An iterative biased‐randomized heuristic for the fleet size and mix vehicle‐routing problem with backhauls
This paper analyzes the fleet mixed vehicle‐routing problem with backhauls, a rich and realistic variant of the popular vehicle‐routing problem in which both delivery and pick‐up customers are served from a central depot using a heterogeneous and configurable fleet of vehicles. After a literature re...
Gespeichert in:
| Veröffentlicht in: | International transactions in operational research 2019-01, Vol.26 (1), p.289-301 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 301 |
|---|---|
| container_issue | 1 |
| container_start_page | 289 |
| container_title | International transactions in operational research |
| container_volume | 26 |
| creator | Belloso, Javier Juan, Angel A. Faulin, Javier |
| description | This paper analyzes the fleet mixed vehicle‐routing problem with backhauls, a rich and realistic variant of the popular vehicle‐routing problem in which both delivery and pick‐up customers are served from a central depot using a heterogeneous and configurable fleet of vehicles. After a literature review on the issue and a detailed description of the problem, a solution based on a multistart biased‐randomized heuristic is proposed. Our algorithm uses an iterative method that relies on solving a series of smaller instances of the homogeneous‐fleet version of the problem and then using these subsolutions as partial solutions for the original heterogeneous instance. In order to better guide the exploration of the solutions space, the algorithm employs several biased‐randomized processes: a first one for selecting a vehicle type; a second one for sorting the savings list; and a third one to define the number of routes that must be selected from the homogenous‐fleet subsolution. The computational experiments show that our approach is competitive and able to provide 20 new best‐known solutions for a 36‐instance benchmark recently proposed in the literature. |
| doi_str_mv | 10.1111/itor.12379 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_csuc_</sourceid><recordid>TN_cdi_csuc_recercat_oai_recercat_cat_2072_350063</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2110287024</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3439-5adc4987307b9207c4d97e115e86ac0b660cd80152cafde9ff25ea4fc0986b103</originalsourceid><addsrcrecordid>eNp9UdtKAzEQDaJgrb74BQHfhK2TveexFC-FQkH0OWSzs27qdrcm2Wp98hP8Rr_E1Bb65sAwDHPO4QyHkEsGI-brRrvOjFgYZfyIDFicJUHEeXJMBsBTHqTA0lNyZu0CAFjCsgFpxy3VDo10eo200NJi-fP1bWRbdkv9iSWtsTfaOq1o1RnqaqRVg-io9VfqYXSpP-gaa60a3DK73un2ha5MVzS4pO_a1bSQ6rWWfWPPyUklG4sX-zkkz3e3T5OHYDa_n07Gs0BFccSDRJYq5nkWQVbwEDIVlzxDxhLMU6mgSFNQZe5fCJWsSuRVFSYo40oBz9OCQTQkbKerbK-EQYVGSSc6qQ_Ltr12KKIEII0852rH8dbferROLLretN6mCBmDMM8gjD3qeq9sOmsNVmJl9FKajWAgthmIbQbiL4ODjXfd4OYfpJg-zR93nF_GDoz4</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2110287024</pqid></control><display><type>article</type><title>An iterative biased‐randomized heuristic for the fleet size and mix vehicle‐routing problem with backhauls</title><source>Wiley Online Library Journals Frontfile Complete</source><source>EBSCOhost Business Source Complete</source><source>Recercat</source><creator>Belloso, Javier ; Juan, Angel A. ; Faulin, Javier</creator><creatorcontrib>Belloso, Javier ; Juan, Angel A. ; Faulin, Javier</creatorcontrib><description>This paper analyzes the fleet mixed vehicle‐routing problem with backhauls, a rich and realistic variant of the popular vehicle‐routing problem in which both delivery and pick‐up customers are served from a central depot using a heterogeneous and configurable fleet of vehicles. After a literature review on the issue and a detailed description of the problem, a solution based on a multistart biased‐randomized heuristic is proposed. Our algorithm uses an iterative method that relies on solving a series of smaller instances of the homogeneous‐fleet version of the problem and then using these subsolutions as partial solutions for the original heterogeneous instance. In order to better guide the exploration of the solutions space, the algorithm employs several biased‐randomized processes: a first one for selecting a vehicle type; a second one for sorting the savings list; and a third one to define the number of routes that must be selected from the homogenous‐fleet subsolution. The computational experiments show that our approach is competitive and able to provide 20 new best‐known solutions for a 36‐instance benchmark recently proposed in the literature.