{"id":"https://openalex.org/W3145389849","doi":"https://doi.org/10.1109/wsc48552.2020.9383942","title":"Enhancing Input Parameter Estimation by Machine Learning for the Simulation of Large-Scale Logistics Networks","display_name":"Enhancing Input Parameter Estimation by Machine Learning for the Simulation of Large-Scale Logistics Networks","publication_year":2020,"publication_date":"2020-12-14","ids":{"openalex":"https://openalex.org/W3145389849","doi":"https://doi.org/10.1109/wsc48552.2020.9383942","mag":"3145389849"},"language":"en","primary_location":{"id":"doi:10.1109/wsc48552.2020.9383942","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wsc48552.2020.9383942","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 Winter Simulation Conference (WSC)","raw_type":"proceedings-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/A5100355962","display_name":"Yang Liu","orcid":"https://orcid.org/0000-0002-6825-1936"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yang Liu","raw_affiliation_strings":["JD Logistics, Beijing, CHINA"],"affiliations":[{"raw_affiliation_string":"JD Logistics, Beijing, CHINA","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Liang Yan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Yan","raw_affiliation_strings":["JD Logistics, Beijing, CHINA"],"affiliations":[{"raw_affiliation_string":"JD Logistics, Beijing, CHINA","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100320010","display_name":"Sheng Liu","orcid":"https://orcid.org/0000-0003-2731-9591"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sheng Liu","raw_affiliation_strings":["JD Logistics, Beijing, CHINA"],"affiliations":[{"raw_affiliation_string":"JD Logistics, Beijing, CHINA","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014692257","display_name":"Ting Jiang","orcid":"https://orcid.org/0000-0001-5873-3627"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ting Jiang","raw_affiliation_strings":["JD Logistics, Beijing, CHINA"],"affiliations":[{"raw_affiliation_string":"JD Logistics, Beijing, CHINA","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100401336","display_name":"Feng Zhang","orcid":"https://orcid.org/0009-0002-7472-4901"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Zhang","raw_affiliation_strings":["JD Logistics, Beijing, CHINA"],"affiliations":[{"raw_affiliation_string":"JD Logistics, Beijing, CHINA","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100445144","display_name":"Yu Wang","orcid":"https://orcid.org/0000-0002-0431-1039"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Wang","raw_affiliation_strings":["JD Logistics, Beijing, CHINA"],"affiliations":[{"raw_affiliation_string":"JD Logistics, Beijing, CHINA","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056217038","display_name":"Shengnan Wu","orcid":"https://orcid.org/0000-0001-9193-6071"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shengnan Wu","raw_affiliation_strings":["JD Logistics, Beijing, CHINA"],"affiliations":[{"raw_affiliation_string":"JD Logistics, Beijing, CHINA","institution_ids":["https://openalex.org/I4210103986"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5100355962"],"corresponding_institution_ids":["https://openalex.org/I4210103986"],"apc_list":null,"apc_paid":null,"fwci":0.9288,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.79430289,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"608","last_page":"619"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11195","display_name":"Simulation Techniques and Applications","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11195","display_name":"Simulation Techniques and Applications","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T14420","display_name":"Advanced Research in Systems and Signal Processing","score":0.9603999853134155,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T10524","display_name":"Traffic control and management","score":0.9555000066757202,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/computer-science","display_name":"Computer science","score":0.804503321647644},{"id":"https://openalex.org/keywords/obstacle","display_name":"Obstacle","score":0.5964545011520386},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.5290383696556091},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5261083245277405},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5012164115905762},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4946717917919159},{"id":"https://openalex.org/keywords/sorting","display_name":"Sorting","score":0.48596394062042236},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.470523476600647},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.420107901096344},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.41657713055610657},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38455456495285034},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.1507648229598999},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11668014526367188}