcy using different Machine Learning Algorithms. Whether the company will go bankrupt or not is one of the most challenging and toughest question to answer in the 21st Century. Bankruptcy is defined as the final stage of failure for a firm. A company declares that it has gone bankrupt when at that present moment it does not have enough funds to pay the creditors. It is a global
problem. This paper provides a unique methodology to classify companies as bankrupt or healthy by applying predictive analytics. The prediction model stated in this paper yields better accuracy with standard parameters used for bankruptcy prediction than previously applied prediction methodologies.
Measurement and Comparison of Productivity Performance Under Fuzzy Imprecise ...Waqas Tariq
The creation of goods and services requires changing the expended resources into the output goods and services. How efficiently we transform these input resources into goods and services depends on the productivity of the transformation process. However, it has been observed there is always a vagueness or imprecision associated with the values of inputs and outputs. Therefore, it becomes hard for a productivity measurement expert to specify the amount of resources and the outputs as exact scalar numbers. The present paper, applies fuzzy set theory to measure and compare productivity performance of transformation processes when numerical data cannot be specified in exact terms. The approach makes it possible to measure and compare productivity of organizational units (including non-government and non-profit entities) when the expert inputs can not be specified as exact scalar quantities. The model has been applied to compare productivity of different branches of a company.
IRJET-Handwritten Digit Classification using Machine Learning ModelsIRJET Journal
This document summarizes a research paper that compares different machine learning models for classifying handwritten digits using the MNIST dataset. It finds that a Support Vector Machine Classifier achieves the highest accuracy of 98.3% compared to 96.3% for a Random Forest Classifier and 88.97% for a Logistic Regression model. The paper preprocesses the images, implements the three classifiers, and evaluates their performance based on accuracy and other metrics like precision and recall. It concludes that the SVM Classifier is the most accurate for classifying handwritten digits from the MNIST dataset.
I have done this analysis using SAS on a dataset with 5000 records. I have used CART and Logistic regression to build a predictive model to identify customers which are likely to shift to competitors network.
Seven Basic Quality Control Tools أدوات ضبط الجودة السبعةMohamed Khaled
The 7 QC tools are fundamental instruments to improve the process and product quality. They are used to examine the production process.
► The seven basic tools are:
1- Check sheet
2- Pareto analysis
3- Cause and Effect Diagram
4- Scatter plot
5- Histogram
6- Flowchart
7- Control charts
-------------------------------------------------------------------------------------
#7_Basic_Quality_Control_Tools #Check_sheet #Pareto_analysis #Fishbone #Scatter_plot #Histogram #Flowchart #Control_charts #CFturbo #Pump_simulation_using_ANSYS #Water_Hammer #أدوات_ضبط_الجودة_السبعة #نموذج_التحقق #مخطط_باريتو #مخطط_السبب_والأثر #مخطط_التشتت #مدرج_تكراري #خرائط_التدفق #خرائط_ضبط_الجودة
This document provides a literature review on personnel scheduling problems. It begins by defining personnel scheduling and explaining why it is an important problem for many organizations. It then discusses different classifications of personnel scheduling problems, such as shift scheduling, days off scheduling, and tour scheduling. The document reviews various solution methods that have been proposed, including linear programming, integer programming, heuristics, and metaheuristics. It also discusses classifications based on personnel characteristics, the decisions being made, constraints, performance measures, solution techniques, and applications in different domains. In summary, the document surveys the landscape of research on personnel scheduling problems, including classifications of the problems and various approaches that have been developed to solve them.
Survey on Feature Selection and Dimensionality Reduction TechniquesIRJET Journal
This document discusses dimensionality reduction techniques for data mining. It begins with an introduction explaining why dimensionality reduction is important for effective machine learning and data mining. It then describes several popular dimensionality reduction algorithms, including Singular Value Decomposition (SVD), Partial Least Squares Regression (PLSR), Linear Discriminant Analysis (LDA), and Locally Linear Embedding (LLE). For each technique, it provides a brief overview of the algorithm and its applications. The document serves to analyze and compare various dimensionality reduction methods and their strengths and weaknesses.
