Adaptive large neighborhood search heuristics for the vehicle routing problem with stochastic demands and weight-related cost
•Addressed a VRP variant that considers stochastic demands and weight-related cost.•Adopted the a priori optimization solution concept to deal with the problem.•Proposed a novel and flexible recourse strategy.•Implemented three ALNS heuristics to solve the problem.•Generated test instances and bench...
Gespeichert in:
| Veröffentlicht in: | Transportation research. Part E, Logistics and transportation review Logistics and transportation review, 2016-01, Vol.85, p.69-89 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 89 |
|---|---|
| container_issue | |
| container_start_page | 69 |
| container_title | Transportation research. Part E, Logistics and transportation review |
| container_volume | 85 |
| creator | Luo, Zhixing Qin, Hu Zhang, Dezhi Lim, Andrew |
| description | •Addressed a VRP variant that considers stochastic demands and weight-related cost.•Adopted the a priori optimization solution concept to deal with the problem.•Proposed a novel and flexible recourse strategy.•Implemented three ALNS heuristics to solve the problem.•Generated test instances and benchmark results for future researchers of the problem.
The vehicle routing problem (VRP) with stochastic demands and weight-related cost is an extension of the VRP. Although some researchers have studied the VRP with either stochastic demands or weight-related cost, the literature on this problem is quite limited. We adopt the a priori optimization to tackle this problem and propose a dynamic programming to compute the expected cost of each route. We develop the adaptive large neighborhood search heuristics equipped with several approximate methods for the problem. To evaluate our heuristics, we generate 84 test instances. Computational results demonstrate the performance of our heuristics and can serve as benchmarks for future researchers. |
| doi_str_mv | 10.1016/j.tre.2015.11.004 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1786202718</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S1366554515002070</els_id><sourcerecordid>1786202718</sourcerecordid><originalsourceid>FETCH-LOGICAL-c450t-41acf3e050c664a8c76c19ada1777f434c8f3e056dca4522b133557822f6d63b3</originalsourceid><addsrcrecordid>eNp9kT1rHDEQhpdgQ_yRH5BOkCbNriWtvkwqY5zEYHBj10I3mr3Vsbe6SNozLvzfrculSuFGI5jnGWZ4m-Yrox2jTF1tupKw45TJjrGOUvGpOWNGm1bqa3FS_71SrZRCfm7Oc95QWiXJz5q3G-92JeyRTC6tkcwY1uMqpjFGTzK6BCMZcUkhlwCZDDGRMiLZ4xhgQpLiUsK8JrsUVxNuyUsoI8klwugOAvG4dbPPpD7k5TC6tAknV9ATiLlcNqeDmzJ--Vcvmuefd0-3v9uHx1_3tzcPLQhJSyuYg6FHKikoJZwBrYBdO--Y1noQvQDzt608OCE5X7G-l1IbzgflVb_qL5rvx7l1zz8L5mK3IQNOk5sxLtkybRSnXDNT0W__oZu4pLluVylpODWG60qxIwUp5pxwsLsUti69WkbtIRC7sTUQewjEMmZrINX5cXSwXroPmGyGgDOgDwmhWB_DB_Y7DzeVAA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1758208827</pqid></control><display><type>article</type><title>Adaptive large neighborhood search heuristics for the vehicle routing problem with stochastic demands and weight-related cost</title><source>Elsevier ScienceDirect Journals</source><creator>Luo, Zhixing ; Qin, Hu ; Zhang, Dezhi ; Lim, Andrew</creator><creatorcontrib>Luo, Zhixing ; Qin, Hu ; Zhang, Dezhi ; Lim, Andrew</creatorcontrib><description>•Addressed a VRP variant that considers stochastic demands and weight-related cost.•Adopted the a priori optimization solution concept to deal with the problem.•Proposed a novel and flexible recourse strategy.•Implemented three ALNS heuristics to solve the problem.•Generated test instances and benchmark results for future researchers of the problem.