</description><identifier>ISSN: 0969-6016</identifier><identifier>EISSN: 1475-3995</identifier><identifier>DOI: 10.1111/itor.12379</identifier><language>eng</language><publisher>Oxford: Blackwell Publishing Ltd</publisher><subject>aleatorització esbiaixada ; aleatorización sesgada ; Algorismes ; algorismes multistart ; Algorithms ; Algoritmos ; algoritmos multistart ; biased randomization ; fleet size and mix vehicle-routing problem ; heuristics ; heurística ; Iterative methods ; Literature reviews ; multistart algorithms ; Operations research ; Randomization ; resolució de problemes reals de flotes de vehicles capacitat ; resolución de problemas reales de flotas de vehículos capacitado ; Route planning ; Routing ; ruta para vehículos ; ruta per a vehicles ; Vehicle routing ; vehicle-routing problem with backhauls</subject><ispartof>International transactions in operational research, 2019-01, Vol.26 (1), p.289-301</ispartof><rights>2017 The Authors. International Transactions in Operational Research © 2017 International Federation of Operational Research Societies</rights><rights>2019 The Authors.</rights><rights>cc-by-nc-nd info:eu-repo/semantics/openAccess <a href="http://creativecommons.org/licenses/by-nc-nd/3.0/es/">http://creativecommons.org/licenses/by-nc-nd/3.0/es/</a> <a href="http://creativecommons.org/licenses/by-nc-nd/3.0/es/">http://creativecommons.org/licenses/by-nc-nd/3.0/es/</a></rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3439-5adc4987307b9207c4d97e115e86ac0b660cd80152cafde9ff25ea4fc0986b103</citedby><cites>FETCH-LOGICAL-c3439-5adc4987307b9207c4d97e115e86ac0b660cd80152cafde9ff25ea4fc0986b103</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fitor.12379$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fitor.12379$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>230,314,778,782,883,1414,26957,27907,27908,45557,45558</link.rule.ids></links><search><creatorcontrib>Belloso, Javier</creatorcontrib><creatorcontrib>Juan, Angel A.</creatorcontrib><creatorcontrib>Faulin, Javier</creatorcontrib><title>An iterative biased‐randomized heuristic for the fleet size and mix vehicle‐routing problem with backhauls</title><title>International transactions in operational research</title><description>This paper analyzes the fleet mixed vehicle‐routing problem with backhauls, a rich and realistic variant of the popular vehicle‐routing problem in which both delivery and pick‐up customers are served from a central depot using a heterogeneous and configurable fleet of vehicles. After a literature review on the issue and a detailed description of the problem, a solution based on a multistart biased‐randomized heuristic is proposed. Our algorithm uses an iterative method that relies on solving a series of smaller instances of the homogeneous‐fleet version of the problem and then using these subsolutions as partial solutions for the original heterogeneous instance. In order to better guide the exploration of the solutions space, the algorithm employs several biased‐randomized processes: a first one for selecting a vehicle type; a second one for sorting the savings list; and a third one to define the number of routes that must be selected from the homogenous‐fleet subsolution. The computational experiments show that our approach is competitive and able to provide 20 new best‐known solutions for a 36‐instance benchmark recently proposed in the literature.</description><subject>aleatorització esbiaixada</subject><subject>aleatorización sesgada</subject><subject>Algorismes</subject><subject>algorismes multistart</subject><subject>Algorithms</subject><subject>Algoritmos</subject><subject>algoritmos multistart</subject><subject>biased randomization</subject><subject>fleet size and mix vehicle-routing problem</subject><subject>heuristics</subject><subject>heurística</subject><subject>Iterative methods</subject><subject>Literature reviews</subject><subject>multistart algorithms</subject><subject>Operations research</subject><subject>Randomization</subject><subject>resolució de problemes reals de flotes de vehicles capacitat</subject><subject>resolución de problemas reales de flotas de vehículos capacitado</subject><subject>Route planning</subject><subject>Routing</subject><subject>ruta para vehículos</subject><subject>ruta per a vehicles</subject><subject>Vehicle routing</subject><subject>vehicle-routing problem with backhauls</subject><issn>0969-6016</issn><issn>1475-3995</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>XX2</sourceid><recordid>eNp9UdtKAzEQDaJgrb74BQHfhK2TveexFC-FQkH0OWSzs27qdrcm2Wp98hP8Rr_E1Bb65sAwDHPO4QyHkEsGI-brRrvOjFgYZfyIDFicJUHEeXJMBsBTHqTA0lNyZu0CAFjCsgFpxy3VDo10eo200NJi-fP1bWRbdkv9iSWtsTfaOq1o1RnqaqRVg-io9VfqYXSpP-gaa60a3DK73un2ha5MVzS4pO_a1bSQ6rWWfWPPyUklG4sX-zkkz3e3T5OHYDa_n07Gs0BFccSDRJYq5nkWQVbwEDIVlzxDxhLMU6mgSFNQZe5fCJWsSuRVFSYo40oBz9OCQTQkbKerbK-EQYVGSSc6qQ_Ltr12KKIEII0852rH8dbferROLLretN6mCBmDMM8gjD3qeq9sOmsNVmJl9FKajWAgthmIbQbiL4ODjXfd4OYfpJg-zR93nF_GDoz4</recordid><startdate>201901</startdate><enddate>201901</enddate><creator>Belloso, Javier</creator><creator>Juan, Angel A.