{"id":"https://openalex.org/W4404612065","doi":"https://doi.org/10.1145/3678717.3691208","title":"EFECTIW-ROTER: Deep Reinforcement Learning Approach for Solving Heterogeneous Fleet and Demand Vehicle Routing Problem With Time-Window Constraints","display_name":"EFECTIW-ROTER: Deep Reinforcement Learning Approach for Solving Heterogeneous Fleet and Demand Vehicle Routing Problem With Time-Window Constraints","publication_year":2024,"publication_date":"2024-10-29","ids":{"openalex":"https://openalex.org/W4404612065","doi":"https://doi.org/10.1145/3678717.3691208"},"language":"en","primary_location":{"id":"doi:10.1145/3678717.3691208","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3678717.3691208","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5016453087","display_name":"Arash Mozhdehi","orcid":"https://orcid.org/0000-0002-9938-3560"},"institutions":[{"id":"https://openalex.org/I168635309","display_name":"University of Calgary","ror":"https://ror.org/03yjb2x39","country_code":"CA","type":"education","lineage":["https://openalex.org/I168635309"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Arash Mozhdehi","raw_affiliation_strings":["University of Calgary, Calgary, Alberta, Canada"],"raw_orcid":"https://orcid.org/0000-0002-9938-3560","affiliations":[{"raw_affiliation_string":"University of Calgary, Calgary, Alberta, Canada","institution_ids":["https://openalex.org/I168635309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102011706","display_name":"Mahdi Mohammadizadeh","orcid":"https://orcid.org/0009-0003-7718-4602"},"institutions":[{"id":"https://openalex.org/I168635309","display_name":"University of Calgary","ror":"https://ror.org/03yjb2x39","country_code":"CA","type":"education","lineage":["https://openalex.org/I168635309"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Mahdi Mohammadizadeh","raw_affiliation_strings":["University of Calgary, Calgary, Alberta, Canada"],"raw_orcid":"https://orcid.org/0009-0003-7718-4602","affiliations":[{"raw_affiliation_string":"University of Calgary, Calgary, Alberta, Canada","institution_ids":["https://openalex.org/I168635309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101549955","display_name":"Yunli Wang","orcid":"https://orcid.org/0000-0002-2320-954X"},"institutions":[{"id":"https://openalex.org/I4210159778","display_name":"National Research Council Canada","ror":"https://ror.org/04mte1k06","country_code":"CA","type":"government","lineage":["https://openalex.org/I4210159778"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Yunli Wang","raw_affiliation_strings":["National Research Council, Ottawa, Ontario, Canada"],"raw_orcid":"https://orcid.org/0000-0002-2320-954X","affiliations":[{"raw_affiliation_string":"National Research Council, Ottawa, Ontario, Canada","institution_ids":["https://openalex.org/I4210159778"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092688181","display_name":"Sun Sun","orcid":"https://orcid.org/0000-0001-7870-9448"},"institutions":[{"id":"https://openalex.org/I4210159778","display_name":"National Research Council Canada","ror":"https://ror.org/04mte1k06","country_code":"CA","type":"government","lineage":["https://openalex.org/I4210159778"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Sun Sun","raw_affiliation_strings":["National Research Council, Waterloo, Ontario, Canada"],"raw_orcid":"https://orcid.org/0000-0001-7870-9448","affiliations":[{"raw_affiliation_string":"National Research Council, Waterloo, Ontario, Canada","institution_ids":["https://openalex.org/I4210159778"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020399292","display_name":"Xin Wang","orcid":"https://orcid.org/0000-0003-3569-2126"},"institutions":[{"id":"https://openalex.org/I168635309","display_name":"University of Calgary","ror":"https://ror.org/03yjb2x39","country_code":"CA","type":"education","lineage":["https://openalex.org/I168635309"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Xin Wang","raw_affiliation_strings":["University of Calgary, Calgary, Alberta, Canada"],"raw_orcid":"https://orcid.org/0000-0003-3569-2126","affiliations":[{"raw_affiliation_string":"University of Calgary, Calgary, Alberta, Canada","institution_ids":["https://openalex.org/I168635309"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"17","last_page":"28"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10567","display_name":"Vehicle Routing Optimization Methods","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10567","display_name":"Vehicle Routing Optimization Methods","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12546","display_name":"Smart Parking Systems Research","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11942","display_name":"Transportation and Mobility Innovations","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7689250707626343},{"id":"https://openalex.org/keywords/vehicle-routing-problem","display_name":"Vehicle routing problem","score":0.6860628128051758},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6776329874992371},{"id":"https://openalex.org/keywords/window","display_name":"Window (computing)","score":0.5077472925186157},{"id":"https://openalex.org/keywords/routing","display_name":"Routing (electronic design automation)","score":0.4539378583431244},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40105608105659485},{"id":"https://openalex.org/keywords/operations-research","display_name":"Operations research","score":0.3850449323654175},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3339194655418396},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.2817426323890686},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13332095742225647},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.0858912467956543}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7689250707626343},{"id":"https://openalex.org/C123784306","wikidata":"https://www.wikidata.org/wiki/Q944041","display_name":"Vehicle