{"id":"https://openalex.org/W4410772300","doi":"https://doi.org/10.1145/3727353.3727395","title":"Research on the Newsvendor Problem Based on Average Estimation of Deep Learning Models","display_name":"Research on the Newsvendor Problem Based on Average Estimation of Deep Learning Models","publication_year":2025,"publication_date":"2025-01-10","ids":{"openalex":"https://openalex.org/W4410772300","doi":"https://doi.org/10.1145/3727353.3727395"},"language":"en","primary_location":{"id":"doi:10.1145/3727353.3727395","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3727353.3727395","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3727353.3727395","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 4th International Conference on Big Data, Information and Computer Network","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3727353.3727395","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102634186","display_name":"Yuanyuan Zhang","orcid":"https://orcid.org/0009-0004-9471-8650"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuanyuan Zhang","raw_affiliation_strings":["School of Management, University of Science and Technology of China, Hefei, Anhui, China"],"raw_orcid":"https://orcid.org/0009-0004-9471-8650","affiliations":[{"raw_affiliation_string":"School of Management, University of Science and Technology of China, Hefei, Anhui, China","institution_ids":["https://openalex.org/I126520041"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5102634186"],"corresponding_institution_ids":["https://openalex.org/I126520041"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"246","last_page":"250"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10328","display_name":"Supply Chain and Inventory Management","score":0.9442999958992004,"subfield":{"id":"https://openalex.org/subfields/1404","display_name":"Management Information Systems"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10328","display_name":"Supply Chain and Inventory Management","score":0.9442999958992004,"subfield":{"id":"https://openalex.org/subfields/1404","display_name":"Management Information Systems"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11413","display_name":"Risk and Portfolio Optimization","score":0.9388999938964844,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9333999752998352,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/newsvendor-model","display_name":"Newsvendor model","score":0.8704029321670532},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6615689396858215},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.5998658537864685},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5658208131790161},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5613535642623901},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3945660889148712},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11931249499320984},{"id":"https://openalex.org/keywords/systems-engineering","display_name":"Systems engineering","score":0.09499534964561462},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.08854973316192627},{"id":"https://openalex.org/keywords/supply-chain","display_name":"Supply chain","score":0.07395374774932861},{"id":"https://openalex.org/keywords/marketing","display_name":"Marketing","score":0.06203728914260864}],"concepts":[{"id":"https://openalex.org/C36181114","wikidata":"https://www.wikidata.org/wiki/Q3130009","display_name":"Newsvendor model","level":3,"score":0.8704029321670532},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6615689396858215},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.5998658537864685},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5658208131790161},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5613535642623901},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3945660889148712},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11931249499320984},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.09499534964561462},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.08854973316192627},{"id":"https://openalex.org/C108713360","wikidata":"https://www.wikidata.org/wiki/Q1824206","display_name":"Supply chain","level":2,"score":0.07395374774932861},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.06203728914260864}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3727353.3727395","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3727353.3727395","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3727353.3727395","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 4th International Conference on Big Data, Information and Computer Network","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3727353.3727395","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3727353.3727395","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3727353.3727395","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 4th International Conference on Big Data, Information and Computer Network","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.46000000834465027}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4410772300.pdf","grobid_xml":"https://content.openalex.org/works/W4410772300.grobid-xml"},"referenced_works_count":9,"referenced_works":["https://openalex.org/W2510468186","https://openalex.org/W2624060304","https://openalex.org/W2781811268","https://openalex.org/W2938085054","https://openalex.org/W3138862382","https://openalex.org/W3159504261","https://openalex.org/W3194873759","https://openalex.org/W4282920135","https://openalex.org/W6747481506"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W2961085424","https://openalex.org/W3215138031","https://openalex.org/W4306674287","https://openalex.org/W3009238340","https://openalex.org/W4360585206","https://openalex.org/W4321369474","https://openalex.org/W4285208911","https://openalex.org/W4387369504","https://openalex.org/W3082895349"],"abstract_inverted_index":{"Inventory":[0],"control":[1],"largely":[2],"relies":[3],"on":[4,63,127],"forecasting":[5],"future":[6,38],"demand.":[7,104],"Since":[8],"market":[9],"demand":[10,39,55,71,85,95],"is":[11,41,48,57],"often":[12,42],"unstable,":[13],"determining":[14],"an":[15],"appropriate":[16],"or":[17,148],"even":[18],"optimal":[19],"inventory":[20,78,111],"level":[21],"can":[22,98],"not":[23],"only":[24,62],"reduce":[25],"stock":[26],"overages":[27],"but":[28],"also":[29],"lower":[30],"the":[31,54,65,75,91,94,107,137,159,170],"likelihood":[32],"of":[33,77,93,145,172],"stockouts.":[34],"In":[35],"existing":[36],"literature,":[37],"distribution":[40,56],"assumed":[43],"to":[44],"be":[45],"known,":[46],"which":[47,101],"impractical":[49],"in":[50,74,110,156],"real-world":[51],"scenarios.":[52],"When":[53],"unknown,":[58],"most":[59,157],"approaches":[60,125],"focus":[61],"estimating":[64],"unknown":[66],"parameters":[67,129,147],"within":[68],"a":[69],"specific":[70],"distribution.":[72],"However,":[73],"field":[76],"management,":[79,112],"there":[80],"exists":[81],"significant":[82],"uncertainty":[83,109],"regarding":[84],"models.":[86],"We":[87],"cannot":[88],"precisely":[89],"determine":[90],"form":[92],"function,":[96],"nor":[97],"we":[99],"identify":[100],"variables":[102],"influence":[103],"To":[105],"address":[106],"model":[108,116,123,160],"this":[113,173],"paper":[114],"utilizes":[115],"averaging":[117,124,161],"methods":[118,134],"and":[119,130],"proposes":[120],"deep":[121],"learning":[122],"based":[126],"network":[128,146],"variable":[131,149],"selection.":[132,150],"These":[133],"effectively":[135],"avoid":[136],"high":[138],"costs":[139],"associated":[140],"with":[141],"incorrect":[142],"initial":[143],"choices":[144],"Numerical":[151],"simulation":[152],"results":[153],"indicate":[154],"that":[155],"cases,":[158],"method":[162],"outperforms":[163],"traditional":[164],"approaches.":[165],"Furthermore,":[166],"empirical":[167],"studies":[168],"validate":[169],"superiority":[171],"method.":[174]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