The vehicle routing problem (VRP) with stochastic demands and weight-related cost is an extension of the VRP. Although some researchers have studied the VRP with either stochastic demands or weight-related cost, the literature on this problem is quite limited. We adopt the a priori optimization to tackle this problem and propose a dynamic programming to compute the expected cost of each route. We develop the adaptive large neighborhood search heuristics equipped with several approximate methods for the problem. To evaluate our heuristics, we generate 84 test instances. Computational results demonstrate the performance of our heuristics and can serve as benchmarks for future researchers.</description><identifier>ISSN: 1366-5545</identifier><identifier>EISSN: 1878-5794</identifier><identifier>DOI: 10.1016/j.tre.2015.11.004</identifier><identifier>CODEN: TRERFW</identifier><language>eng</language><publisher>Exeter: Elsevier India Pvt Ltd</publisher><subject>A priori optimization ; Adaptive large neighborhood search ; Approximation ; Automotive components ; Benchmarks ; Demand ; Dynamic programming ; Heuristic ; Route optimization ; Route selection ; Routing ; Searching ; Stochastic demands ; Stochastic models ; Stochasticity ; Studies ; Transportation ; Transportation problem (Operations research) ; Weight-related cost</subject><ispartof>Transportation research. Part E, Logistics and transportation review, 2016-01, Vol.85, p.69-89</ispartof><rights>2015 Elsevier Ltd</rights><rights>Copyright Elsevier Sequoia S.A. Jan 2016</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c450t-41acf3e050c664a8c76c19ada1777f434c8f3e056dca4522b133557822f6d63b3</citedby><cites>FETCH-LOGICAL-c450t-41acf3e050c664a8c76c19ada1777f434c8f3e056dca4522b133557822f6d63b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S1366554515002070$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids></links><search><creatorcontrib>Luo, Zhixing</creatorcontrib><creatorcontrib>Qin, Hu</creatorcontrib><creatorcontrib>Zhang, Dezhi</creatorcontrib><creatorcontrib>Lim, Andrew</creatorcontrib><title>Adaptive large neighborhood search heuristics for the vehicle routing problem with stochastic demands and weight-related cost</title><title>Transportation research. Part E, Logistics and transportation review</title><description>•Addressed a VRP variant that considers stochastic demands and weight-related cost.•Adopted the a priori optimization solution concept to deal with the problem.•Proposed a novel and flexible recourse strategy.•Implemented three ALNS heuristics to solve the problem.•Generated test instances and benchmark results for future researchers of the problem.
The vehicle routing problem (VRP) with stochastic demands and weight-related cost is an extension of the VRP. Although some researchers have studied the VRP with either stochastic demands or weight-related cost, the literature on this problem is quite limited. We adopt the a priori optimization to tackle this problem and propose a dynamic programming to compute the expected cost of each route. We develop the adaptive large neighborhood search heuristics equipped with several approximate methods for the problem. To evaluate our heuristics, we generate 84 test instances. Computational results demonstrate the performance of our heuristics and can serve as benchmarks for future researchers.</description><subject>A priori optimization</subject><subject>Adaptive large neighborhood search</subject><subject>Approximation</subject><subject>Automotive components</subject><subject>Benchmarks</subject><subject>Demand</subject><subject>Dynamic programming</subject><subject>Heuristic</subject><subject>Route optimization</subject><subject>Route selection</subject><subject>Routing</subject><subject>Searching</subject><subject>Stochastic demands</subject><subject>Stochastic models</subject><subject>Stochasticity</subject><subject>Studies</subject><subject>Transportation</subject><subject>Transportation problem (Operations research)</subject><subject>Weight-related cost</subject><issn>1366-5545</issn><issn>1878-5794</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNp9kT1rHDEQhpdgQ_yRH5BOkCbNriWtvkwqY5zEYHBj10I3mr3Vsbe6SNozLvzfrculSuFGI5jnGWZ4m-Yrox2jTF1tupKw45TJjrGOUvGpOWNGm1bqa3FS_71SrZRCfm7Oc95QWiXJz5q3G-92JeyRTC6tkcwY1uMqpjFGTzK6BCMZcUkhlwCZDDGRMiLZ4xhgQpLiUsK8JrsUVxNuyUsoI8klwugOAvG4dbPPpD7k5TC6tAknV9ATiLlcNqeDmzJ--Vcvmuefd0-3v9uHx1_3tzcPLQhJSyuYg6FHKikoJZwBrYBdO--Y1noQvQDzt608OCE5X7G-l1IbzgflVb_qL5rvx7l1zz8L5mK3IQNOk5sxLtkybRSnXDNT0W__oZu4pLluVylpODWG60qxIwUp5pxwsLsUti69WkbtIRC7sTUQewjEMmZrINX5cXSwXroPmGyGgDOgDwmhWB_DB_Y7DzeVAA</recordid><startdate>201601</startdate><enddate>201601</enddate><creator>Luo, Zhixing</creator><creator>Qin, Hu</creator><creator>Zhang, Dezhi</creator><creator>Lim, Andrew</creator><general>Elsevier