routing problem","level":3,"score":0.6860628128051758},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6776329874992371},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.5077472925186157},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.4539378583431244},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40105608105659485},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.3850449323654175},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3339194655418396},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.2817426323890686},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13332095742225647},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0858912467956543}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3678717.3691208","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3678717.3691208","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},{"id":"pmh:oai:cisti-icist.nrc-cnrc.ca:cistinparc:033ee03a-98e2-4657-8142-a8b6f905be1a","is_oa":false,"landing_page_url":"https://nrc-publications.canada.ca/eng/view/object/?id=033ee03a-98e2-4657-8142-a8b6f905be1a","pdf_url":null,"source":{"id":"https://openalex.org/S7407055245","display_name":"NPARC","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1572084322","https://openalex.org/W2041029897","https://openalex.org/W2084151425","https://openalex.org/W2093017852","https://openalex.org/W2151554678","https://openalex.org/W2553054816","https://openalex.org/W2588346970","https://openalex.org/W2786263039","https://openalex.org/W2900711844","https://openalex.org/W2963084599","https://openalex.org/W2963925437","https://openalex.org/W3080626793","https://openalex.org/W3090234698","https://openalex.org/W3165356636","https://openalex.org/W3168492867","https://openalex.org/W3180008386","https://openalex.org/W3199468096","https://openalex.org/W4283802063","https://openalex.org/W4291270697","https://openalex.org/W4295105988","https://openalex.org/W4308232794","https://openalex.org/W4308759077","https://openalex.org/W4319787125","https://openalex.org/W4376564152","https://openalex.org/W4393325715","https://openalex.org/W6846500256"],"related_works":["https://openalex.org/W4312143452","https://openalex.org/W2018691209","https://openalex.org/W4247364272","https://openalex.org/W3133151256","https://openalex.org/W2157331354","https://openalex.org/W2132920749","https://openalex.org/W2101108574","https://openalex.org/W2626773395","https://openalex.org/W1575940985","https://openalex.org/W2390901981"],"abstract_inverted_index":{"The":[0],"heterogeneous":[1,105,141,204],"fleet":[2],"and":[3,45,61,79,89,207,247],"demand":[4,91,142],"vehicle":[5,98,205],"routing":[6,221],"problem":[7,16],"with":[8,48,143,211],"time-window":[9,87,167,225],"constraints":[10,88],"(HFDVRPTW)":[11],"is":[12,172,216],"a":[13,29,104,183,196],"crucial":[14],"optimization":[15,63],"of":[17,96,111],"significant":[18],"importance":[19],"in":[20,201],"real-world":[21,231],"logistics":[22],"operations.":[23],"In":[24],"this":[25,59],"paper,":[26],"we":[27],"propose":[28],"deep":[30],"reinforcement":[31],"learning":[32],"(DRL)-based":[33],"method,":[34],"termed":[35],"spatial":[36,144],"Edge-Feature":[37],"EnhanCed":[38],"mulTIgraph":[39],"fusion":[40,170],"encoder":[41],"With":[42],"spectral-based":[43],"embedding":[44,52],"hieRarchical":[46],"decOder":[47],"learnable":[49],"TEmpoRal":[50],"positional":[51,159,212],"(EFECTIW-ROTER,":[53],"pronounced":[54],"\"Effective":[55],"Router\"),":[56],"to":[57,70,77,93,161,174,218,262],"tackle":[58],"complex":[60],"practical":[62],"problem.":[64],"EFECTIW-ROTER":[65,194,250],"utilizes":[66],"two":[67,117,120,202,235],"sparse":[68],"graphs":[69],"represent":[71],"node":[72,176,208],"connectivity,":[73],"where":[74],"nodes":[75,113,132],"correspond":[76],"customers":[78],"the":[80,86,94,109,112,128,131,152,155,191,214],"depot.":[81],"This":[82],"sparsity":[83],"results":[84,229],"from":[85,234],"customers'":[90,140],"relative":[92],"list":[95],"acceptable":[97],"attributes":[99],"specified":[100],"for":[101,190],"service":[102],"within":[103],"fleet,":[106],"determined":[107],"by":[108],"reachability":[110],"based":[114,133,147,165,178,223],"on":[115,134,148,166,179,224],"these":[116,135,180],"factors.":[118,136],"Leveraging":[119],"graph":[121],"Transformer":[122],"models,":[123],"EFECTIW-ROTER's":[124,240],"encoding":[125],"module":[126],"captures":[127],"interactions":[129,177],"between":[130,151],"One":[137],"model":[138,171],"encodes":[139],"edge":[145],"features":[146,195],"travel":[149,252],"time":[150],"nodes,":[153],"while":[154,254],"second":[156],"employs":[157],"temporal":[158,163],"embeddings":[160],"capture":[162],"relationships":[164],"ordering.":[168,227],"A":[169],"introduced":[173],"integrate":[175],"graphs.":[181],"Additionally,":[182],"spectral-attention-based":[184],"pooling":[185],"ensures":[186],"effective":[187,220],"state":[188],"representation":[189],"DRL-based":[192,246],"method.":[193],"hierarchical":[197],"attention":[198],"decoder":[199,215],"operating":[200],"stages:":[203],"selection":[206],"selection.":[209],"Enhanced":[210],"embeddings,":[213],"empowered":[217],"make":[219],"decisions":[222],"constraints'":[226],"Experimental":[228],"using":[230],"traffic":[232],"data":[233],"major":[236],"Canadian":[237],"cities":[238],"confirm":[239],"better":[241],"performance":[242],"over":[243],"current":[244],"state-of-the-art":[245],"heuristic":[248],"methods.":[249],"reduces":[251],"times":[253,259],"also":[255],"achieving":[256],"faster":[257],"computational":[258],"when":[260],"compared":[261],"conventional":[263],"heuristics.":[264],"Additional":[265],"experiments":[266],"demonstrate":[267],"its":[268],"generalizability":[269],"across":[270],"larger":[271],"instances.":[272]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}