</creator><creator>Faulin, Javier</creator><general>Blackwell Publishing Ltd</general><general>International Transactions in Operational Research</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>XX2</scope></search><sort><creationdate>201901</creationdate><title>An iterative biased‐randomized heuristic for the fleet size and mix vehicle‐routing problem with backhauls</title><author>Belloso, Javier ; Juan, Angel A. ; Faulin, Javier</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3439-5adc4987307b9207c4d97e115e86ac0b660cd80152cafde9ff25ea4fc0986b103</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>aleatorització esbiaixada</topic><topic>aleatorización sesgada</topic><topic>Algorismes</topic><topic>algorismes multistart</topic><topic>Algorithms</topic><topic>Algoritmos</topic><topic>algoritmos multistart</topic><topic>biased randomization</topic><topic>fleet size and mix vehicle-routing problem</topic><topic>heuristics</topic><topic>heurística</topic><topic>Iterative methods</topic><topic>Literature reviews</topic><topic>multistart algorithms</topic><topic>Operations research</topic><topic>Randomization</topic><topic>resolució de problemes reals de flotes de vehicles capacitat</topic><topic>resolución de problemas reales de flotas de vehículos capacitado</topic><topic>Route planning</topic><topic>Routing</topic><topic>ruta para vehículos</topic><topic>ruta per a vehicles</topic><topic>Vehicle routing</topic><topic>vehicle-routing problem with backhauls</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Belloso, Javier</creatorcontrib><creatorcontrib>Juan, Angel A.</creatorcontrib><creatorcontrib>Faulin, Javier</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering 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><collection>Recercat</collection><jtitle>International transactions in operational research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Belloso, Javier</au><au>Juan, Angel A.</au><au>Faulin, Javier</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An iterative biased‐randomized heuristic for the fleet size and mix vehicle‐routing problem with backhauls</atitle><jtitle>International transactions in operational research</jtitle><date>2019-01</date><risdate>2019</risdate><volume>26</volume><issue>1</issue><spage>289</spage><epage>301</epage><pages>289-301</pages><issn>0969-6016</issn><eissn>1475-3995</eissn><abstract>This paper analyzes the fleet mixed vehicle‐routing problem with backhauls, a rich and realistic variant of the popular vehicle‐routing problem in which both delivery and pick‐up customers are served from a central depot using a heterogeneous and configurable fleet of vehicles. After a literature review on the issue and a detailed description of the problem, a solution based on a multistart biased‐randomized heuristic is proposed. Our algorithm uses an iterative method that relies on solving a series of smaller instances of the homogeneous‐fleet version of the problem and then using these subsolutions as partial solutions for the original heterogeneous instance. In order to better guide the exploration of the solutions space, the algorithm employs several biased‐randomized processes: a first one for selecting a vehicle type; a second one for sorting the savings list; and a third one to define the number of routes that must be selected from the homogenous‐fleet subsolution. The computational experiments show that our approach is competitive and able to provide 20 new best‐known solutions for a 36‐instance benchmark recently proposed in the literature.</abstract><cop>Oxford</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1111/itor.12379</doi><tpages>13</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0969-6016 |
| ispartof | International transactions in operational research, 2019-01, Vol.26 (1), p.289-301 |
| issn | 0969-6016 1475-3995 |
| language | eng |
| recordid | cdi_csuc_recercat_oai_recercat_cat_2072_350063 |
| source | Wiley Online Library Journals Frontfile Complete; EBSCOhost Business Source Complete; Recercat |
| subjects | aleatorització esbiaixada aleatorización sesgada Algorismes algorismes multistart Algorithms Algoritmos algoritmos multistart biased randomization fleet size and mix vehicle-routing problem heuristics heurística Iterative methods Literature reviews multistart algorithms Operations research Randomization resolució de problemes reals de flotes de vehicles capacitat resolución de problemas reales de flotas de vehículos capacitado Route planning Routing ruta para vehículos ruta per a vehicles Vehicle routing vehicle-routing problem with backhauls |
| title | An iterative biased‐randomized heuristic for the fleet size and mix vehicle‐routing problem with backhauls |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-16T12%3A08%3A57IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_csuc_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=An%20iterative%20biased%E2%80%90randomized%20heuristic%20for%20the%20fleet%20size%20and%20mix%20vehicle%E2%80%90routing%20problem%20with%20backhauls&rft.jtitle=International%20transactions%20in%20operational%20research&rft.au=Belloso,%20Javier&rft.date=2019-01&rft.volume=26&rft.issue=1&rft.spage=289&rft.epage=301&rft.pages=289-301&rft.issn=0969-6016&rft.eissn=1475-3995&rft_id=info:doi/10.1111/itor.12379&rft_dat=%3Cproquest_csuc_%3E2110287024%3C/proquest_csuc_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2110287024&rft_id=info:pmid/&rfr_iscdi=true |