{"id":"https://openalex.org/W4391409119","doi":"https://doi.org/10.1109/wsc60868.2023.10407873","title":"Combining Time Series Data and Snapshot Data for Situation Aware Dispatching in Semiconductor Manufacturing","display_name":"Combining Time Series Data and Snapshot Data for Situation Aware Dispatching in Semiconductor Manufacturing","publication_year":2023,"publication_date":"2023-12-10","ids":{"openalex":"https://openalex.org/W4391409119","doi":"https://doi.org/10.1109/wsc60868.2023.10407873"},"language":"en","primary_location":{"id":"doi:10.1109/wsc60868.2023.10407873","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wsc60868.2023.10407873","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 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/A5084762519","display_name":"Chew Wye Chan","orcid":"https://orcid.org/0000-0002-8523-7888"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chew Wye Chan","raw_affiliation_strings":["D-SIMLAB Technologies Pte Ltd,Singapore,Singapore,609434"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"D-SIMLAB Technologies Pte Ltd,Singapore,Singapore,609434","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090260261","display_name":"Boon Ping Gan","orcid":"https://orcid.org/0009-0003-1030-8762"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Boon Ping Gan","raw_affiliation_strings":["D-SIMLAB Technologies Pte Ltd,Singapore,Singapore,609434"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"D-SIMLAB Technologies Pte Ltd,Singapore,Singapore,609434","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036580072","display_name":"Wentong Cai","orcid":"https://orcid.org/0000-0002-0183-3835"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Wentong Cai","raw_affiliation_strings":["Nanyang Technological University,School of Computer Science and Engineering,Singapore,639798,Singapore,639798"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanyang Technological University,School of Computer Science and Engineering,Singapore,639798,Singapore,639798","institution_ids":["https://openalex.org/I172675005"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7524,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.77821553,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2310","last_page":"2312"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10551","display_name":"Scheduling and Optimization Algorithms","score":0.996999979019165,"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/T10551","display_name":"Scheduling and Optimization Algorithms","score":0.996999979019165,"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/T11814","display_name":"Advanced Manufacturing and Logistics Optimization","score":0.9829000234603882,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9825999736785889,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/snapshot","display_name":"Snapshot (computer storage)","score":0.8683850169181824},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7265552282333374},{"id":"https://openalex.org/keywords/semiconductor-device-fabrication","display_name":"Semiconductor device fabrication","score":0.6919423341751099},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5685606002807617},{"id":"https://openalex.org/keywords/schedule","display_name":"Schedule","score":0.5037125945091248},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.48357173800468445},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4568932056427002},{"id":"https://openalex.org/keywords/factory","display_name":"Factory (object-oriented programming)","score":0.44405052065849304},{"id":"https://openalex.org/keywords/industrial-engineering","display_name":"Industrial engineering","score":0.38013607263565063},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37792980670928955},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36211782693862915},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.19045713543891907},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.13656538724899292}],"concepts":[{"id":"https://openalex.org/C55282118","wikidata":"https://www.wikidata.org/wiki/Q252683","display_name":"Snapshot (computer storage)","level":2,"score":0.8683850169181824},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7265552282333374},{"id":"https://openalex.org/C66018809","wikidata":"https://www.wikidata.org/wiki/Q1570432","display_name":"Semiconductor device fabrication","level":3,"score":0.6919423341751099},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5685606002807617},{"id":"https://openalex.org/C68387754","wikidata":"https://www.wikidata.org/wiki/Q7271585","display_name":"Schedule","level":2,"score":0.5037125945091248},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.48357173800468445},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4568932056427002},{"id":"https://openalex.org/C40149104","wikidata":"https://www.wikidata.org/wiki/Q5620977","display_name":"Factory (object-oriented programming)","level":2,"score":0.44405052065849304},{"id":"https://openalex.org/C13736549","wikidata":"https://www.wikidata.org/wiki/Q4489420","display_name":"Industrial engineering","level":1,"score":0.38013607263565063},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37792980670928955},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36211782693862915},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.19045713543891907},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.13656538724899292},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","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},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C160671074","wikidata":"https://www.wikidata.org/wiki/Q267131","display_name":"Wafer","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wsc60868.2023.10407873","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wsc60868.2023.10407873","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 Winter Simulation Conference (WSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.6399999856948853,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1986986399","https://openalex.org/W2016026777","https://openalex.org/W2040231893","https://openalex.org/W2046629893","https://openalex.org/W2053658258","https://openalex.org/W2064675550","https://openalex.org/W2069641426","https://openalex.org/W2082474823","https://openalex.org/W2093622944","https://openalex.org/W2101234009","https://openalex.org/W2134677473","https://openalex.org/W2152422220","https://openalex.org/W2187089797","https://openalex.org/W2565892336","https://openalex.org/W2765421612","https://openalex.org/W2807766588","https://openalex.org/W2845688424","https://openalex.org/W2890760539","https://openalex.org/W2951632519","https://openalex.org/W2998216295","https://openalex.org/W3006685767","https://openalex.org/W3011076252","https://openalex.org/W3012656115","https://openalex.org/W3017264588","https://openalex.org/W3034453054","https://openalex.org/W3098049288","https://openalex.org/W3099916295","https://openalex.org/W3138899789","https://openalex.org/W3141472204","https://openalex.org/W4210492122","https://openalex.org/W4233573190","https://openalex.org/W4317792685","https://openalex.org/W6675354045","https://openalex.org/W6676959241","https://openalex.org/W6681799635","https://openalex.org/W7066667914"],"related_works":["https://openalex.org/W2542847180","https://openalex.org/W3034994054","https://openalex.org/W2805712290","https://openalex.org/W2155226960","https://openalex.org/W2909129499","https://openalex.org/W2392087771","https://openalex.org/W2761598930","https://openalex.org/W2553994394","https://openalex.org/W2356600124","https://openalex.org/W4288388567"],"abstract_inverted_index":{"Dispatch":[0],"rules":[1,19],"are":[2],"commonly":[3],"used":[4],"to":[5,51,95,139],"schedule":[6],"lots":[7],"in":[8,30,49,62],"the":[9,32,63,78,107,113,123,135,140],"semiconductor":[10],"industry.":[11],"Previous":[12],"studies":[13],"have":[14],"indicated":[15],"that":[16,130],"adapting":[17],"dispatch":[18,38,101,136],"can":[20,67,133],"improve":[21],"overall":[22],"factory":[23,147],"performance.":[24,148],"Machine":[25],"learning":[26,31,98],"has":[27],"proven":[28],"useful":[29],"relationship":[33],"between":[34],"manufacturing":[35,64,141],"situations":[36],"and":[37,87,143],"rules.":[39],"However,":[40],"using":[41],"only":[42],"snapshot":[43,93],"data":[44,86,94,121],"at":[45],"a":[46,145],"given":[47],"point":[48],"time":[50,71,84],"generate":[52],"features":[53,82,91,116],"for":[54,60,100],"these":[55],"models":[56,99],"does":[57],"not":[58],"account":[59],"trends":[61],"situation,":[65],"which":[66],"be":[68],"represented":[69],"as":[70,112],"series":[72,85],"data.":[73],"To":[74],"address":[75],"this":[76,110,131],"issue,":[77],"proposed":[79],"method":[80],"generates":[81],"from":[83,92,117],"combines":[88],"them":[89],"with":[90],"train":[96],"machine":[97],"rule":[102,137],"prediction.":[103],"The":[104],"results":[105,128],"demonstrate":[106],"effectiveness":[108],"of":[109,115,120],"methodology,":[111],"combination":[114],"both":[118],"types":[119],"achieves":[122],"highest":[124],"prediction":[125],"accuracy.":[126],"Simulation":[127],"show":[129],"approach":[132],"adapt":[134],"according":[138],"situation":[142],"achieve":[144],"comparable":[146]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}