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Solving an extended multi-row facility layout problem with fuzzy clearances using GA

نویسنده:
S. Safarzadeh
,
حمیدرضا کوشا
,
Hamidreza Koosha
سال
: 2017
چکیده: Multi-row facility layout problem (MRFLP) is a class of facility layout problems, which decides upon the arrangement of facilities in some fixed numbers of rows in order to minimize material handling cost. Nowadays, according to the new layout requirements, the facility layout problems (FLPs) have many applications such as hospital layout, construction site layout planning and layout of logistics facilities. Therefore, we study an extended MRFLP, as a novel layout problem, with the following main assumptions: 1) the facilities are arranged in a two-dimensional area and without splitter rows, 2) multiple products are available, 3) distance between each pair of facilities, due to inaccurate and flexible manufacturing processes and other limitations (such as WIPs, industrial instruments, transportation lines and etc.), is considered as fuzzy number, and 4) the objective function is considered as minimizing the material handling and lost opportunity costs. To model these assumptions, a nonlinear mixed-integer programming model with fuzzy constraints is presented and then converted to a linear mixed-integer programming model. Since the developed model is an NP-hard problem, a genetic algorithm approach is suggested to find the best solutions with a minimum cost function. Additionally, three different crossover methods are compared in the proposed genetic algorithm and finally, a sensitivity analysis is performed to discuss important parameters.
یو آر آی: http://libsearch.um.ac.ir:80/fum/handle/fum/3361770
کلیدواژه(گان): Facility layout,Multi-row facility layout problem (MRFLP),Genetic algorithm,Lost opportunity cost,Fuzzy sets
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    Solving an extended multi-row facility layout problem with fuzzy clearances using GA

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contributor authorS. Safarzadehen
contributor authorحمیدرضا کوشاen
contributor authorHamidreza Kooshafa
date accessioned2020-06-06T13:36:28Z
date available2020-06-06T13:36:28Z
date issued2017
identifier urihttp://libsearch.um.ac.ir:80/fum/handle/fum/3361770
description abstractMulti-row facility layout problem (MRFLP) is a class of facility layout problems, which decides upon the arrangement of facilities in some fixed numbers of rows in order to minimize material handling cost. Nowadays, according to the new layout requirements, the facility layout problems (FLPs) have many applications such as hospital layout, construction site layout planning and layout of logistics facilities. Therefore, we study an extended MRFLP, as a novel layout problem, with the following main assumptions: 1) the facilities are arranged in a two-dimensional area and without splitter rows, 2) multiple products are available, 3) distance between each pair of facilities, due to inaccurate and flexible manufacturing processes and other limitations (such as WIPs, industrial instruments, transportation lines and etc.), is considered as fuzzy number, and 4) the objective function is considered as minimizing the material handling and lost opportunity costs. To model these assumptions, a nonlinear mixed-integer programming model with fuzzy constraints is presented and then converted to a linear mixed-integer programming model. Since the developed model is an NP-hard problem, a genetic algorithm approach is suggested to find the best solutions with a minimum cost function. Additionally, three different crossover methods are compared in the proposed genetic algorithm and finally, a sensitivity analysis is performed to discuss important parameters.en
languageEnglish
titleSolving an extended multi-row facility layout problem with fuzzy clearances using GAen
typeJournal Paper
contenttypeExternal Fulltext
subject keywordsFacility layouten
subject keywordsMulti-row facility layout problem (MRFLP)en
subject keywordsGenetic algorithmen
subject keywordsLost opportunity costen
subject keywordsFuzzy setsen
journal titleApplied Soft Computingfa
pages819-831
journal volume61
journal issue1
identifier linkhttps://profdoc.um.ac.ir/paper-abstract-1064380.html
identifier articleid1064380
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