{"id":"https://openalex.org/W4411143998","doi":"https://doi.org/10.1109/tce.2025.3577902","title":"Double Deep Q-Learning Network for Package Pickup and Delivery Route Prediction","display_name":"Double Deep Q-Learning Network for Package Pickup and Delivery Route Prediction","publication_year":2025,"publication_date":"2025-06-09","ids":{"openalex":"https://openalex.org/W4411143998","doi":"https://doi.org/10.1109/tce.2025.3577902"},"language":"en","primary_location":{"id":"doi:10.1109/tce.2025.3577902","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tce.2025.3577902","pdf_url":null,"source":{"id":"https://openalex.org/S126824455","display_name":"IEEE Transactions on Consumer Electronics","issn_l":"0098-3063","issn":["0098-3063","1558-4127"],"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 Transactions on Consumer Electronics","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/A5117932588","display_name":"Metages Molla Gubena","orcid":null},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Metages Molla Gubena","raw_affiliation_strings":["School of Management, Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Management, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066285868","display_name":"Yuli Zhang","orcid":"https://orcid.org/0000-0002-9382-6308"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuli Zhang","raw_affiliation_strings":["School of Management, Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Management, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030506041","display_name":"Zijuan Ma","orcid":"https://orcid.org/0000-0003-2260-1862"},"institutions":[{"id":"https://openalex.org/I4210144436","display_name":"Shanghai Huayi Group (China)","ror":"https://ror.org/044f58834","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210144436"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziqian Ma","raw_affiliation_strings":["Advertising Business Technology Department, Ant Smart Information Technology (Shanghai) Company Ltd., Shanghai, China","Ant Smart Information Technology (Shanghai) Co. Ltd, China"],"affiliations":[{"raw_affiliation_string":"Advertising Business Technology Department, Ant Smart Information Technology (Shanghai) Company Ltd., Shanghai, China","institution_ids":["https://openalex.org/I4210144436"]},{"raw_affiliation_string":"Ant Smart Information Technology (Shanghai) Co. Ltd, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020778225","display_name":"Xiaoyu Ma","orcid":"https://orcid.org/0000-0001-8144-0232"},"institutions":[{"id":"https://openalex.org/I109173049","display_name":"Beijing Foreign Studies University","ror":"https://ror.org/00jdr0662","country_code":"CN","type":"education","lineage":["https://openalex.org/I109173049"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoyu Ma","raw_affiliation_strings":["International Business School, Beijing Foreign Studies University, Beijing, China","International Business School, Beijing Foreign Studies University, Haidian District, Beijing, China"],"affiliations":[{"raw_affiliation_string":"International Business School, Beijing Foreign Studies University, Beijing, China","institution_ids":["https://openalex.org/I109173049"]},{"raw_affiliation_string":"International Business School, Beijing Foreign Studies University, Haidian District, Beijing, China","institution_ids":["https://openalex.org/I109173049"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113856817","display_name":"Yucai Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210148944","display_name":"D\u00e0-Ji\u0101ng Innovations Science and Technology (China)","ror":"https://ror.org/04fmkfb67","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210148944"]},{"id":"https://openalex.org/I4210151703","display_name":"China Coal Technology and Engineering Group Corp (China)","ror":"https://ror.org/045d9gj14","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210151703"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yucai Yang","raw_affiliation_strings":["JD Group’s Dada Business Division, DJI Network Technology (Shanghai) Company, Shanghai, China","DJI Network Technology (Shanghai) Co, Yangpu District, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"JD