Integrated production and delivery with single machine and multiple vehicles
•We consider multi-objective scheduling with a single machine and multiple vehicles.•The goal is to minimize vehicle delivery and total customer waiting time.•We propose a PD-NSGA-II algorithm for this NP-hard problem.•The performance of the algorithm is tested through random data.•It is shown that...
Gespeichert in:
Veröffentlicht in: | Expert systems with applications 2016-09, Vol.57, p.12-20 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 20 |
---|---|
container_issue | |
container_start_page | 12 |
container_title | Expert systems with applications |
container_volume | 57 |
creator | Li, Kai Zhou, Chao Leung, Joseph Y-T. Ma, Ying |
description | •We consider multi-objective scheduling with a single machine and multiple vehicles.•The goal is to minimize vehicle delivery and total customer waiting time.•We propose a PD-NSGA-II algorithm for this NP-hard problem.•The performance of the algorithm is tested through random data.•It is shown that the algorithm can offer high-quality solutions in reasonable time.
This paper considers a class of multi-objective production–distribution scheduling problem with a single machine and multiple vehicles. The objective is to minimize the vehicle delivery cost and the total customer waiting time. It is assumed that the manufacturer’s production department has a single machine to process orders. The distribution department has multiple vehicles to deliver multiple orders to multiple customers after the orders have been processed. Since each delivery involves multiple customers, it involves a vehicle routing problem. Most previous research work attempts at tackling this problem focus on single-objective optimization system. This paper builds a multi-objective mathematical model for the problem. Through deep analysis, this paper proposes that for each non-dominated solution in the Pareto solution set, the orders in the same delivery batch are processed contiguously and their processing order is immaterial. Thus we can view the orders in the same delivery batch as a block. The blocks should be processed in ascending order of the values of their average workload. All the analysis results are embedded into a non-dominated genetic algorithm with the elite strategy (PD-NSGA-II). The performance of the algorithm is tested through random data. It is shown that the proposed algorithm can offer high-quality solutions in reasonable time. |
doi_str_mv | 10.1016/j.eswa.2016.02.033 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1825457836</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0957417416300641</els_id><sourcerecordid>1825457836</sourcerecordid><originalsourceid>FETCH-LOGICAL-c381t-92aebb9083a26d339a40811a1bdeb554464d0da824d589cb072cab70c050e4133</originalsourceid><addsrcrecordid>eNp9kE1LAzEQhoMoWKt_wNMevew6-dhNFrxI8aNQ8KLnkE2mbcp-1CRt6b93az17mmF4n2HmIeSeQkGBVo-bAuPBFGzsC2AFcH5BJlRJnley5pdkAnUpc0GluCY3MW4AqASQE7KY9wlXwSR02TYMbmeTH_rM9C5z2Po9hmN28GmdRd-vWsw6Y9e-x99At2uT347DPa69bTHekqulaSPe_dUp-Xp9-Zy954uPt_nseZFbrmjKa2awaWpQ3LDKcV4bAYpSQxuHTVkKUQkHzigmXKlq24Bk1jQSLJSAgnI-JQ_nvePF3zuMSXc-Wmxb0-Owi5oqVopSKl6NUXaO2jDEGHCpt8F3Jhw1BX1Spzf6pE6f1GlgelQ3Qk9nCMcn9h6DjtZjb9H5gDZpN_j_8B-7jnf_</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1825457836</pqid></control><display><type>article</type><title>Integrated production and delivery with single machine and multiple vehicles</title><source>Elsevier ScienceDirect Journals</source><creator>Li, Kai ; Zhou, Chao ; Leung, Joseph Y-T. ; Ma, Ying</creator><creatorcontrib>Li, Kai ; Zhou, Chao ; Leung, Joseph Y-T. ; Ma, Ying</creatorcontrib><description>•We consider multi-objective scheduling with a single machine and multiple vehicles.•The goal is to minimize vehicle delivery and total customer waiting time.•We propose a PD-NSGA-II algorithm for this NP-hard problem.•The performance of the algorithm is tested through random data.•It is shown that the algorithm can offer high-quality solutions in reasonable time.
