{"id":"https://openalex.org/W4403577787","doi":"https://doi.org/10.1145/3627673.3680056","title":"Process-Informed Deep Learning for Enhanced Order Fulfillment Cycle Time Prediction in On-Demand Grocery Retailing","display_name":"Process-Informed Deep Learning for Enhanced Order Fulfillment Cycle Time Prediction in On-Demand Grocery Retailing","publication_year":2024,"publication_date":"2024-10-20","ids":{"openalex":"https://openalex.org/W4403577787","doi":"https://doi.org/10.1145/3627673.3680056"},"language":"en","primary_location":{"id":"doi:10.1145/3627673.3680056","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3627673.3680056","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management","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/A5115603815","display_name":"Jiawen Wei","orcid":"https://orcid.org/0009-0000-0462-5161"},"institutions":[{"id":"https://openalex.org/I4210087373","display_name":"Meizu (China)","ror":"https://ror.org/0067g4302","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210087373"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiawen Wei","raw_affiliation_strings":["Meituan, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0000-0462-5161","affiliations":[{"raw_affiliation_string":"Meituan, Beijing, China","institution_ids":["https://openalex.org/I4210087373"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107950381","display_name":"Z. Ye","orcid":"https://orcid.org/0009-0005-6908-944X"},"institutions":[{"id":"https://openalex.org/I4210087373","display_name":"Meizu (China)","ror":"https://ror.org/0067g4302","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210087373"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziwen Ye","raw_affiliation_strings":["Meituan, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0005-6908-944X","affiliations":[{"raw_affiliation_string":"Meituan, Beijing, China","institution_ids":["https://openalex.org/I4210087373"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100297739","display_name":"Chuan Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuan Yang","raw_affiliation_strings":["Nankai University, Tianjin, China"],"raw_orcid":"https://orcid.org/0009-0007-5773-4880","affiliations":[{"raw_affiliation_string":"Nankai University, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Chen Chen","orcid":"https://orcid.org/0009-0006-0535-5523"},"institutions":[{"id":"https://openalex.org/I4210087373","display_name":"Meizu (China)","ror":"https://ror.org/0067g4302","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210087373"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen Chen","raw_affiliation_strings":["Meituan, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0006-0535-5523","affiliations":[{"raw_affiliation_string":"Meituan, Beijing, China","institution_ids":["https://openalex.org/I4210087373"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050110466","display_name":"Guangrui Ma","orcid":"https://orcid.org/0000-0002-2286-2571"},"institutions":[{"id":"https://openalex.org/I4210087373","display_name":"Meizu (China)","ror":"https://ror.org/0067g4302","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210087373"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guangrui Ma","raw_affiliation_strings":["Meituan, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-2286-2571","affiliations":[{"raw_affiliation_string":"Meituan, Beijing, China","institution_ids":["https://openalex.org/I4210087373"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6103,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.72935081,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"4975","last_page":"4982"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11814","display_name":"Advanced Manufacturing and Logistics Optimization","score":0.9850000143051147,"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/T11814","display_name":"Advanced Manufacturing and Logistics Optimization","score":0.9850000143051147,"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":0.9779999852180481,"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/T12384","display_name":"Customer churn and segmentation","score":0.970300018787384,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/order","display_name":"Order (exchange)","score":0.631881594657898},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6056585311889648},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5435441136360168},{"id":"https://openalex.org/keywords/industrial-engineering","display_name":"Industrial engineering","score":0.326151967048645},{"id":"https://openalex.org/keywords/marketing","display_name":"Marketing","score":0.32306593656539917},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.24601981043815613},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12508487701416016}],"concepts":[{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.631881594657898},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6056585311889648},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5435441136360168},{"id":"https://openalex.org/C13736549","wikidata":"https://www.wikidata.org/wiki/Q4489420","display_name":"Industrial