A hybrid estimation of distribution algorithm for flexible job-shop scheduling problems with process plan flexibility
The flexible job-shop environments have become increasingly significant because of rapid improvements on shop floors such as production technologies, manufacturing processes and systems. Several real manufacturing and service companies have had to use alternative machines or processes for each opera...
Gespeichert in:
Veröffentlicht in: | Applied intelligence (Dordrecht, Netherlands) Netherlands), 2018-10, Vol.48 (10), p.3707-3734 |
---|---|
Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 3734 |
---|---|
container_issue | 10 |
container_start_page | 3707 |
container_title | Applied intelligence (Dordrecht, Netherlands) |
container_volume | 48 |
creator | Pérez-Rodríguez, Ricardo Hernández-Aguirre, Arturo |
description | The flexible job-shop environments have become increasingly significant because of rapid improvements on shop floors such as production technologies, manufacturing processes and systems. Several real manufacturing and service companies have had to use alternative machines or processes for each operation and the availability of alternative process plans for each job in order to achieve good performance on the shop floor where conflicting objectives are common, e.g. the overall completion time for all jobs and the workload of the most loaded machine. In this paper, we propose a Pareto approach based on the hybridization of an estimation of distribution algorithm and the Mallows distribution in order to build better sequences for flexible job-shop scheduling problems with process plan flexibility and to solve conflicting objectives. This hybrid approach exploits the Pareto-front information used as an input parameter in the Mallows distribution. Various instances and numerical experiments are presented to illustrate that shop floor performance can be noticeably improved using the proposed approach. In addition, statistical tests are executed to validate this novel research. |
doi_str_mv | 10.1007/s10489-018-1160-z |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2098378113</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2098378113</sourcerecordid><originalsourceid>FETCH-LOGICAL-c316t-a459d5c445e0fec7bdc4b41b4e8a0e52d0b7a969d168b10485d335a5201471b43</originalsourceid><addsrcrecordid>eNp1kMtKxDAUhoMoOI4-gLuA6-hJk96Ww-ANBtwouAtJk04zdJqatOj49KZ2wJWrcMj3n8uH0DWFWwqQ3wUKvCgJ0IJQmgH5PkELmuaM5LzMT9ECyoSTLCvfz9FFCDsAYAzoAo0r3ByUtxqbMNi9HKzrsKuxtmHwVo2_tWy3ztuh2ePaeVy35suq1uCdUyQ0rsehaoweW9ttce9d_NoH_Bn5qapMCLhvZXfM2dYOh0t0Vss2mKvju0RvD_ev6yeyeXl8Xq82pGI0G4jkaanTivPUQG2qXOmKK04VN4UEkyYaVC7LrNQ0K9QkINWMpTJNgPI8YmyJbua-cZGPMV4odm70XRwpEigLlheUskjRmaq8C8GbWvQ-qvAHQUFMdsVsV0S7YrIrvmMmmTMhst3W-L_O_4d-AEI4f_c</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2098378113</pqid></control><display><type>article</type><title>A hybrid estimation of distribution algorithm for flexible job-shop scheduling problems with process plan flexibility</title><source>SpringerLink Journals</source><creator>Pérez-Rodríguez, Ricardo ; Hernández-Aguirre, Arturo</creator><creatorcontrib>Pérez-Rodríguez, Ricardo ; Hernández-Aguirre, Arturo</creatorcontrib><description>The flexible job-shop environments have become increasingly significant because of rapid improvements on shop floors such as production technologies, manufacturing processes and systems. Several real manufacturing and service companies have had to use alternative machines or processes for each operation and the availability of alternative process plans for each job in order to achieve good performance on the shop floor where conflicting objectives are common, e.g. the overall completion time for all jobs and the workload of the most loaded machine. In this paper, we propose a Pareto approach based on the hybridization of an estimation of distribution algorithm and the Mallows distribution in order to build better sequences for flexible job-shop scheduling problems with process plan flexibility and to solve conflicting objectives. This hybrid approach exploits the Pareto-front information used as an input parameter in the Mallows distribution. Various instances and numerical experiments are presented to illustrate that shop floor performance can be noticeably improved using the proposed approach. In addition, statistical tests are executed to validate this novel research.