This paper considers a class of multi-objective production–distribution scheduling problem with a single machine and multiple vehicles. The objective is to minimize the vehicle delivery cost and the total customer waiting time. It is assumed that the manufacturer’s production department has a single machine to process orders. The distribution department has multiple vehicles to deliver multiple orders to multiple customers after the orders have been processed. Since each delivery involves multiple customers, it involves a vehicle routing problem. Most previous research work attempts at tackling this problem focus on single-objective optimization system. This paper builds a multi-objective mathematical model for the problem. Through deep analysis, this paper proposes that for each non-dominated solution in the Pareto solution set, the orders in the same delivery batch are processed contiguously and their processing order is immaterial. Thus we can view the orders in the same delivery batch as a block. The blocks should be processed in ascending order of the values of their average workload. All the analysis results are embedded into a non-dominated genetic algorithm with the elite strategy (PD-NSGA-II). The performance of the algorithm is tested through random data. It is shown that the proposed algorithm can offer high-quality solutions in reasonable time.</description><identifier>ISSN: 0957-4174</identifier><identifier>EISSN: 1873-6793</identifier><identifier>DOI: 10.1016/j.eswa.2016.02.033</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>Algorithms ; Blocking ; Construction ; Delivery scheduling ; Expert systems ; Mathematical models ; Pareto optimization ; PD-NSGA-II algorithm ; Production–distribution scheduling ; Strategy ; Vehicle routing ; Vehicles</subject><ispartof>Expert systems with applications, 2016-09, Vol.57, p.12-20</ispartof><rights>2016 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c381t-92aebb9083a26d339a40811a1bdeb554464d0da824d589cb072cab70c050e4133</citedby><cites>FETCH-LOGICAL-c381t-92aebb9083a26d339a40811a1bdeb554464d0da824d589cb072cab70c050e4133</cites><orcidid>0000-0002-2144-2364</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0957417416300641$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65534</link.rule.ids></links><search><creatorcontrib>Li, Kai</creatorcontrib><creatorcontrib>Zhou, Chao</creatorcontrib><creatorcontrib>Leung, Joseph Y-T.</creatorcontrib><creatorcontrib>Ma, Ying</creatorcontrib><title>Integrated production and delivery with single machine and multiple vehicles</title><title>Expert systems with applications</title><description>•We consider multi-objective scheduling with a single machine and multiple vehicles.•The goal is to minimize vehicle delivery and total customer waiting time.•We propose a PD-NSGA-II algorithm for this NP-hard problem.•The performance of the algorithm is tested through random data.•It is shown that the algorithm can offer high-quality solutions in reasonable time.
This paper considers a class of multi-objective production–distribution scheduling problem with a single machine and multiple vehicles. The objective is to minimize the vehicle delivery cost and the total customer waiting time. It is assumed that the manufacturer’s production department has a single machine to process orders. The distribution department has multiple vehicles to deliver multiple orders to multiple customers after the orders have been processed. Since each delivery involves multiple customers, it involves a vehicle routing problem. Most previous research work attempts at tackling this problem focus on single-objective optimization system. This paper builds a multi-objective mathematical model for the problem. Through deep analysis, this paper proposes that for each non-dominated solution in the Pareto solution set, the orders in the same delivery batch are processed contiguously and their processing order is immaterial. Thus we can view the orders in the same delivery batch as a block. The blocks should be processed in ascending order of the values of their average workload. All the analysis results are embedded into a non-dominated genetic algorithm with the elite strategy (PD-NSGA-II). The performance of the algorithm is tested through random data. It is shown that the proposed algorithm can offer high-quality solutions in reasonable time.</description><subject>Algorithms</subject><subject>Blocking</subject><subject>Construction</subject><subject>Delivery scheduling</subject><subject>Expert systems</subject><subject>Mathematical models</subject><subject>Pareto optimization</subject><subject>PD-NSGA-II algorithm</subject><subject>Production–distribution scheduling</subject><subject>Strategy</subject><subject>Vehicle routing</subject><subject>Vehicles</subject><issn>0957-4174</issn><issn>1873-6793</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LAzEQhoMoWKt_wNMevew6-dhNFrxI8aNQ8KLnkE2mbcp-1CRt6b93az17mmF4n2HmIeSeQkGBVo-bAuPBFGzsC2AFcH5BJlRJnley5pdkAnUpc0GluCY3MW4AqASQE7KY9wlXwSR02TYMbmeTH_rM9C5z2Po9hmN28GmdRd-vWsw6Y9e-x99At2uT347DPa69bTHekqulaSPe_dUp-Xp9-Zy954uPt_nseZFbrmjKa2awaWpQ3LDKcV4bAYpSQxuHTVkKUQkHzigmXKlq24Bk1jQSLJSAgnI-JQ_nvePF3zuMSXc-Wmxb0-Owi5oqVopSKl6NUXaO2jDEGHCpt8F3Jhw1BX1Spzf6pE6f1GlgelQ3Qk9nCMcn9h6DjtZjb9H5gDZpN_j_8B-7jnf_</recordid><startdate>20160915</startdate><enddate>20160915</enddate><creator>Li, Kai</creator><creator>Zhou, Chao</creator><creator>Leung, Joseph Y-T.