A methodology for solving facility layout problem considering barriers: genetic algorithm coupled with A search
This work proposes a new methodology and mathematical formulation to address the facility layout problem. The goal is to minimise the total material handling cost subjected to production-derived constraints. This cost is a function of the distance that the products should cover within the facility....
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Veröffentlicht in: | Journal of intelligent manufacturing 2020-03, Vol.31 (3), p.615-640 |
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creator | Besbes, Mariem Zolghadri, Marc Costa Affonso, Roberta Masmoudi, Faouzi Haddar, Mohamed |
description | This work proposes a new methodology and mathematical formulation to address the facility layout problem. The goal is to minimise the total material handling cost subjected to production-derived constraints. This cost is a function of the distance that the products should cover within the facility. The first idea is to use the
A
∗
algorithm to identify the distances between workstations in a more realistic way.
A
∗
determines the shortest path within the facility that contains obstacles and transportation routes. The second idea is to combine a genetic algorithm and the
A
∗
algorithm with a homogenous methodology to improve the quality of the facility layouts. In an iterative way, the layout solution space is explored using the genetic algorithm. We study the impacts of the appropriate crossover and mutation operators and the values of the parameters used in this algorithm on the cost of the proposed arrangements. These operators and parameter values are fine-tuned using Monte Carlo simulations. The facility arrangements are all compared and discussed based on their material handling cost associated with the Euclidean distance, rectilinear distance, and
A
∗
algorithm. Finally, we present a set of conclusions regarding the suggested methodology and discuss our future research goals. |
doi_str_mv | 10.1007/s10845-019-01468-x |
format | Article |
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A
∗
algorithm to identify the distances between workstations in a more realistic way.
A
∗
determines the shortest path within the facility that contains obstacles and transportation routes. The second idea is to combine a genetic algorithm and the
A
∗
algorithm with a homogenous methodology to improve the quality of the facility layouts. In an iterative way, the layout solution space is explored using the genetic algorithm. We study the impacts of the appropriate crossover and mutation operators and the values of the parameters used in this algorithm on the cost of the proposed arrangements. These operators and parameter values are fine-tuned using Monte Carlo simulations. The facility arrangements are all compared and discussed based on their material handling cost associated with the Euclidean distance, rectilinear distance, and
A
∗
algorithm. Finally, we present a set of conclusions regarding the suggested methodology and discuss our future research goals.</description><identifier>ISSN: 0956-5515</identifier><identifier>EISSN: 1572-8145</identifier><identifier>DOI: 10.1007/s10845-019-01468-x</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Advanced manufacturing technologies ; Algorithms ; Business and Management ; Computer simulation ; Control ; Crossovers ; Engineering Sciences ; Euclidean geometry ; Facilities management ; Genetic algorithms ; Iterative methods ; Layouts ; Machines ; Manufacturing ; Manufacturing cells ; Materials handling ; Mechatronics ; Methodology ; Monte Carlo simulation ; Operators (mathematics) ; Parameters ; Processes ; Production ; Robotics ; Shortest-path problems ; Solution space ; Workstations</subject><ispartof>Journal of intelligent manufacturing, 2020-03, Vol.31 (3), p.615-640</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2019</rights><rights>Journal of Intelligent Manufacturing is a copyright of Springer, (2019). All Rights Reserved.</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c445t-79accbda36dc267054363efaa720b228b55950dea38f6b8615d989d761dc238e3</citedby><cites>FETCH-LOGICAL-c445t-79accbda36dc267054363efaa720b228b55950dea38f6b8615d989d761dc238e3</cites><orcidid>0000-0003-2995-0424 ; 0000-0002-0377-2271</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10845-019-01468-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10845-019-01468-x$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>230,314,776,780,881,27903,27904,41467,42536,51297</link.rule.ids><backlink>$$Uhttps://hal.science/hal-03217955$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Besbes, Mariem</creatorcontrib><creatorcontrib>Zolghadri, Marc</creatorcontrib><creatorcontrib>Costa Affonso, Roberta</creatorcontrib><creatorcontrib>Masmoudi, Faouzi</creatorcontrib><creatorcontrib>Haddar, Mohamed</creatorcontrib><title>A methodology for solving facility layout problem considering barriers: genetic algorithm coupled with A search</title><title>Journal of intelligent manufacturing</title><addtitle>J Intell Manuf</addtitle><description>This work proposes a new methodology and mathematical formulation to address the facility layout problem. The goal is to minimise the total material handling cost subjected to production-derived constraints. This cost is a function of the distance that the products should cover within the facility. The first idea is to use the
A
∗
algorithm to identify the distances between workstations in a more realistic way.
