A hybrid genetic algorithm for the vehicle routing problem with simultaneous pickup and delivery

The vehicle routing problem with simultaneous pickup and delivery is an important variation of VRP where customers require simultaneous pickup and delivery service. In this paper, we proposed a hybrid genetic algorithm to solve this problem. In the proposed algorithm, we proposed a pheromone-based c...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Fanggeng Zhao, Dong Mei, Jiangsheng Sun, Weimin Liu
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Benchmark testing Costs Genetic algorithm Genetic algorithms Genetic mutations Heuristic algorithms Mechanical engineering Partitioning algorithms Pheromone-based crossover Pickup and delivery Routing Transportation Vehicle routing Vehicles
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

container_end_page	3933
container_issue
container_start_page	3928
container_title
container_volume
creator	Fanggeng Zhao Dong Mei Jiangsheng Sun Weimin Liu
description	The vehicle routing problem with simultaneous pickup and delivery is an important variation of VRP where customers require simultaneous pickup and delivery service. In this paper, we proposed a hybrid genetic algorithm to solve this problem. In the proposed algorithm, we proposed a pheromone-based crossover operator that utilizes both the local and global information to construct offspring. The local information used in crossover operator includes edge lengths and adjacency relations, while the global information is stored as pheromone trails. To improve the performance of genetic algorithm, a local search procedure is integrated into GA, and acts as the mutation operator. Our hybrid algorithm was tested on benchmark instances, and experimental results are conclusively in favor of our algorithm.
doi_str_mv	10.1109/CCDC.2009.5192035
format	Conference Proceeding
fullrecord	<record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_5192035</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5192035</ieee_id><sourcerecordid>5192035</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-1da624c8ed26f4f9478ee5c8d6c1aa0efe189d80c68c206693afd1fd9b2b3c653</originalsourceid><addsrcrecordid>eNpVkMlOwzAYhM1SiVL6AIiLXyDFe-xjFVapEhc4F8f-3RiyyUlAfXsqUSExlzl8o-8wCF1TsqKUmNuiuCtWjBCzktQwwuUJWppcU8GEYDnj5hTNqRE6M0LkZ_8YY-d_jJsZujxotCGKUHWBlsPwQQ4RkktO5-h9jat9maLHO2hhjA7betelOFYNDl3CYwX4C6roasCpm8bY7nCfurKGBn8fVniIzVSPtoVuGnAf3efUY9t67KGOX5D2V2gWbD3A8tgL9PZw_1o8ZZuXx-divckizeWYUW8VE06DZyqIYESuAaTTXjlqLYEAVBuviVPaMaKU4TZ4GrwpWcmdknyBbn69EQC2fYqNTfvt8Tz-A0UxXgA</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>A hybrid genetic algorithm for the vehicle routing problem with simultaneous pickup and delivery</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Fanggeng Zhao ; Dong Mei ; Jiangsheng Sun ; Weimin Liu</creator><creatorcontrib>Fanggeng Zhao ; Dong Mei ; Jiangsheng Sun ; Weimin Liu</creatorcontrib><description>The vehicle routing problem with simultaneous pickup and delivery is an important variation of VRP where customers require simultaneous pickup and delivery service. In this paper, we proposed a hybrid genetic algorithm to solve this problem. In the proposed algorithm, we proposed a pheromone-based crossover operator that utilizes both the local and global information to construct offspring. The local information used in crossover operator includes edge lengths and adjacency relations, while the global information is stored as pheromone trails. To improve the performance of genetic algorithm, a local search procedure is integrated into GA, and acts as the mutation operator. Our hybrid algorithm was tested on benchmark instances, and experimental results are conclusively in favor of our algorithm.</description><identifier>ISSN: 1948-9439</identifier><identifier>ISBN: 9781424427222</identifier><identifier>ISBN: 1424427223</identifier><identifier>EISSN: 1948-9447</identifier><identifier>EISBN: 9781424427239</identifier><identifier>EISBN: 1424427231</identifier><identifier>DOI: 10.1109/CCDC.2009.5192035</identifier><identifier>LCCN: 2008906016</identifier><language>eng</language><publisher>IEEE</publisher><subject>Benchmark testing ; Costs ; Genetic algorithm ; Genetic algorithms ; Genetic mutations ; Heuristic algorithms ; Mechanical engineering ; Partitioning algorithms ; Pheromone-based crossover ; Pickup and delivery ; Routing ; Transportation ; Vehicle routing ; Vehicles</subject><ispartof>2009 Chinese Control and Decision Conference, 2009, p.3928-3933</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5192035$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,27902,54895</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5192035$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Fanggeng Zhao</creatorcontrib><creatorcontrib>Dong Mei</creatorcontrib><creatorcontrib>Jiangsheng Sun</creatorcontrib><creatorcontrib>Weimin Liu</creatorcontrib><title>A hybrid genetic algorithm for the vehicle routing problem with simultaneous pickup and delivery</title><title>2009 Chinese Control and Decision Conference</title><addtitle>CCDC</addtitle><description>The vehicle routing problem with simultaneous pickup and delivery is an important variation of VRP where customers require simultaneous pickup and delivery service. In this paper, we proposed a hybrid genetic algorithm to solve this problem. In the proposed algorithm, we proposed a pheromone-based crossover operator that utilizes both the local and global information to construct offspring. The local information used in crossover operator includes edge lengths and adjacency relations, while the global information is stored as pheromone trails. To improve the performance of genetic algorithm, a local search procedure is integrated into GA, and acts as the mutation operator. Our hybrid algorithm was tested on benchmark instances, and experimental results are conclusively in favor of our algorithm.