</description><identifier>ISSN: 0924-669X</identifier><identifier>EISSN: 1573-7497</identifier><identifier>DOI: 10.1007/s10489-018-1160-z</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Artificial Intelligence ; Completion time ; Computer Science ; Flexibility ; Job shops ; Machines ; Manufacturing ; Mechanical Engineering ; Processes ; Production scheduling ; Scheduling ; Sequences ; Statistical tests</subject><ispartof>Applied intelligence (Dordrecht, Netherlands), 2018-10, Vol.48 (10), p.3707-3734</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2018</rights><rights>Applied Intelligence is a copyright of Springer, (2018). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-a459d5c445e0fec7bdc4b41b4e8a0e52d0b7a969d168b10485d335a5201471b43</citedby><cites>FETCH-LOGICAL-c316t-a459d5c445e0fec7bdc4b41b4e8a0e52d0b7a969d168b10485d335a5201471b43</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10489-018-1160-z$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10489-018-1160-z$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Pérez-Rodríguez, Ricardo</creatorcontrib><creatorcontrib>Hernández-Aguirre, Arturo</creatorcontrib><title>A hybrid estimation of distribution algorithm for flexible job-shop scheduling problems with process plan flexibility</title><title>Applied intelligence (Dordrecht, Netherlands)</title><addtitle>Appl Intell</addtitle><description>The flexible job-shop environments have become increasingly significant because of rapid improvements on shop floors such as production technologies, manufacturing processes and systems. Several real manufacturing and service companies have had to use alternative machines or processes for each operation and the availability of alternative process plans for each job in order to achieve good performance on the shop floor where conflicting objectives are common, e.g. the overall completion time for all jobs and the workload of the most loaded machine. In this paper, we propose a Pareto approach based on the hybridization of an estimation of distribution algorithm and the Mallows distribution in order to build better sequences for flexible job-shop scheduling problems with process plan flexibility and to solve conflicting objectives. This hybrid approach exploits the Pareto-front information used as an input parameter in the Mallows distribution. Various instances and numerical experiments are presented to illustrate that shop floor performance can be noticeably improved using the proposed approach. In addition, statistical tests are executed to validate this novel research.</description><subject>Artificial Intelligence</subject><subject>Completion time</subject><subject>Computer Science</subject><subject>Flexibility</subject><subject>Job shops</subject><subject>Machines</subject><subject>Manufacturing</subject><subject>Mechanical Engineering</subject><subject>Processes</subject><subject>Production scheduling</subject><subject>Scheduling</subject><subject>Sequences</subject><subject>Statistical tests</subject><issn>0924-669X</issn><issn>1573-7497</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kMtKxDAUhoMoOI4-gLuA6-hJk96Ww-ANBtwouAtJk04zdJqatOj49KZ2wJWrcMj3n8uH0DWFWwqQ3wUKvCgJ0IJQmgH5PkELmuaM5LzMT9ECyoSTLCvfz9FFCDsAYAzoAo0r3ByUtxqbMNi9HKzrsKuxtmHwVo2_tWy3ztuh2ePaeVy35suq1uCdUyQ0rsehaoweW9ttce9d_NoH_Bn5qapMCLhvZXfM2dYOh0t0Vss2mKvju0RvD_ev6yeyeXl8Xq82pGI0G4jkaanTivPUQG2qXOmKK04VN4UEkyYaVC7LrNQ0K9QkINWMpTJNgPI8YmyJbua-cZGPMV4odm70XRwpEigLlheUskjRmaq8C8GbWvQ-qvAHQUFMdsVsV0S7YrIrvmMmmTMhst3W-L_O_4d-AEI4f_c</recordid><startdate>20181001</startdate><enddate>20181001</enddate><creator>Pérez-Rodríguez, Ricardo</creator><creator>Hernández-Aguirre, Arturo</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SC</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8FL</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K60</scope><scope>K6~</scope><scope>K7-</scope><scope>L.