{"id":"https://openalex.org/W7120020836","doi":"https://doi.org/10.48550/arxiv.2601.04705","title":"A zone-based training approach for last-mile routing using Graph Neural Networks and Pointer Networks","display_name":"A zone-based training approach for last-mile routing using Graph Neural Networks and Pointer Networks","publication_year":2026,"publication_date":"2026-01-08","ids":{"openalex":"https://openalex.org/W7120020836","doi":"https://doi.org/10.48550/arxiv.2601.04705"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.04705","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.04705","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2601.04705","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5122690430","display_name":"\u00c0ngel Ruiz-Fas","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ruiz-Fas, \u00c0ngel","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122420863","display_name":"Carlos Granell","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Granell, Carlos","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Ramos, Jos\u00e9 Francisco","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ramos, Jos\u00e9 Francisco","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122626006","display_name":"Joaqu\u00edn Huerta","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huerta, Joaqu\u00edn","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5122547605","display_name":"Sergio Trilles","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Trilles, Sergio","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5122690430"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10567","display_name":"Vehicle Routing Optimization Methods","score":0.41670000553131104,"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":0.41670000553131104,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.14830000698566437,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.045899998396635056,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pointer","display_name":"Pointer (user interface)","score":0.4754999876022339},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.46880000829696655},{"id":"https://openalex.org/keywords/heuristics","display_name":"Heuristics","score":0.45649999380111694},{"id":"https://openalex.org/keywords/traverse","display_name":"Traverse","score":0.4528000056743622},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44920000433921814},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.4339999854564667},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.43160000443458557},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.38510000705718994}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6904000043869019},{"id":"https://openalex.org/C150202949","wikidata":"https://www.wikidata.org/wiki/Q107602","display_name":"Pointer (user interface)","level":2,"score":0.4754999876022339},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.46880000829696655},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.45649999380111694},{"id":"https://openalex.org/C176809094","wikidata":"https://www.wikidata.org/wiki/Q15401496","display_name":"Traverse","level":2,"score":0.4528000056743622},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44920000433921814},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.4339999854564667},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.43160000443458557},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.38510000705718994},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.37950000166893005},{"id":"https://openalex.org/C50558702","wikidata":"https://www.wikidata.org/wiki/Q5535067","display_name":"Geographic routing","level":5,"score":0.3433000147342682},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.3425999879837036},{"id":"https://openalex.org/C117045392","wikidata":"https://www.wikidata.org/wiki/Q4899215","display_name":"Betweenness centrality","level":3,"score":0.33090001344680786},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.32690000534057617},{"id":"https://openalex.org/C184896649","wikidata":"https://www.wikidata.org/wiki/Q290066","display_name":"Routing table","level":4,"score":0.32170000672340393},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31700000166893005},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2858999967575073},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C80899671","wikidata":"https://www.wikidata.org/wiki/Q1304193","display_name":"Vertex (graph theory)","level":3,"score":0.2732999920845032},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26499998569488525},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.26179999113082886},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.26089999079704285},{"id":"https://openalex.org/C3017489831","wikidata":"https://www.wikidata.org/wiki/Q2393193","display_name":"Running time","level":2,"score":0.2522999942302704}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.04705","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.04705","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2601.04705","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.04705","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.43803414702415466}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Rapid":[0],"e-commerce":[1],"growth":[2],"has":[3],"pushed":[4],"last-mile":[5,45,60],"delivery":[6,61],"networks":[7],"to":[8,27,43,50,58,119,128,155,236],"their":[9],"limits,":[10],"where":[11],"small":[12],"routing":[13,46],"gains":[14],"translate":[15],"into":[16],"lower":[17],"costs,":[18],"faster":[19],"service,":[20],"and":[21,83,114,153,168,217],"fewer":[22],"emissions.":[23],"Classical":[24],"heuristics":[25],"struggle":[26],"adapt":[28],"when":[29],"travel":[30,89],"times":[31],"are":[32,81,87,151,170,192,220],"highly":[33],"asymmetric":[34,88],"(e.g.,":[35],"one-way":[36],"streets,":[37],"congestion).":[38],"A":[39,91,106],"deep":[40],"learning-based":[41],"approach":[42,65,198,246],"the":[44,101,112,115,122,133,148,164,177,185,188,202,207,226,232,237,244,251],"problem":[47],"is":[48,66,72,179,199],"presented":[49,64],"generate":[51,156],"geographical":[52,157],"zones":[53,158],"composed":[54],"of":[55,137,159,166,176,187,243,253],"stop":[56],"sequences":[57],"minimize":[59],"times.":[62,90],"The":[63,240],"an":[67],"encoder-decoder":[68],"architecture.":[69],"Each":[70],"route":[71,145,229,256],"represented":[73],"as":[74,132,250],"a":[75,126,138,173,223],"complete":[76],"directed":[77],"graph":[78],"whose":[79,84],"nodes":[80],"stops":[82,146,186,254],"edge":[85],"weights":[86],"Graph":[92],"Neural":[93],"Network":[94,108],"encoder":[95],"produces":[96],"node":[97,118,131],"embeddings":[98,113],"that":[99,195],"captures":[100],"spatial":[102],"relationships":[103],"between":[104],"stops.":[105],"Pointer":[107],"decoder":[109],"then":[110],"takes":[111],"route's":[116],"start":[117],"sequentially":[120],"select":[121],"next":[123,134],"stops,":[124],"assigning":[125],"probability":[127],"each":[129],"unvisited":[130],"destination.":[135],"Cells":[136],"Discrete":[139],"Global":[140],"Grid":[141],"System":[142],"which":[143,163,191],"contain":[144],"in":[147,162,194,225,231],"training":[149,167,189,219,234],"data":[150],"obtained":[152],"clustered":[154],"similar":[160],"size":[161],"process":[165],"inference":[169],"divided.":[171],"Subsequently,":[172],"different":[174],"instance":[175],"model":[178],"trained":[180],"per":[181,255],"zone":[182],"only":[183],"considering":[184],"routes":[190,205],"included":[193],"zone.":[196],"This":[197],"evaluated":[200],"using":[201],"Los":[203],"Angeles":[204],"from":[206,215],"2021":[208],"Amazon":[209],"Last":[210],"Mile":[211],"Routing":[212],"Challenge.":[213],"Results":[214],"general":[216,238],"zone-based":[218,233,245],"compared,":[221],"showing":[222],"reduction":[224],"average":[227],"predicted":[228],"length":[230],"compared":[235],"training.":[239],"performance":[241],"improvement":[242],"becomes":[247],"more":[248],"pronounced":[249],"number":[252],"increases.":[257]},"counts_by_year":[],"updated_date":"2026-04-23T09:07:50.710637","created_date":"2026-01-10T00:00:00"}