IRJET- A Comprehensive Outline of the Types of SimulationIRJET Journal
1) Simulation is the process of imitating a real-life system to solve problems without interacting with the real system. It allows testing scenarios that may be dangerous, expensive, or impossible in reality.
2) There are two main types of simulation: deterministic and stochastic. Deterministic simulation does not involve randomness, while stochastic simulation introduces randomness.
3) Within stochastic simulation, Monte Carlo simulation is static and measures outputs over time, while discrete event simulation is dynamic and measures outputs when events occur.
Comparative Analysis of Machine Learning Algorithms for their Effectiveness i...IRJET Journal
1. The document presents a comparative analysis of machine learning algorithms for predicting customer churn in the telecom industry.
2. Logistic regression, random forest, and balanced random forest classifiers were evaluated on a dataset of 25,000 customers described by 111 variables.
3. The balanced logistic regression model that used SMOTE to address class imbalance achieved the best performance with an area under the ROC curve of 0.861, accurately predicting churn with an accuracy of 77% and recall of 76% on the test set.
IRJET- Analysis of Various Machine Learning Algorithms for Stock Value Predic...IRJET Journal
This document analyzes and compares the performance of various machine learning algorithms for stock value prediction, including linear regression, logistic regression, k-nearest neighbors (kNN), decision trees, and support vector machines (SVM). The algorithms are tested on stock market data from five companies. SVM, kNN, and decision trees are found to have the best performance based on mean squared error, with kNN and decision trees being the most accurate predictors of stock market value.
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1. Which of the following is a measure of operations and supply management efficiency used by Wall Street? Dividend payout ratio Receivable turnover Current ratio Financial leverage Earnings per share growth
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Which of the following is an input to the master production schedule (mps)johann11374
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1. Which of the following is a measure of operations and supply management efficiency used by Wall Street? Dividend payout ratio Receivable turnover Current ratio Financial leverage Earnings per share growth
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An assumption of learning curve theory is which of the followingjohann11370
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1. Which of the following is a measure of operations and supply management efficiency used by Wall Street? Dividend payout ratio Receivable turnover Current ratio Financial leverage Earnings per share growth
2. An activity-system map is which of the following? A diagram that shows how a company's strategy is delivered to customers A timeline displaying major planned events A network guide to route airlines A facility layout schematic noting what is done where A listing of activities that make up a project
From an operational perspective, yield management is most effective under whi...johann11371
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1. Which of the following is a measure of operations and supply management efficiency used by Wall Street? Dividend payout ratio Receivable turnover Current ratio Financial leverage Earnings per share growth
2. An activity-system map is which of the following? A diagram that shows how a company's strategy is delivered to customers A timeline displaying major planned events A network guide to route airlines A facility layout schematic noting what is done where A listing of activities that make up a project
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1. Which of the following is a measure of operations and supply management efficiency used by Wall Street? Dividend payout ratio Receivable turnover Current ratio Financial leverage Earnings per share growth
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2. An activity-system map is which of the following? A diagram that shows how a company's strategy is delivered to customers A timeline displaying major planned events A network guide to route airlines A facility layout schematic noting what is done where A listing of activities that make up a project
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1. Which of the following is a measure of operations and supply management efficiency used by Wall Street? Dividend payout ratio Receivable turnover Current ratio Financial leverage Earnings per share growth
2. An activity-system map is which of the following? A diagram that shows how a company's strategy is delivered to customers A timeline displaying major planned events A network guide to route airlines A facility layout schematic noting what is done where A listing of activities that make up a project
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1. Which of the following is a measure of operations and supply management efficiency used by Wall Street? Dividend payout ratio Receivable turnover Current ratio Financial leverage Earnings per share growth
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1. Which of the following is a measure of operations and supply management efficiency used by Wall Street? Dividend payout ratio Receivable turnover Current ratio Financial leverage Earnings per share growth
2. An activity-system map is which of the following? A diagram that shows how a company's strategy is delivered to customers A timeline displaying major planned events A network guide to route airlines A facility layout schematic noting what is done where A listing of activities that make up a project
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1. Which of the following is a measure of operations and supply management efficiency used by Wall Street? Dividend payout ratio Receivable turnover Current ratio Financial leverage Earnings per share growth
2. An activity-system map is which of the following? A diagram that shows how a company's strategy is delivered to customers A timeline displaying major planned events A network guide to route airlines A facility layout schematic noting what is done where A listing of activities that make up a project
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1.