Group’s Dada Business Division, DJI Network Technology (Shanghai) Company, Shanghai, China","institution_ids":["https://openalex.org/I4210151703"]},{"raw_affiliation_string":"DJI Network Technology (Shanghai) Co, Yangpu District, Shanghai, China","institution_ids":["https://openalex.org/I4210148944"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5117932588"],"corresponding_institution_ids":["https://openalex.org/I125839683"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.21233147,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"71","issue":"3","first_page":"7503","last_page":"7522"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12176","display_name":"Optimization and Packing Problems","score":0.9746999740600586,"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/T12176","display_name":"Optimization and Packing Problems","score":0.9746999740600586,"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.9502999782562256,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/pickup","display_name":"Pickup","score":0.7061344385147095},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5609804391860962},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.362175315618515},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.3344261944293976},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3047183156013489}],"concepts":[{"id":"https://openalex.org/C2776221269","wikidata":"https://www.wikidata.org/wiki/Q572648","display_name":"Pickup","level":3,"score":0.7061344385147095},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5609804391860962},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.362175315618515},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3344261944293976},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3047183156013489},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tce.2025.3577902","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tce.2025.3577902","pdf_url":null,"source":{"id":"https://openalex.org/S126824455","display_name":"IEEE Transactions on Consumer Electronics","issn_l":"0098-3063","issn":["0098-3063","1558-4127"],"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 Transactions on Consumer Electronics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4791093690","display_name":null,"funder_award_id":"2022ZD0115403","funder_id":"https://openalex.org/F4320329860","funder_display_name":"National Science and Technology Major Project"},{"id":"https://openalex.org/G6035118567","display_name":null,"funder_award_id":"72271029","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G764779399","display_name":null,"funder_award_id":"72321002","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8241859633","display_name":null,"funder_award_id":"72322021","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"},{"id":"https://openalex.org/F4320329860","display_name":"National Science and Technology Major Project","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W1547192778","https://openalex.org/W1968594877","https://openalex.org/W2029681015","https://openalex.org/W2039766013","https://openalex.org/W2050817363","https://openalex.org/W2063659595","https://openalex.org/W2144931885","https://openalex.org/W2145339207","https://openalex.org/W2151131571","https://openalex.org/W2194775991","https://openalex.org/W2224737250","https://openalex.org/W2339713317","https://openalex.org/W2566079294","https://openalex.org/W2746553466","https://openalex.org/W2766447205","https://openalex.org/W2975802312","https://openalex.org/W2991015479","https://openalex.org/W3008607946","https://openalex.org/W3091755028","https://openalex.org/W3168794627","https://openalex.org/W3174924523","https://openalex.org/W3176724216","https://openalex.org/W3195359402","https://openalex.org/W3215277647","https://openalex.org/W3216229816","https://openalex.org/W4206035315","https://openalex.org/W4206834333","https://openalex.org/W4220706539","https://openalex.org/W4220747123","https://openalex.org/W4281744394","https://openalex.org/W4285239096","https://openalex.org/W4285239975","https://openalex.org/W4285741773","https://openalex.org/W4290927838","https://openalex.org/W4368232627","https