Optimization of Simultaneous Scheduling for Machines and Automated Guided Vehicles Using Fuzzy Genetic Algorithm

Flexible manufacturing system (FMS) has been introduced by the researchers as an integrated manufacturing environment. Automated guided vehicles (AGVs) introduced as the main tool of material handling systems in FMS. While the scheduling of AGVs and machines are highly related; simultaneous sched...

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Main Author:	Badakhshian, Mostafa
Format:	Thesis
Language:	English English
Published:	2009
Subjects:
Online Access:	http://psasir.upm.edu.my/id/eprint/7354/1/FK_2009_45a.pdf
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id	my-upm-ir.7354
record_format	uketd_dc
spelling	my-upm-ir.73542013-05-27T07:34:51Z Optimization of Simultaneous Scheduling for Machines and Automated Guided Vehicles Using Fuzzy Genetic Algorithm 2009-06 Badakhshian, Mostafa Flexible manufacturing system (FMS) has been introduced by the researchers as an integrated manufacturing environment. Automated guided vehicles (AGVs) introduced as the main tool of material handling systems in FMS. While the scheduling of AGVs and machines are highly related; simultaneous scheduling of machines and AGVs has been proposed in the literature. Genetic algorithm (GA) proposed as a robust tool for optimization of scheduling problems. Setting the proper crossover and mutation rates are of vital importance for the performance of the GA. Fuzzy logic controllers (FLCs) have been used in the literature to control key parameters of the GA which is addressed as fuzzy GA (FGA). A new application of FGA method in simultaneous scheduling of AGVs and machines is presented. The general GA is modified for the aforementioned application; more over an FLC is developed to control mutation and crossover rates of the GA. The objective of proposed FGA method is to minimize the makespan, production completion time of all jobs that they are produced simultaneously. An optimal sequence of operations is obtained by GA. There is a heuristic algorithm to assign the AGVs to the operations. As the main findings, the performance of GA in simultaneous scheduling of AGVs and machines is enhanced by using proposed method, furthermore a new mutation operator has been proposed. Several experiments have been done to the proposed test cases. The results showed that tournament selection scheme may outperform roulette wheel in this problem. Various combinations of population size and number of generations are compared to each other in terms of their objective function. In large scale problems FGA method may outperforms GA method, while in small and medium problems they have the same performance. The fluctuation of obtained makespan in FGA method is less than GA method which means that it is more probable to find a better solution by FGA rather than GA. 2009-06 Thesis http://psasir.upm.edu.my/id/eprint/7354/ http://psasir.upm.edu.my/id/eprint/7354/1/FK_2009_45a.pdf application/pdf en public masters Universiti Putra Malaysia Faculty of Engineering English
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English English
topic
spellingShingle	Badakhshian, Mostafa Optimization of Simultaneous Scheduling for Machines and Automated Guided Vehicles Using Fuzzy Genetic Algorithm
description	Flexible manufacturing system (FMS) has been introduced by the researchers as an integrated manufacturing environment. Automated guided vehicles (AGVs) introduced as the main tool of material handling systems in FMS. While the scheduling of AGVs and machines are highly related; simultaneous scheduling of machines and AGVs has been proposed in the literature. Genetic algorithm (GA) proposed as a robust tool for optimization of scheduling problems. Setting the proper crossover and mutation rates are of vital importance for the performance of the GA. Fuzzy logic controllers (FLCs) have been used in the literature to control key parameters of the GA which is addressed as fuzzy GA (FGA). A new application of FGA method in simultaneous scheduling of AGVs and machines is presented. The general GA is modified for the aforementioned application; more over an FLC is developed to control mutation and crossover rates of the GA. The objective of proposed FGA method is to minimize the makespan, production completion time of all jobs that they are produced simultaneously. An optimal sequence of operations is obtained by GA. There is a heuristic algorithm to assign the AGVs to the operations. As the main findings, the performance of GA in simultaneous scheduling of AGVs and machines is enhanced by using proposed method, furthermore a new mutation operator has been proposed. Several experiments have been done to the proposed test cases. The results showed that tournament selection scheme may outperform roulette wheel in this problem. Various combinations of population size and number of generations are compared to each other in terms of their objective function. In large scale problems FGA method may outperforms GA method, while in small and medium problems they have the same performance. The fluctuation of obtained makespan in FGA method is less than GA method which means that it is more probable to find a better solution by FGA rather than GA.
format	Thesis
qualification_level	Master's degree
author	Badakhshian, Mostafa
author_facet	Badakhshian, Mostafa
author_sort	Badakhshian, Mostafa
title	Optimization of Simultaneous Scheduling for Machines and Automated Guided Vehicles Using Fuzzy Genetic Algorithm
title_short	Optimization of Simultaneous Scheduling for Machines and Automated Guided Vehicles Using Fuzzy Genetic Algorithm
title_full	Optimization of Simultaneous Scheduling for Machines and Automated Guided Vehicles Using Fuzzy Genetic Algorithm
title_fullStr	Optimization of Simultaneous Scheduling for Machines and Automated Guided Vehicles Using Fuzzy Genetic Algorithm
title_full_unstemmed	Optimization of Simultaneous Scheduling for Machines and Automated Guided Vehicles Using Fuzzy Genetic Algorithm
title_sort	optimization of simultaneous scheduling for machines and automated guided vehicles using fuzzy genetic algorithm
granting_institution	Universiti Putra Malaysia
granting_department	Faculty of Engineering
publishDate	2009
url	http://psasir.upm.edu.my/id/eprint/7354/1/FK_2009_45a.pdf
_version_	1747810692024500224

Optimization of Simultaneous Scheduling for Machines and Automated Guided Vehicles Using Fuzzy Genetic Algorithm

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