Which of the following is a measure of operations and supply management efficiency used by Wall Street?
Dividend payout ratio
Receivable turnover
Current ratio
Financial leverage
Earnings per share growth
Similar to decision making uncertain environment a queuing theory approach (20)
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
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to enroll for the donor request from the application itself. If the admin wish to make user
a registered donor, with some of the formalities with the organization it can be done.
Specialization of this application is that the user will not have to register on sign-in for
searching the blood banks and blood donors it can be just done by installing the
application to the mobile.
The purpose of making this application is to save the user’s time for searching blood of
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SQLite database. This application will provide most of basic functionality required for an
emergency time application. All the details of Blood banks and Blood donors are stored
in the database i.e. SQLite.
This application allowed the user to get all the information regarding blood banks and
blood donors such as Name, Number, Address, Blood Group, rather than searching it on
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#Abstract:
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- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
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- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
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- Validate access.
- Exploiting IAM PassRole Misconfiguration
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Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
Applications of artificial Intelligence in Mechanical Engineering.pdf
decision making uncertain environment a queuing theory approach
1. International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-2, Issue-6, June- 2016]
Infogain Publication (Infogainpublication.com) ISSN: 2454-1311
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Decision Making Uncertain Environment - A
Queuing Theory Approach
Bhupender Kumar Som
Associate Professor, JIMS, Rohini, New Delhi, India
Abstract— Consumer behavior is one of the most uncertain
phenomenons. Customer impatience is one of those
uncertain phenomenon’s which is a threat to any business.
Customer impatience results in loss of customers and
business. Stochastic modeling provides numerical
measurement of necessary measures of performance in any
business up-to a certain extent. In this paper a multi-server
Markovian queuing system is developed with reverse
balking and impatient customers. Reverse balking is a very
new concept introduced in stochastic queuing models.
While reneging is one of the most known phenomenon in
queuing theory. Steady-state solution of the newly
developed model is derived. Necessary measures of
performance are obtained and numerical results are
presented. Sensitivity analysis of the model is also
performed. MATLAB and MS Excel are used as and when
needed.
Keywords— Reverse balking, customer impatience,
retention of customers, stochastic modeling, queuing
theory.
I. INTRODUCTION AND LITERATURE
REVIEW
In this era of globalization and liberalization managing
business has become a challenging task. Consumer behavior
is one of the most uncertain characteristics of business
environment. Customers have become more selective.
Brand switching is more frequent. Due to higher level of
expectations, customers get more impatient with a particular
firm. Customer impatience has also become a burning
problem in the corporate world. Queuing theory offers
various stochastic models that can be used in various
service systems facing customer impatience. By adopting
and applying these stochastic models strategy making
becomes highly effective. The premier work on customer
impatience in queuing theory appeared in [Haight, 1957,
1959], [Anker & Gafarian, 1963a, 1963b], [Bareer, 1957]
etc. Since then a number of papers have appeared on this
concept (reneging and balking). In these models, reneging
and balking is a function of system size/ queue length.
Larger is the system size more is the reneging and similar is
the case of balking. But, when it comes to sensitive
businesses like investment, selection of a food court,
selection of a service station etc. more number of customers
with a particular firm become the attracting (investing)
factor for more investing customers. Thus, the probability
of joining in such a firm is high. Modeling such a system as
a queuing system indicates that the probability of balking
will be low when the system size is more and vice-versa,
which is balking in the reverse sense (we call it Reverse
Balking).