://openalex.org/W4377712050","https://openalex.org/W4385245566","https://openalex.org/W4385567562","https://openalex.org/W4385568314","https://openalex.org/W4386038421","https://openalex.org/W4386578417","https://openalex.org/W4387871049","https://openalex.org/W4387907632","https://openalex.org/W4389880252","https://openalex.org/W4390956158","https://openalex.org/W4392207709","https://openalex.org/W4392667076","https://openalex.org/W4399571974","https://openalex.org/W4402297942","https://openalex.org/W4403728066"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2185313546","https://openalex.org/W2185850456","https://openalex.org/W2994962043","https://openalex.org/W2351571732","https://openalex.org/W1496169957","https://openalex.org/W2324305961","https://openalex.org/W1971021931"],"abstract_inverted_index":{"Package":[0],"pickup":[1,74],"and":[2,24,47,55,75,108,134,144,184,201,220],"delivery":[3,17,76],"services":[4],"are":[5],"essential":[6],"for":[7,72,105,130,138],"real-time":[8],"logistics,":[9],"where":[10],"riders":[11],"must":[12],"navigate":[13],"operational":[14,216],"constraints":[15],"(e.g.,":[16],"deadlines,":[18],"pickup-delivery":[19],"precedence),":[20],"personalized":[21],"routing":[22,120],"preferences,":[23,219],"dynamic":[25,236],"demand":[26],"fluctuations.":[27],"Existing":[28],"approaches":[29],"exhibit":[30],"critical":[31],"limitations:":[32],"combinatorial":[33],"optimization":[34],"(CO)":[35],"often":[36],"ignores":[37],"rider":[38,218],"preferences;":[39],"deep":[40,48,69,82],"learning":[41,50],"(DL)":[42],"struggles":[43],"to":[44,117,154,197,229],"enforce":[45],"constraints;":[46],"reinforcement":[49],"(DRL)":[51],"faces":[52],"sparse":[53],"rewards":[54],"training":[56,203],"instability.":[57],"To":[58],"address":[59],"these":[60],"challenges,":[61],"we":[62],"propose":[63],"a":[64,98,171],"novel":[65,91],"hybrid":[66],"framework\u2014the":[67],"double":[68,81],"Q-learning":[70],"network":[71,96,192],"package":[73],"route":[77,132,233],"prediction":[78,234],"(DDQN-PPDRP)\u2014that":[79],"integrates":[80],"Q-networks":[83],"with":[84,102,114,151,226],"constraint-aware":[85],"optimization.":[86],"Our":[87],"method":[88],"introduces":[89],"three":[90],"components:":[92],"(1)":[93],"an":[94,109],"ensemble":[95],"combining":[97],"traveling":[99],"salesman":[100],"model":[101,118],"time":[103,204],"windows":[104],"constraint":[106],"satisfaction":[107],"enhanced":[110],"DeepRoute":[111],"transformer":[112],"(i-DeepRoute)":[113],"precedence-aware":[115],"masking":[116],"rider-specific":[119],"behavior;":[121],"(2)":[122],"dual-reward":[123],"functions":[124],"incorporating":[125],"complete":[126],"match":[127],"rate":[128],"(CMR)":[129],"strict":[131],"alignment":[133],"edit":[135],"distance":[136],"(ED)":[137],"incremental":[139],"feedback,":[140],"alleviating":[141],"reward":[142],"sparsity;":[143],"(3)":[145],"adaptive":[146],"prioritized":[147],"experience":[148],"replay":[149],"(APER)":[150],"double-buffer":[152],"strategy":[153],"stabilize":[155],"training.":[156],"Evaluated":[157],"on":[158],"real-world":[159],"data":[160],"from":[161],"Dada":[162],"Express,":[163],"DDQN-PPDRP":[164,213],"achieves":[165],"significant":[166],"improvements":[167],"over":[168],"state-of-the-art":[169],"baselines:":[170],"29.14%":[172],"reduction":[173],"in":[174,235],"location":[175],"square":[176],"deviation,":[177],"48.33%":[178],"lower":[179],"ED,":[180],"19.20%":[181],"higher":[182],"CMR,":[183],"6.67%":[185],"improved":[186],"rank":[187],"correlation.":[188],"It":[189],"also":[190],"reduces":[191],"parameters":[193],"by":[194,205],"85%":[195],"compared":[196],"pure":[198],"DL":[199],"methods":[200],"cuts":[202],"57.7%":[206],"through":[207],"APER.":[208],"These":[209],"results":[210],"demonstrate":[211],"that":[212],"effectively":[214],"balances":[215],"constraints,":[217],"computational":[221],"efficiency,":[222],"harmonizing":[223],"CO\u2019s":[224],"interpretability":[225],"DRL\u2019s":[227],"adaptability":[228],"advance":[230],"scalable,":[231],"constraint-compliant":[232],"logistics":[237],"environments.":[238]},"counts_by_year":[],"updated_date":"2025-11-12T23:11:45.498971","created_date":"2025-10-10T00:00:00"}