engineering","level":1,"score":0.326151967048645},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.32306593656539917},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.24601981043815613},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12508487701416016},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3627673.3680056","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3627673.3680056","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","score":0.41999998688697815,"id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2047840281","https://openalex.org/W2295598076","https://openalex.org/W2512971201","https://openalex.org/W2594336185","https://openalex.org/W2792091275","https://openalex.org/W2809128166","https://openalex.org/W2809623940","https://openalex.org/W2896480560","https://openalex.org/W2963430933","https://openalex.org/W2981664222","https://openalex.org/W3007925440","https://openalex.org/W3012514093","https://openalex.org/W3080227975","https://openalex.org/W3080548826","https://openalex.org/W3104926413","https://openalex.org/W3172656902","https://openalex.org/W3209067688","https://openalex.org/W4205373477","https://openalex.org/W4221085709","https://openalex.org/W4364358787","https://openalex.org/W4385283878"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W4286716842","https://openalex.org/W2371281775"],"abstract_inverted_index":{"Accurate":[0],"prediction":[1,87,114],"of":[2,20,66,102,110,142,153,177,184,196,219],"Order":[3],"Fulfillment":[4],"Cycle":[5],"Time":[6],"(OFCT)":[7],"is":[8,61],"essential":[9],"for":[10,41,240],"improving":[11],"customer":[12,46,251],"satisfaction":[13],"and":[14,37,54,76,130,139,211,249],"operational":[15],"efficiency":[16],"within":[17],"the":[18,103,108,126,135,140,171,182,197,217],"domain":[19],"on-demand":[21],"grocery":[22],"retailing":[23],"(OGR).":[24],"OGR":[25,199,241],"platforms":[26,200,242],"typically":[27],"rely":[28],"on":[29,146,187,193],"Front":[30],"Distribution":[31],"Centers":[32],"(FDCs)":[33],"to":[34,44,57,84,123,169,244],"manage":[35],"inventory":[36],"deploy":[38],"dedicated":[39],"fleets":[40],"last-mile":[42],"delivery":[43,58,74,131],"fulfill":[45],"demands.":[47],"Orders":[48],"are":[49],"processed":[50],"at":[51],"FDCs":[52],"initially":[53],"then":[55],"dispatched":[56],"fleets.":[59],"OFCT":[60,86,113,235],"influenced":[62],"by":[63,98,208],"a":[64,99,151,231],"multitude":[65],"factors":[67,80],"such":[68],"as":[69],"order":[70,104,147,178],"volume,":[71],"processing":[72,129],"capabilities,":[73],"capacities,":[75],"dispatching":[77],"strategies.":[78],"These":[79],"pose":[81],"significant":[82],"challenges":[83],"refining":[85],"accuracy.":[88],"This":[89],"paper":[90],"presents":[91,230],"an":[92],"innovative":[93],"deep":[94],"learning":[95],"model":[96,204,224],"informed":[97],"detailed":[100],"comprehension":[101],"fulfillment":[105,247],"process,":[106],"with":[107],"objective":[109],"significantly":[111],"enhancing":[112],"precision.":[115],"We":[116],"employ":[117],"Recurrent":[118],"Neural":[119],"Network":[120],"(RNN)":[121],"blocks":[122],"dynamically":[124],"evaluate":[125],"workload":[127],"across":[128],"stages.":[132],"To":[133],"address":[134],"interactions":[136,173],"among":[137],"orders":[138],"impact":[141],"latent":[143],"courier":[144],"dynamics":[145],"prioritization,":[148],"we":[149,215],"incorporate":[150],"suite":[152],"specialized":[154],"attention":[155],"modules":[156],"into":[157],"our":[158,203,223],"framework.":[159],"Our":[160,228],"approach":[161],"further":[162],"employs":[163],"Deep":[164],"Bayesian":[165],"Multi-Target":[166],"Learning":[167],"(DBMTL)":[168],"discern":[170],"sequential":[172],"between":[174],"various":[175],"stages":[176,186],"fulfillment,":[179],"thereby":[180],"elucidating":[181],"influence":[183],"earlier":[185],"subsequent":[188],"ones.":[189],"Through":[190],"online":[191],"experiments":[192],"Meituan-Maicai,":[194],"one":[195],"biggest":[198],"in":[201,222,234],"China,":[202],"demonstrates":[205],"its":[206],"superiority":[207],"outperforming":[209],"well-acknowledged":[210],"advanced":[212],"baselines.":[213],"Furthermore,":[214],"assess":[216],"contributions":[218],"specific":[220],"designs":[221],"through":[225],"ablation":[226],"studies.":[227],"research":[229],"notable":[232],"advancement":[233],"prediction,":[236],"providing":[237],"valuable":[238],"insights":[239],"seeking":[243],"optimize":[245],"their":[246],"operations":[248],"enhance":[250],"experiences.":[252]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}