{"id":"https://openalex.org/W4410204260","doi":"https://doi.org/10.1109/lra.2025.3568309","title":"Deep Reinforcement Learning for Solving Two-Echelon Capacity Vehicle Routing Problem: An End-to-End Method","display_name":"Deep Reinforcement Learning for Solving Two-Echelon Capacity Vehicle Routing Problem: An End-to-End Method","publication_year":2025,"publication_date":"2025-05-08","ids":{"openalex":"https://openalex.org/W4410204260","doi":"https://doi.org/10.1109/lra.2025.3568309"},"language":"en","primary_location":{"id":"doi:10.1109/lra.2025.3568309","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lra.2025.3568309","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Robotics and Automation Letters","raw_type":"journal-article"},"type":"article","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/A5028632483","display_name":"Weice Sun","orcid":"https://orcid.org/0009-0006-9165-2355"},"institutions":[{"id":"https://openalex.org/I55712492","display_name":"Zhejiang University of Technology","ror":"https://ror.org/02djqfd08","country_code":"CN","type":"education","lineage":["https://openalex.org/I55712492"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Weice Sun","raw_affiliation_strings":["College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China","institution_ids":["https://openalex.org/I55712492"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081960668","display_name":"Zhi Pei","orcid":"https://orcid.org/0000-0001-6808-1490"},"institutions":[{"id":"https://openalex.org/I55712492","display_name":"Zhejiang University of Technology","ror":"https://ror.org/02djqfd08","country_code":"CN","type":"education","lineage":["https://openalex.org/I55712492"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhi Pei","raw_affiliation_strings":["College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China","institution_ids":["https://openalex.org/I55712492"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5028632483"],"corresponding_institution_ids":["https://openalex.org/I55712492"],"apc_list":null,"apc_paid":null,"fwci":2.6709,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.89865699,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":"10","issue":"6","first_page":"6432","last_page":"6439"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10567","display_name":"Vehicle Routing Optimization Methods","score":0.9876000285148621,"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":0.9876000285148621,"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/T10551","display_name":"Scheduling and Optimization Algorithms","score":0.9739000201225281,"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/T12782","display_name":"Assembly Line Balancing Optimization","score":0.9711999893188477,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/end-to-end-principle","display_name":"End-to-end principle","score":0.8898690342903137},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.642990231513977},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5491412878036499},{"id":"https://openalex.org/keywords/dead-end","display_name":"Dead end","score":0.4346546232700348},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42028099298477173},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21703967452049255}],"concepts":[{"id":"https://openalex.org/C74296488","wikidata":"https://www.wikidata.org/wiki/Q2527392","display_name":"End-to-end principle","level":2,"score":0.8898690342903137},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.642990231513977},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5491412878036499},{"id":"https://openalex.org/C2986709869","wikidata":"https://www.wikidata.org/wiki/Q398589","display_name":"Dead end","level":3,"score":0.4346546232700348},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42028099298477173},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21703967452049255},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lra.2025.3568309","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lra.2025.3568309","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Robotics and Automation Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3897412543","display_name":null,"funder_award_id":"72271222","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7641860655","display_name":null,"funder_award_id":"W2411062","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G978423042","display_name":null,"funder_award_id":"71871203","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1490008947","https://openalex.org/W1849755676","https://openalex.org/W2058530158","https://openalex.org/W2136284166","https://openalex.org/W2143058845","https://openalex.org/W2145339207","https://openalex.org/W2146004814","https://openalex.org/W2164177447","https://openalex.org/W2165698076","https://openalex.org/W2194775991","https://openalex.org/W2262276395","https://openalex.org/W2529339987","https://openalex.org/W2593437733","https://openalex.org/W2762626604","https://openalex.org/W2791446441","https://openalex.org/W2949867951","https://openalex.org/W3044549082","https://openalex.org/W4213318779","https://openalex.org/W4292289277","https://openalex.org/W4385245566","https://openalex.org/W4391990016","https://openalex.org/W6725207838","https://openalex.org/W6754726630"],"related_works":["https://openalex.org/W2151749779","https://openalex.org/W3179968364","https://openalex.org/W3016188207","https://openalex.org/W4400488565","https://openalex.org/W2218833963","https://openalex.org/W2964709658","https://openalex.org/W4240479622","https://openalex.org/W4246369259","https://openalex.org/W1914800454","https://openalex.org/W4237894622"],"abstract_inverted_index":{"Two-echelon":[0],"distribution":[1,11,59,125],"networks":[2],"significantly":[3],"enhance":[4],"the":[5,10,22,34,51,57,61,65,75,79,89,123,128,138,147],"delivery":[6],"speed":[7,140],"and":[8,100,126,146],"reduce":[9],"cost.":[12],"Recent":[13],"years":[14],"have":[15],"seen":[16],"a":[17,95,156],"growing":[18],"trend":[19],"in":[20,46,50],"applying":[21],"reinforcement":[23],"learning":[24],"method":[25,116,163],"to":[26,77,106],"deal":[27],"with":[28,170],"combinatorial":[29],"optimization":[30],"problems":[31],"such":[32],"as":[33,94],"Vehicle":[35,82],"Routing":[36,83],"Problem":[37,84],"(VRP).":[38],"The":[39],"advantage":[40],"of":[41,54,67,143],"Deep":[42],"Reinforcement":[43],"Learning":[44],"(DRL)":[45],"this":[47],"context":[48],"lies":[49],"fast":[52],"solving":[53],"instances":[55,121],"under":[56],"same":[58,124],"via":[60],"trained":[62],"models.":[63],"To":[64],"best":[66],"our":[68,113,162],"knowledge,":[69],"no":[70],"prior":[71],"research":[72],"has":[73],"applied":[74],"DRL":[76,104],"tackle":[78],"Two-Echelon":[80],"Capacitated":[81],"(2E-CVRP).":[85],"This":[86],"paper,":[87],"for":[88],"first":[90],"time,":[91],"models":[92],"2E-CVRP":[93],"Markov":[96],"Decision":[97],"Process":[98],"(MDP)":[99],"proposes":[101],"an":[102],"end-to-end":[103],"approach":[105],"handle":[107],"it.":[108],"Experimental":[109],"results":[110],"show":[111],"that":[112,137,142],"proposed":[114],"DRL-2E-CVRP":[115],"can":[117],"rapidly":[118],"solve":[119],"unseen":[120],"from":[122],"improve":[127],"solution":[129,139,148],"quality":[130],"through":[131],"transfer":[132],"learning.":[133],"It":[134],"is":[135],"observed":[136],"surpasses":[141],"commercial":[144],"solvers,":[145],"accuracy":[149],"matches":[150],"or":[151],"even":[152],"exceeds":[153],"them":[154],"within":[155],"limited":[157],"time":[158],"span.":[159],"In":[160],"addition,":[161],"also":[164],"demonstrates":[165],"strong":[166],"performance":[167],"on":[168],"benchmarks":[169],"unknown":[171],"distributions.":[172]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