The concept of reverse balking is introduced by [Jain, et.
al., 2014], they studied a single server Markovain queuing
system with reverse balking. [Kumar et. al., 2014] further
introduce notion of reverse reneging and applied it with
reverse balking. [Kumar et. al., 2013, 2014] designed
queuing systems for various environments and further
optimized them for various parameters.
Finding impatience a threat to business firms employ
various strategies to retain a reneging customer and they
manage to do it with some probability. [Kumar, et. al.,
2011] introduced the concept of retention of reneged
customer in their work. They study a single –server queuing
system with retention of reneged customers and balking.
[Kumar, et. al., 2012] also study a multi-server queue with
discouraged arrivals and retention of reneged customers.
[Kumar, et. al., 2012, 2012a] further extend their work on
single and multi-server feedback queues. Literature survey
unfolds the need of the study due to following reasons.
Once a customer moves in to the system by looking at the
large number of customers already present in the system, he
may find the service unsatisfactory, as it is difficult for the
firms to handle a huge chunk of customers at times. The
customer starts experiencing delay and dissatisfaction in
service. The customer becomes impatient due to this and
considers leaving the system without completion of his
service. This customer impatience can be termed as
Reneging in queuing literature. [Som, 2014] developed a
2. International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-2, Issue-6, June- 2016]
Infogain Publication (Infogainpublication.com) ISSN: 2454-1311
www.ijaems.com Page | 809
single-server queuing model by incorporating customer
impatience and reverse balking. He also performed
economic analysis of the model. Extending the work of the
paper.
Owing to the practically valid aspects of above mentioned
concepts, sensitive businesses with customer impatience are
formulated as queuing system in this paper. Consider any
life insurance company, where the purchase of policy refers
to the arrival of a customer in the queuing system
(insurance firms), the processed claim refers to as the
departure from the queuing system, where the claim
processing department is a multi-server and finite system
capacity (the number of policies it can accommodate). The
claims are processed in order of their arrival (i.e. the queue
discipline is FCFS). We incorporate the reverse balking and
reneging into this model. The model is based in Markovian
assumptions.
We present steady-state analysis of the stochastic models as
described above and derive important measures which help
in the management of sensitive businesses like investment
business. Numerical examples are provided for more clarity.
Rest of the paper is structured as follows: in section 2
assumptions under which the model is developed are
presented; section 3 deals with the mathematical
formulation; in section 4 steady state solution is derived;
section 5 deals with measures of performance; Numerical
illustrations and sensitivity analysis of the model is
performed in section 6; conclusions and future work are
provided in section7.
II. MODEL ASSUMPTIONS
1. The arrival to a queuing system (insurance firm) occur,
one by one in accordance with a Poisson process with
mean rateλ. The inter-arrival times are independently,
identically and exponentially distributed with
parameterλ.
2. There is a multi-server and the policy claims are
processed in parallel. The service times are
independently, identically and exponentially
distributed with parameter µ such as = for <
. = for ≥ .
3. The capacity of the system is finite, say N.
4. The policy claims are processed in order of their
arrival, i.e. the queue discipline is First-come, First-
served.
5. (a) When the system is empty, the customers balk (do
not purchase policy) with probability and may
purchase with probability p’ (= 1 – q’).
(b) When there is at-least one customer in the system,
the customers balk with a probability 1 − and join
the system with probability . Such kind of balking
is referred to as reverse balking.
6. The policy holders keeping their policies in force after
some time, say T may get impatient due to certain
reasons and decide to surrender their services before
completion (the customer wait up-to certain time T and
may leave the system before getting service due to
impatience). The reneging times (T) are independently,
identically and exponentially distributed with
parameterξ.