India Pvt Ltd</general><general>Elsevier Sequoia S.A</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope></search><sort><creationdate>201601</creationdate><title>Adaptive large neighborhood search heuristics for the vehicle routing problem with stochastic demands and weight-related cost</title><author>Luo, Zhixing ; Qin, Hu ; Zhang, Dezhi ; Lim, Andrew</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c450t-41acf3e050c664a8c76c19ada1777f434c8f3e056dca4522b133557822f6d63b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>A priori optimization</topic><topic>Adaptive large neighborhood search</topic><topic>Approximation</topic><topic>Automotive components</topic><topic>Benchmarks</topic><topic>Demand</topic><topic>Dynamic programming</topic><topic>Heuristic</topic><topic>Route optimization</topic><topic>Route selection</topic><topic>Routing</topic><topic>Searching</topic><topic>Stochastic demands</topic><topic>Stochastic models</topic><topic>Stochasticity</topic><topic>Studies</topic><topic>Transportation</topic><topic>Transportation problem (Operations research)</topic><topic>Weight-related cost</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Luo, Zhixing</creatorcontrib><creatorcontrib>Qin, Hu</creatorcontrib><creatorcontrib>Zhang, Dezhi</creatorcontrib><creatorcontrib>Lim, Andrew</creatorcontrib><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><jtitle>Transportation research. Part E, Logistics and transportation review</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Luo, Zhixing</au><au>Qin, Hu</au><au>Zhang, Dezhi</au><au>Lim, Andrew</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Adaptive large neighborhood search heuristics for the vehicle routing problem with stochastic demands and weight-related cost</atitle><jtitle>Transportation research. Part E, Logistics and transportation review</jtitle><date>2016-01</date><risdate>2016</risdate><volume>85</volume><spage>69</spage><epage>89</epage><pages>69-89</pages><issn>1366-5545</issn><eissn>1878-5794</eissn><coden>TRERFW</coden><abstract>•Addressed a VRP variant that considers stochastic demands and weight-related cost.•Adopted the a priori optimization solution concept to deal with the problem.•Proposed a novel and flexible recourse strategy.•Implemented three ALNS heuristics to solve the problem.•Generated test instances and benchmark results for future researchers of the problem.
The vehicle routing problem (VRP) with stochastic demands and weight-related cost is an extension of the VRP. Although some researchers have studied the VRP with either stochastic demands or weight-related cost, the literature on this problem is quite limited. We adopt the a priori optimization to tackle this problem and propose a dynamic programming to compute the expected cost of each route. We develop the adaptive large neighborhood search heuristics equipped with several approximate methods for the problem. To evaluate our heuristics, we generate 84 test instances. Computational results demonstrate the performance of our heuristics and can serve as benchmarks for future researchers.</abstract><cop>Exeter</cop><pub>Elsevier India Pvt Ltd</pub><doi>10.1016/j.tre.2015.11.004</doi><tpages>21</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1366-5545 |
| ispartof | Transportation research. Part E, Logistics and transportation review, 2016-01, Vol.85, p.69-89 |
| issn | 1366-5545 1878-5794 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1786202718 |
| source | Elsevier ScienceDirect Journals |
| subjects | A priori optimization Adaptive large neighborhood search Approximation Automotive components Benchmarks Demand Dynamic programming Heuristic Route optimization Route selection Routing Searching Stochastic demands Stochastic models Stochasticity Studies Transportation Transportation problem (Operations research) Weight-related cost |
| title | Adaptive large neighborhood search heuristics for the vehicle routing problem with stochastic demands and weight-related cost |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-02T03%3A30%3A38IST&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=Adaptive%20large%20neighborhood%20search%20heuristics%20for%20the%20vehicle%20routing%20problem%20with%20stochastic%20demands%20and%20weight-related%20cost&rft.jtitle=Transportation%20research.%20Part%20E,%20Logistics%20and%20transportation%20review&rft.au=Luo,%20Zhixing&rft.date=2016-01&rft.volume=85&rft.spage=69&rft.epage=89&rft.pages=69-89&rft.issn=1366-5545&rft.eissn=1878-5794&rft.coden=TRERFW&rft_id=info:doi/10.1016/j.tre.2015.11.004&rft_dat=%3Cproquest_cross%3E1786202718%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=1758208827&rft_id=info:pmid/&rft_els_id=S1366554515002070&rfr_iscdi=true |