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.804503321647644},{"id":"https://openalex.org/C2776650193","wikidata":"https://www.wikidata.org/wiki/Q264661","display_name":"Obstacle","level":2,"score":0.5964545011520386},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.5290383696556091},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5261083245277405},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5012164115905762},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4946717917919159},{"id":"https://openalex.org/C111696304","wikidata":"https://www.wikidata.org/wiki/Q2303697","display_name":"Sorting","level":2,"score":0.48596394062042236},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.470523476600647},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.420107901096344},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.41657713055610657},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38455456495285034},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.1507648229598999},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11668014526367188},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wsc48552.2020.9383942","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wsc48552.2020.9383942","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 Winter Simulation Conference (WSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W569532246","https://openalex.org/W1495780167","https://openalex.org/W1565377632","https://openalex.org/W2008709856","https://openalex.org/W2085487226","https://openalex.org/W2096042744","https://openalex.org/W2098515641","https://openalex.org/W2106364680","https://openalex.org/W2114672179","https://openalex.org/W2121561701","https://openalex.org/W2126985405","https://openalex.org/W2141465109","https://openalex.org/W2159942600","https://openalex.org/W2165874743","https://openalex.org/W2187089797","https://openalex.org/W2291845375","https://openalex.org/W2295598076","https://openalex.org/W2482589566","https://openalex.org/W2484564806","https://openalex.org/W2764426818","https://openalex.org/W2783820198","https://openalex.org/W2791501755","https://openalex.org/W2911500590","https://openalex.org/W2911674704","https://openalex.org/W2912154061","https://openalex.org/W2913106999","https://openalex.org/W2913419142","https://openalex.org/W2949240291","https://openalex.org/W3008892598","https://openalex.org/W3100832357","https://openalex.org/W3102476541","https://openalex.org/W3135902452","https://openalex.org/W3150083996","https://openalex.org/W4229581914","https://openalex.org/W4230414189","https://openalex.org/W4232753955","https://openalex.org/W4238052655","https://openalex.org/W4238174850","https://openalex.org/W4240558030","https://openalex.org/W4240638556","https://openalex.org/W4248518610","https://openalex.org/W4250841606","https://openalex.org/W6677948204","https://openalex.org/W6680735885","https://openalex.org/W6684578312"],"related_works":["https://openalex.org/W2794103424","https://openalex.org/W4245435724","https://openalex.org/W1996530509","https://openalex.org/W3028317537","https://openalex.org/W2389515972","https://openalex.org/W2055301889","https://openalex.org/W1505959757","https://openalex.org/W2376554934","https://openalex.org/W2077790809","https://openalex.org/W2906246018"],"abstract_inverted_index":{"The":[0,114],"quality":[1],"of":[2,12,109,124,131,136],"large-scale":[3],"logistics":[4],"network":[5],"simulation":[6],"highly":[7],"depends":[8],"on":[9,106],"the":[10,50,73,85,107,129],"estimation":[11,108,123],"its":[13],"key":[14],"input":[15,125],"parameters,":[16,126],"which":[17,49],"are":[18,25,53,78],"usually":[19],"influenced":[20],"by":[21],"various":[22],"factors":[23,52,77],"that":[24,75],"difficult":[26],"to":[27,38,64,80,91],"obtain.":[28],"To":[29,71],"tackle":[30],"this":[31,33],"challenge,":[32],"paper":[34],"proposes":[35],"a":[36,66],"framework":[37,87],"estimate":[39],"these":[40],"parameters":[41],"with":[42,128],"high":[43],"precision":[44],"through":[45],"machine":[46],"learning,":[47],"in":[48,82],"impacting":[51],"divided":[54],"into":[55],"static":[56],"and":[57,60,95,134],"dynamic":[58,76],"groups":[59],"used":[61],"as":[62],"features":[63,98],"train":[65],"learning":[67,90],"model":[68],"for":[69,99],"estimation.":[70],"overcome":[72],"obstacle":[74],"hard":[79],"obtain":[81],"some":[83],"scenarios,":[84],"proposed":[86],"employs":[88],"unsupervised":[89],"analyze":[92],"their":[93],"patterns":[94],"extract":[96],"time-invariant":[97],"modeling.":[100],"A":[101],"validation":[102],"study":[103],"is":[104],"conducted":[105],"distribution":[110],"center":[111],"sorting":[112],"times.":[113],"results":[115],"proved":[116],"our":[117],"approach":[118],"can":[119],"generate":[120],"more":[121],"accurate":[122],"even":[127],"shift":[130],"operational":[132],"plans":[133],"absence":[135],"relevant":[137],"data.":[138]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":3}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}