</creator><creator>Ma, Ying</creator><general>Elsevier Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-2144-2364</orcidid></search><sort><creationdate>20160915</creationdate><title>Integrated production and delivery with single machine and multiple vehicles</title><author>Li, Kai ; Zhou, Chao ; Leung, Joseph Y-T. ; Ma, Ying</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c381t-92aebb9083a26d339a40811a1bdeb554464d0da824d589cb072cab70c050e4133</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Algorithms</topic><topic>Blocking</topic><topic>Construction</topic><topic>Delivery scheduling</topic><topic>Expert systems</topic><topic>Mathematical models</topic><topic>Pareto optimization</topic><topic>PD-NSGA-II algorithm</topic><topic>Production–distribution scheduling</topic><topic>Strategy</topic><topic>Vehicle routing</topic><topic>Vehicles</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Kai</creatorcontrib><creatorcontrib>Zhou, Chao</creatorcontrib><creatorcontrib>Leung, Joseph Y-T.</creatorcontrib><creatorcontrib>Ma, Ying</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science 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><jtitle>Expert systems with applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Kai</au><au>Zhou, Chao</au><au>Leung, Joseph Y-T.</au><au>Ma, Ying</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Integrated production and delivery with single machine and multiple vehicles</atitle><jtitle>Expert systems with applications</jtitle><date>2016-09-15</date><risdate>2016</risdate><volume>57</volume><spage>12</spage><epage>20</epage><pages>12-20</pages><issn>0957-4174</issn><eissn>1873-6793</eissn><abstract>•We consider multi-objective scheduling with a single machine and multiple vehicles.•The goal is to minimize vehicle delivery and total customer waiting time.•We propose a PD-NSGA-II algorithm for this NP-hard problem.•The performance of the algorithm is tested through random data.•It is shown that the algorithm can offer high-quality solutions in reasonable time.
This paper considers a class of multi-objective production–distribution scheduling problem with a single machine and multiple vehicles. The objective is to minimize the vehicle delivery cost and the total customer waiting time. It is assumed that the manufacturer’s production department has a single machine to process orders. The distribution department has multiple vehicles to deliver multiple orders to multiple customers after the orders have been processed. Since each delivery involves multiple customers, it involves a vehicle routing problem. Most previous research work attempts at tackling this problem focus on single-objective optimization system. This paper builds a multi-objective mathematical model for the problem. Through deep analysis, this paper proposes that for each non-dominated solution in the Pareto solution set, the orders in the same delivery batch are processed contiguously and their processing order is immaterial. Thus we can view the orders in the same delivery batch as a block. The blocks should be processed in ascending order of the values of their average workload. All the analysis results are embedded into a non-dominated genetic algorithm with the elite strategy (PD-NSGA-II). The performance of the algorithm is tested through random data. It is shown that the proposed algorithm can offer high-quality solutions in reasonable time.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.eswa.2016.02.033</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-2144-2364</orcidid></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0957-4174 |
ispartof | Expert systems with applications, 2016-09, Vol.57, p.12-20 |
issn | 0957-4174 1873-6793 |
language | eng |
recordid | cdi_proquest_miscellaneous_1825457836 |
source | Elsevier ScienceDirect Journals |
subjects | Algorithms Blocking Construction Delivery scheduling Expert systems Mathematical models Pareto optimization PD-NSGA-II algorithm Production–distribution scheduling Strategy Vehicle routing Vehicles |
title | Integrated production and delivery with single machine and multiple vehicles |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-16T14%3A08%3A45IST&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=Integrated%20production%20and%20delivery%20with%20single%20machine%20and%20multiple%20vehicles&rft.jtitle=Expert%20systems%20with%20applications&rft.au=Li,%20Kai&rft.date=2016-09-15&rft.volume=57&rft.spage=12&rft.epage=20&rft.pages=12-20&rft.issn=0957-4174&rft.eissn=1873-6793&rft_id=info:doi/10.1016/j.eswa.2016.02.033&rft_dat=%3Cproquest_cross%3E1825457836%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=1825457836&rft_id=info:pmid/&rft_els_id=S0957417416300641&rfr_iscdi=true |