III. STOCHASTIC MODEL FORMULATION
Differential difference equations of the model is given by:
= −λ ′
+ µ
; n =0 (1)
= λ ′ −
1
− 1
λ + µ + 2µ
; n =1 (2)
=
− 1
− 1
λ −
− 1
λ + µ + ! + 1 µ " #
2 ≤ < (3)
=
− 1
− 1
λ −
− 1
λ + µ + − ξ + % µ + ! + 1 − "ξ & #
≥ (4)
3. International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-2, Issue-6, June- 2016]
Infogain Publication (Infogainpublication.com) ISSN: 2454-1311
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= λ − % µ + − ξ &
; n = N (5)
IV. STEADY- STATE SOLUTION
In steady state lim*→∞ = , lim*→∞ ′ = 0. Therefore the equations (1) to (5) become:
0 = −λ ′
+ µ
; n =0 (6)
0 = λ ′ −
1
− 1
λ + µ + 2µ
; n =1 (7)
0 =
− 1
− 1
λ −
− 1
λ + µ + ! + 1 µ " #
2 ≤ < (8)
0 =
− 1
− 1
λ −
− 1
λ + µ + − ξ + % µ + ! + 1 − " ξ& #
≥ (9)
= λ − % µ + − ξ &
; n = N (10)
Steady-state solution of the model is obtained by solving (6) – (10) iteratively. Probability of n customers in the system can be
given by:
=
.
/
/
/
0
/
/
/
12
− 1 !
− 1
4
5
6
78
9 ′ , <
2
− 1 !
− 1
4
5
+ : − ξ
4
5
6
;
78<8;
9 ′ , ≥
2
− 2 !
− 1
4
5
+ : − ξ
4
5
6
;
78<8;
9 ′ , =
=
Using the normalization condition
1
1
N
n
n
P
=
=∑ , we get
+ > + >
8;
;
8
+ = 1
= ?1 + 2
− 1 !
− 1
4
5
6
78
9 ′
+ 2
− 1 !
− 1
4
5
+ : − ξ
4
5
6
;
78<8;
9 ′
+ 2
− 2 !
− 1
4
5
+ : − ξ
4
5
6
;
78<8;
9 ′@
5. International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-2, Issue-6, June- 2016]
Infogain Publication (Infogainpublication.com) ISSN: 2454-1311
www.ijaems.com Page | 812
3.7 0.47274 0.00020 8.25781
3.8 0.46051 0.00017 8.25616
3.9 0.44899 0.00015 8.25431
4.0 0.43811 0.00013 8.25232
4.1 0.42781 0.00011 8.25021
4.2 0.41804 0.00009 8.24801
4.3 0.40875 0.00008 8.24575
4.4 0.39991 0.00007 8.24343
4.5 0.39148 0.00006 8.24108
4.6 0.38343 0.00006 8.23870
4.7 0.37573 0.00005 8.23631
4.8 0.36836 0.00004 8.23392
4.9 0.36129 0.00004 8.23152
5.0 0.35451 0.00003 8.22914
An increasing rate of service ensures a large number of serviced customers leaving the system that leaves a negative impact on
system size. This can be observed from table -1. Following figure shows change in system size with increasing rate of service.
Fig.1: Ls Vs
6.1 Sensitivity Analysis
In this section sensitivity analysis of the model is presented. Variations in required measures of performance are observed with
respective variable. Results are presented through graphs for better insight
Table.2:
µ =3, ξ =0.1, q′ =0.8, c=3, N =15
Mean Arrival Rate
(λ)
Expected System Size
(Ls)
Average Rate of Reneging
(Rr)
Average Rate of Reverse Balking
(Rb')
5 0.29905 0.00001 4.10322
6 0.35450 0.00003 4.93749
7 0.41003 0.00008 5.77227
8 0.46650 0.00018 6.60563
0.00000
0.10000
0.20000
0.30000
0.40000
0.50000
0.60000
0.70000
3.0 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4.0 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 5.0
6. International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-2, Issue-6, June- 2016]
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9 0.52505 0.00038 7.43524
10 0.58730 0.00074 8.25796
11 0.65557 0.00138 9.06939
12 0.73319 0.00250 9.86309
13 0.82493 0.00436 10.62960
14 0.93730 0.00738 11.35519
15 1.07892 0.01209 12.02055
16 1.26051 0.01922 12.59966
17 1.49435 0.02960 13.05966
18 1.79298 0.04416 13.36238
19 2.16673 0.06371 13.46872
20 2.62057 0.08878 13.34601
21 3.15088 0.11936 12.97729
22 3.74367 0.15476 12.36948
23 4.37553 0.19362 11.55642
24 5.01751 0.23411 10.59444
25 5.64072 0.27431 9.55130
From table -2 it is clearly visible that, with increase in average arrival rate, expected system size increases. An increasing
expected system size leads to high confidence of customers with the firm and rate of reverse balking decreases therefore. Due to
this more and more arriving customers join the particular firm. The insight can be observed from graph below. On other hand rate
of reneging increases gradually as increasing number creates a dense network due to high system size that leads to high level of
impatience.
