A simulated annealing algorithm to determine a group layout and production plan in a dynamic cellular manufacturing system

In this paper, a mixed-integer linearized programming (MINLP) model is presented to design a group layout (GL) of a cellular manufacturing system (CMS) in a dynamic environment with considering production planning (PP) decisions. This model incorporates with an extensive coverage of important manufa...

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Main Authors: Reza KiA, Nikbakhsh Javadian, Reza Tavakkoli-Moghaddam
Format: Article
Language:English
Published: Islamic Azad University, Qazvin Branch 2014-03-01
Series:Journal of Optimization in Industrial Engineering
Subjects:
Online Access:http://www.qjie.ir/article_139_a4d0d9a4edb7fdc504e13bc93812b0d9.pdf
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spelling doaj-ec662bc8d485417abe0c4858ed440c262020-11-24T22:45:38ZengIslamic Azad University, Qazvin BranchJournal of Optimization in Industrial Engineering2251-99042423-39352014-03-017143752139A simulated annealing algorithm to determine a group layout and production plan in a dynamic cellular manufacturing systemReza KiA0Nikbakhsh Javadian1Reza Tavakkoli-Moghaddam2Department of Industrial Engineering, Mazandaran University of Science & Technology, Babol, IranDepartment of Industrial Engineering, Mazandaran University of Science & Technology, Babol, IranSchool of Industrial Engineering, College of Engineering, University of Tehran, Tehran, IranIn this paper, a mixed-integer linearized programming (MINLP) model is presented to design a group layout (GL) of a cellular manufacturing system (CMS) in a dynamic environment with considering production planning (PP) decisions. This model incorporates with an extensive coverage of important manufacturing features used in the design of CMSs. There are also some features that make the presented model different from the previous studies. These include: 1) the variable number of cells, 2) machine depot keeping idle machines, and 3) integration of cell formation (CF), GL and PP decisions in a dynamic environment. The objective is to minimize the total costs (i.e., costs of intra-cell and inter-cell material handling, machine relocation, machine purchase, machine overhead, machine processing, forming cells, outsourcing and inventory holding). Two numerical examples are solved by the GAMS software to illustrate the results obtained by the incorporated features. Since the problem is NP-hard, an efficient simulated annealing (SA) algorithm is developed to solve the presented model. It is then tested using several test problems with different sizes and settings to verify the computational efficiency of the developed algorithm in compare to the GAMS software. The obtained results show that the quality of the solutions obtained by SA is entirely satisfactory in compare to GAMS software based on the objective value and computational time, especially for large-sized problems.http://www.qjie.ir/article_139_a4d0d9a4edb7fdc504e13bc93812b0d9.pdfdynamic cellular manufacturing systemsgroup layoutproduction planningsimulated annealing
collection DOAJ
language English
format Article
sources DOAJ
author Reza KiA
Nikbakhsh Javadian
Reza Tavakkoli-Moghaddam
spellingShingle Reza KiA
Nikbakhsh Javadian
Reza Tavakkoli-Moghaddam
A simulated annealing algorithm to determine a group layout and production plan in a dynamic cellular manufacturing system
Journal of Optimization in Industrial Engineering
dynamic cellular manufacturing systems
group layout
production planning
simulated annealing
author_facet Reza KiA
Nikbakhsh Javadian
Reza Tavakkoli-Moghaddam
author_sort Reza KiA
title A simulated annealing algorithm to determine a group layout and production plan in a dynamic cellular manufacturing system
title_short A simulated annealing algorithm to determine a group layout and production plan in a dynamic cellular manufacturing system
title_full A simulated annealing algorithm to determine a group layout and production plan in a dynamic cellular manufacturing system
title_fullStr A simulated annealing algorithm to determine a group layout and production plan in a dynamic cellular manufacturing system
title_full_unstemmed A simulated annealing algorithm to determine a group layout and production plan in a dynamic cellular manufacturing system
title_sort simulated annealing algorithm to determine a group layout and production plan in a dynamic cellular manufacturing system
publisher Islamic Azad University, Qazvin Branch
series Journal of Optimization in Industrial Engineering
issn 2251-9904
2423-3935
publishDate 2014-03-01
description In this paper, a mixed-integer linearized programming (MINLP) model is presented to design a group layout (GL) of a cellular manufacturing system (CMS) in a dynamic environment with considering production planning (PP) decisions. This model incorporates with an extensive coverage of important manufacturing features used in the design of CMSs. There are also some features that make the presented model different from the previous studies. These include: 1) the variable number of cells, 2) machine depot keeping idle machines, and 3) integration of cell formation (CF), GL and PP decisions in a dynamic environment. The objective is to minimize the total costs (i.e., costs of intra-cell and inter-cell material handling, machine relocation, machine purchase, machine overhead, machine processing, forming cells, outsourcing and inventory holding). Two numerical examples are solved by the GAMS software to illustrate the results obtained by the incorporated features. Since the problem is NP-hard, an efficient simulated annealing (SA) algorithm is developed to solve the presented model. It is then tested using several test problems with different sizes and settings to verify the computational efficiency of the developed algorithm in compare to the GAMS software. The obtained results show that the quality of the solutions obtained by SA is entirely satisfactory in compare to GAMS software based on the objective value and computational time, especially for large-sized problems.
topic dynamic cellular manufacturing systems
group layout
production planning
simulated annealing
url http://www.qjie.ir/article_139_a4d0d9a4edb7fdc504e13bc93812b0d9.pdf
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