A
∗
determines the shortest path within the facility that contains obstacles and transportation routes. The second idea is to combine a genetic algorithm and the
A
∗
algorithm with a homogenous methodology to improve the quality of the facility layouts. In an iterative way, the layout solution space is explored using the genetic algorithm. We study the impacts of the appropriate crossover and mutation operators and the values of the parameters used in this algorithm on the cost of the proposed arrangements. These operators and parameter values are fine-tuned using Monte Carlo simulations. The facility arrangements are all compared and discussed based on their material handling cost associated with the Euclidean distance, rectilinear distance, and
A
∗
algorithm. Finally, we present a set of conclusions regarding the suggested methodology and discuss our future research goals.</description><subject>Advanced manufacturing technologies</subject><subject>Algorithms</subject><subject>Business and Management</subject><subject>Computer simulation</subject><subject>Control</subject><subject>Crossovers</subject><subject>Engineering Sciences</subject><subject>Euclidean geometry</subject><subject>Facilities management</subject><subject>Genetic algorithms</subject><subject>Iterative methods</subject><subject>Layouts</subject><subject>Machines</subject><subject>Manufacturing</subject><subject>Manufacturing cells</subject><subject>Materials handling</subject><subject>Mechatronics</subject><subject>Methodology</subject><subject>Monte Carlo simulation</subject><subject>Operators (mathematics)</subject><subject>Parameters</subject><subject>Processes</subject><subject>Production</subject><subject>Robotics</subject><subject>Shortest-path 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methodology for solving facility layout problem considering barriers: genetic algorithm coupled with A search</title><author>Besbes, Mariem ; Zolghadri, Marc ; Costa Affonso, Roberta ; Masmoudi, Faouzi ; Haddar, Mohamed</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c445t-79accbda36dc267054363efaa720b228b55950dea38f6b8615d989d761dc238e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Advanced manufacturing technologies</topic><topic>Algorithms</topic><topic>Business and Management</topic><topic>Computer simulation</topic><topic>Control</topic><topic>Crossovers</topic><topic>Engineering Sciences</topic><topic>Euclidean geometry</topic><topic>Facilities management</topic><topic>Genetic algorithms</topic><topic>Iterative methods</topic><topic>Layouts</topic><topic>Machines</topic><topic>Manufacturing</topic><topic>Manufacturing cells</topic><topic>Materials handling</topic><topic>Mechatronics</topic><topic>Methodology</topic><topic>Monte Carlo simulation</topic><topic>Operators (mathematics)</topic><topic>Parameters</topic><topic>Processes</topic><topic>Production</topic><topic>Robotics</topic><topic>Shortest-path problems</topic><topic>Solution space</topic><topic>Workstations</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Besbes, Mariem</creatorcontrib><creatorcontrib>Zolghadri, Marc</creatorcontrib><creatorcontrib>Costa Affonso, Roberta</creatorcontrib><creatorcontrib>Masmoudi, Faouzi</creatorcontrib><creatorcontrib>Haddar, Mohamed</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest 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Basic</collection><collection>Hyper Article en Ligne (HAL)</collection><jtitle>Journal of intelligent manufacturing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Besbes, Mariem</au><au>Zolghadri, Marc</au><au>Costa Affonso, Roberta</au><au>Masmoudi, Faouzi</au><au>Haddar, Mohamed</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A methodology for solving facility layout problem considering barriers: genetic algorithm coupled with A search</atitle><jtitle>Journal of intelligent manufacturing</jtitle><stitle>J Intell Manuf</stitle><date>2020-03-01</date><risdate>2020</risdate><volume>31</volume><issue>3</issue><spage>615</spage><epage>640</epage><pages>615-640</pages><issn>0956-5515</issn><eissn>1572-8145</eissn><abstract>This work proposes a new methodology and mathematical formulation to address the facility layout problem. The goal is to minimise the total material handling cost subjected to production-derived constraints. This cost is a function of the distance that the products should cover within the facility. The first idea is to use the
A
∗
algorithm to identify the distances between workstations in a more realistic way.
A
∗
determines the shortest path within the facility that contains obstacles and transportation routes. The second idea is to combine a genetic algorithm and the
A
∗
algorithm with a homogenous methodology to improve the quality of the facility layouts. In an iterative way, the layout solution space is explored using the genetic algorithm. We study the impacts of the appropriate crossover and mutation operators and the values of the parameters used in this algorithm on the cost of the proposed arrangements. These operators and parameter values are fine-tuned using Monte Carlo simulations. The facility arrangements are all compared and discussed based on their material handling cost associated with the Euclidean distance, rectilinear distance, and
A
∗
algorithm. Finally, we present a set of conclusions regarding the suggested methodology and discuss our future research goals.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10845-019-01468-x</doi><tpages>26</tpages><orcidid>https://orcid.org/0000-0003-2995-0424</orcidid><orcidid>https://orcid.org/0000-0002-0377-2271</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Advanced manufacturing technologies Algorithms Business and Management Computer simulation Control Crossovers Engineering Sciences Euclidean geometry Facilities management Genetic algorithms Iterative methods Layouts Machines Manufacturing Manufacturing cells Materials handling Mechatronics Methodology Monte Carlo simulation Operators (mathematics) Parameters Processes Production Robotics Shortest-path problems Solution space Workstations |
title | A methodology for solving facility layout problem considering barriers: genetic algorithm coupled with A search |
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