Fig.2: Rb vs λ
Figure -1 clearly states that more and more arrivals cause an increase in system size due to which rate of reverse balking
decreases.
0.00000
2.00000
4.00000
6.00000
8.00000
10.00000
12.00000
14.00000
16.00000
5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Rb vs λ
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Table.3:
µ =3, λ =2, q′ =0.2, c=3, N =15
Rate of Reneging
(ξ)
Expected System Size
(Ls)
Rate of Reneging
(Rr)
0.05 0.354518 0.000018
0.06 0.354516 0.000021
0.07 0.354515 0.000024
0.08 0.354513 0.000028
0.09 0.354512 0.000031
0.1 0.354510 0.000035
0.11 0.354509 0.000038
0.12 0.354507 0.000041
0.13 0.354505 0.000045
0.14 0.354504 0.000048
0.15 0.354502 0.000052
From table -3, it can be observe that increasing rate of reneging causes decrease in expected system size and increase in average
rate of reneging. This is because increasing rate of reneging states that more and more customers are moving out of the system
without completing their service.
Fig.3: E vs Rr
Figure -3 represents increase in average rate of reneging with increase in reneging rate that is obvious.
Table.4:
ξ =0.2, µ =3, λ =10, c=3, N =15
Probability of Reverse Balking
(q')
Expected System Size
(Ls)
Average Rate of Reverse Balking
(Rb')
0.1 1.01918 0.00128
0.000000
0.000010
0.000020
0.000030
0.000040
0.000050
0.000060
0.05 0.06 0.07 0.08 0.09 0.1 0.11 0.12 0.13 0.14 0.15
ξ and Rr
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0.2 0.99310 0.00125
0.3 0.96146 0.00121
0.4 0.92229 0.00116
0.5 0.87253 0.00109
0.6 0.80719 0.00101
0.7 0.71763 0.00090
0.8 0.58730 0.00074
0.9 0.38018 0.00048
1.0 0.00000 0.00000
It can be observed from table -5, that with increase in probability of reverse balking when there were no customers in the system
expected system size reduces and at q’ =1 (probability that an arriving customer does not join the system) expected system size
drops to zero. And Rb′ = 10, states that all arriving customers reverse balked.
Fig.4: q’ vs Rb’
Figure -4, represents increasing rate of reverse balking w.r.t. increase in probability of reverse balking.
VII. CONCLUSION
In this paper a multi-server Mrkovian queuing system with
reverse balking and reneging of customers is developed.
Steady-state solution of the model is derived. Necessary
measures of performance are obtained. Numerical results
are obtained by writing and algorithm in MS Excel and
MATLAB. Sensitivity analysis of the model is also
performed. Measures of performance with relevant
variables are studied.
The results are of immense use for making growth
strategies. The model mentioned above can be tailor-made
as per need and want for the firms operating in uncertain
business environment. In future cost-profit analysis of the
model can be presented with optimization.
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0.00000
2.00000
4.00000
6.00000
8.00000
10.00000
12.00000
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
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9. International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-2, Issue-6, June- 2016]
Infogain Publication (Infogainpublication.com) ISSN: 2454-1311
www.ijaems.com Page | 816
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