-</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0C</scope><scope>M0N</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PSYQQ</scope><scope>PTHSS</scope><scope>Q9U</scope></search><sort><creationdate>20181001</creationdate><title>A hybrid estimation of distribution algorithm for flexible job-shop scheduling problems with process plan flexibility</title><author>Pérez-Rodríguez, Ricardo ; Hernández-Aguirre, Arturo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-a459d5c445e0fec7bdc4b41b4e8a0e52d0b7a969d168b10485d335a5201471b43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Artificial Intelligence</topic><topic>Completion time</topic><topic>Computer Science</topic><topic>Flexibility</topic><topic>Job shops</topic><topic>Machines</topic><topic>Manufacturing</topic><topic>Mechanical Engineering</topic><topic>Processes</topic><topic>Production scheduling</topic><topic>Scheduling</topic><topic>Sequences</topic><topic>Statistical tests</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pérez-Rodríguez, Ricardo</creatorcontrib><creatorcontrib>Hernández-Aguirre, Arturo</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Computer Science Database</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ABI/INFORM Global</collection><collection>Computing Database</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest One Psychology</collection><collection>Engineering Collection</collection><collection>ProQuest Central Basic</collection><jtitle>Applied intelligence (Dordrecht, Netherlands)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pérez-Rodríguez, Ricardo</au><au>Hernández-Aguirre, Arturo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A hybrid estimation of distribution algorithm for flexible job-shop scheduling problems with process plan flexibility</atitle><jtitle>Applied intelligence (Dordrecht, Netherlands)</jtitle><stitle>Appl Intell</stitle><date>2018-10-01</date><risdate>2018</risdate><volume>48</volume><issue>10</issue><spage>3707</spage><epage>3734</epage><pages>3707-3734</pages><issn>0924-669X</issn><eissn>1573-7497</eissn><abstract>The flexible job-shop environments have become increasingly significant because of rapid improvements on shop floors such as production technologies, manufacturing processes and systems. Several real manufacturing and service companies have had to use alternative machines or processes for each operation and the availability of alternative process plans for each job in order to achieve good performance on the shop floor where conflicting objectives are common, e.g. the overall completion time for all jobs and the workload of the most loaded machine. In this paper, we propose a Pareto approach based on the hybridization of an estimation of distribution algorithm and the Mallows distribution in order to build better sequences for flexible job-shop scheduling problems with process plan flexibility and to solve conflicting objectives. This hybrid approach exploits the Pareto-front information used as an input parameter in the Mallows distribution. Various instances and numerical experiments are presented to illustrate that shop floor performance can be noticeably improved using the proposed approach. In addition, statistical tests are executed to validate this novel research.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10489-018-1160-z</doi><tpages>28</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0924-669X |
ispartof | Applied intelligence (Dordrecht, Netherlands), 2018-10, Vol.48 (10), p.3707-3734 |
issn | 0924-669X 1573-7497 |
language | eng |
recordid | cdi_proquest_journals_2098378113 |
source | SpringerLink Journals |
subjects | Artificial Intelligence Completion time Computer Science Flexibility Job shops Machines Manufacturing Mechanical Engineering Processes Production scheduling Scheduling Sequences Statistical tests |
title | A hybrid estimation of distribution algorithm for flexible job-shop scheduling problems with process plan flexibility |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-03T06%3A39%3A32IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20hybrid%20estimation%20of%20distribution%20algorithm%20for%20flexible%20job-shop%20scheduling%20problems%20with%20process%20plan%20flexibility&rft.jtitle=Applied%20intelligence%20(Dordrecht,%20Netherlands)&rft.au=P%C3%A9rez-Rodr%C3%ADguez,%20Ricardo&rft.date=2018-10-01&rft.volume=48&rft.issue=10&rft.spage=3707&rft.epage=3734&rft.pages=3707-3734&rft.issn=0924-669X&rft.eissn=1573-7497&rft_id=info:doi/10.1007/s10489-018-1160-z&rft_dat=%3Cproquest_cross%3E2098378113%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2098378113&rft_id=info:pmid/&rfr_iscdi=true |