</description><subject>Benchmark testing</subject><subject>Costs</subject><subject>Genetic algorithm</subject><subject>Genetic algorithms</subject><subject>Genetic mutations</subject><subject>Heuristic algorithms</subject><subject>Mechanical engineering</subject><subject>Partitioning algorithms</subject><subject>Pheromone-based crossover</subject><subject>Pickup and delivery</subject><subject>Routing</subject><subject>Transportation</subject><subject>Vehicle routing</subject><subject>Vehicles</subject><issn>1948-9439</issn><issn>1948-9447</issn><isbn>9781424427222</isbn><isbn>1424427223</isbn><isbn>9781424427239</isbn><isbn>1424427231</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVkMlOwzAYhM1SiVL6AIiLXyDFe-xjFVapEhc4F8f-3RiyyUlAfXsqUSExlzl8o-8wCF1TsqKUmNuiuCtWjBCzktQwwuUJWppcU8GEYDnj5hTNqRE6M0LkZ_8YY-d_jJsZujxotCGKUHWBlsPwQQ4RkktO5-h9jat9maLHO2hhjA7betelOFYNDl3CYwX4C6roasCpm8bY7nCfurKGBn8fVniIzVSPtoVuGnAf3efUY9t67KGOX5D2V2gWbD3A8tgL9PZw_1o8ZZuXx-divckizeWYUW8VE06DZyqIYESuAaTTXjlqLYEAVBuviVPaMaKU4TZ4GrwpWcmdknyBbn69EQC2fYqNTfvt8Tz-A0UxXgA</recordid><startdate>200906</startdate><enddate>200906</enddate><creator>Fanggeng Zhao</creator><creator>Dong Mei</creator><creator>Jiangsheng Sun</creator><creator>Weimin Liu</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200906</creationdate><title>A hybrid genetic algorithm for the vehicle routing problem with simultaneous pickup and delivery</title><author>Fanggeng Zhao ; Dong Mei ; Jiangsheng Sun ; Weimin Liu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-1da624c8ed26f4f9478ee5c8d6c1aa0efe189d80c68c206693afd1fd9b2b3c653</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Benchmark testing</topic><topic>Costs</topic><topic>Genetic algorithm</topic><topic>Genetic algorithms</topic><topic>Genetic mutations</topic><topic>Heuristic algorithms</topic><topic>Mechanical engineering</topic><topic>Partitioning algorithms</topic><topic>Pheromone-based crossover</topic><topic>Pickup and delivery</topic><topic>Routing</topic><topic>Transportation</topic><topic>Vehicle routing</topic><topic>Vehicles</topic><toplevel>online_resources</toplevel><creatorcontrib>Fanggeng Zhao</creatorcontrib><creatorcontrib>Dong Mei</creatorcontrib><creatorcontrib>Jiangsheng Sun</creatorcontrib><creatorcontrib>Weimin Liu</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Fanggeng Zhao</au><au>Dong Mei</au><au>Jiangsheng Sun</au><au>Weimin Liu</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A hybrid genetic algorithm for the vehicle routing problem with simultaneous pickup and delivery</atitle><btitle>2009 Chinese Control and Decision Conference</btitle><stitle>CCDC</stitle><date>2009-06</date><risdate>2009</risdate><spage>3928</spage><epage>3933</epage><pages>3928-3933</pages><issn>1948-9439</issn><eissn>1948-9447</eissn><isbn>9781424427222</isbn><isbn>1424427223</isbn><eisbn>9781424427239</eisbn><eisbn>1424427231</eisbn><abstract>The vehicle routing problem with simultaneous pickup and delivery is an important variation of VRP where customers require simultaneous pickup and delivery service. In this paper, we proposed a hybrid genetic algorithm to solve this problem. In the proposed algorithm, we proposed a pheromone-based crossover operator that utilizes both the local and global information to construct offspring. The local information used in crossover operator includes edge lengths and adjacency relations, while the global information is stored as pheromone trails. To improve the performance of genetic algorithm, a local search procedure is integrated into GA, and acts as the mutation operator. Our hybrid algorithm was tested on benchmark instances, and experimental results are conclusively in favor of our algorithm.</abstract><pub>IEEE</pub><doi>10.1109/CCDC.2009.5192035</doi><tpages>6</tpages></addata></record>
fulltext	fulltext_linktorsrc
identifier	ISSN: 1948-9439
ispartof	2009 Chinese Control and Decision Conference, 2009, p.3928-3933
issn	1948-9439 1948-9447
language	eng
recordid	cdi_ieee_primary_5192035
source	IEEE Electronic Library (IEL) Conference Proceedings
subjects	Benchmark testing Costs Genetic algorithm Genetic algorithms Genetic mutations Heuristic algorithms Mechanical engineering Partitioning algorithms Pheromone-based crossover Pickup and delivery Routing Transportation Vehicle routing Vehicles
title	A hybrid genetic algorithm for the vehicle routing problem with simultaneous pickup and delivery
url	https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-05T03%3A17%3A06IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=A%20hybrid%20genetic%20algorithm%20for%20the%20vehicle%20routing%20problem%20with%20simultaneous%20pickup%20and%20delivery&rft.btitle=2009%20Chinese%20Control%20and%20Decision%20Conference&rft.au=Fanggeng%20Zhao&rft.date=2009-06&rft.spage=3928&rft.epage=3933&rft.pages=3928-3933&rft.issn=1948-9439&rft.eissn=1948-9447&rft.isbn=9781424427222&rft.isbn_list=1424427223&rft_id=info:doi/10.1109/CCDC.2009.5192035&rft_dat=%3Cieee_6IE%3E5192035%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=9781424427239&rft.eisbn_list=1424427231&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=5192035&rfr_iscdi=true