{"id":"https://openalex.org/W4387475768","doi":"https://doi.org/10.1109/tase.2023.3320189","title":"Bayesian Optimization for the Vehicle Dwelling Policy in a Semiconductor Wafer Fab","display_name":"Bayesian Optimization for the Vehicle Dwelling Policy in a Semiconductor Wafer Fab","publication_year":2023,"publication_date":"2023-10-10","ids":{"openalex":"https://openalex.org/W4387475768","doi":"https://doi.org/10.1109/tase.2023.3320189"},"language":"en","primary_location":{"id":"doi:10.1109/tase.2023.3320189","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tase.2023.3320189","pdf_url":null,"source":{"id":"https://openalex.org/S34881539","display_name":"IEEE Transactions on Automation Science and Engineering","issn_l":"1545-5955","issn":["1545-5955","1558-3783"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Automation Science and Engineering","raw_type":"journal-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/A5058763559","display_name":"Bonggwon Kang","orcid":"https://orcid.org/0000-0001-7367-9817"},"institutions":[{"id":"https://openalex.org/I4921948","display_name":"Pusan National University","ror":"https://ror.org/01an57a31","country_code":"KR","type":"education","lineage":["https://openalex.org/I4921948"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Bonggwon Kang","raw_affiliation_strings":["Department of Industrial Engineering, Industrial Data Science and Engineering, Pusan National University, Busan, South Korea","Industrial Data Science and Engineering, Department of Industrial Engineering, Pusan National University, Busan, South Korea"],"raw_orcid":"https://orcid.org/0000-0001-7367-9817","affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering, Industrial Data Science and Engineering, Pusan National University, Busan, South Korea","institution_ids":["https://openalex.org/I4921948"]},{"raw_affiliation_string":"Industrial Data Science and Engineering, Department of Industrial Engineering, Pusan National University, Busan, South Korea","institution_ids":["https://openalex.org/I4921948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035632114","display_name":"Chiwoo Park","orcid":"https://orcid.org/0000-0002-2463-8901"},"institutions":[{"id":"https://openalex.org/I103163165","display_name":"Florida State University","ror":"https://ror.org/05g3dte14","country_code":"US","type":"education","lineage":["https://openalex.org/I103163165"]},{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chiwoo Park","raw_affiliation_strings":["Department of Industrial and Manufacturing Engineering, Florida State University, Tallahassee, FL, USA","Department of Industrial and Systems Engineering, University of Washington, Seattle, WA, USA"],"raw_orcid":"https://orcid.org/0000-0002-2463-8901","affiliations":[{"raw_affiliation_string":"Department of Industrial and Manufacturing Engineering, Florida State University, Tallahassee, FL, USA","institution_ids":["https://openalex.org/I103163165"]},{"raw_affiliation_string":"Department of Industrial and Systems Engineering, University of Washington, Seattle, WA, USA","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089642794","display_name":"Haejoong Kim","orcid":"https://orcid.org/0000-0002-3727-094X"},"institutions":[{"id":"https://openalex.org/I28615091","display_name":"Kyonggi University","ror":"https://ror.org/032xf8h46","country_code":"KR","type":"education","lineage":["https://openalex.org/I28615091"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Haejoong Kim","raw_affiliation_strings":["Department of Industrial and Management Engineering, Kyonggi University, Gyeonggi, Suwon, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-3727-094X","affiliations":[{"raw_affiliation_string":"Department of Industrial and Management Engineering, Kyonggi University, Gyeonggi, Suwon, South Korea","institution_ids":["https://openalex.org/I28615091"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079132110","display_name":"Soondo Hong","orcid":"https://orcid.org/0000-0001-7817-6776"},"institutions":[{"id":"https://openalex.org/I4921948","display_name":"Pusan National University","ror":"https://ror.org/01an57a31","country_code":"KR","type":"education","lineage":["https://openalex.org/I4921948"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Soondo Hong","raw_affiliation_strings":["Department of Industrial Engineering, Industrial Data Science and Engineering, Pusan National University, Busan, South Korea","Industrial Data Science and Engineering, Department of Industrial Engineering, Pusan National University, Busan, South Korea"],"raw_orcid":"https://orcid.org/0000-0001-7817-6776","affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering, Industrial Data Science and Engineering, Pusan National University, Busan, South Korea","institution_ids":["https://openalex.org/I4921948"]},{"raw_affiliation_string":"Industrial Data Science and Engineering, Department of Industrial Engineering, Pusan National University, Busan, South Korea","institution_ids":["https://openalex.org/I4921948"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.1359,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.79609444,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"21","issue":"4","first_page":"5942","last_page":"5952"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11814","display_name":"Advanced Manufacturing and Logistics Optimization","score":0.9977999925613403,"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":0.9977999925613403,"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":0.982699990272522,"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/T11159","display_name":"Manufacturing Process and Optimization","score":0.9818999767303467,"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/wafer","display_name":"Wafer","score":0.5569326877593994},{"id":"https://openalex.org/keywords/semiconductor","display_name":"Semiconductor","score":0.5062138438224792},{"id":"https://openalex.org/keywords/semiconductor-device-fabrication","display_name":"Semiconductor device fabrication","score":0.50138258934021},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.48649996519088745},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4712272882461548},{"id":"https://openalex.org/keywords/wafer-fabrication","display_name":"Wafer fabrication","score":0.4385088086128235},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.40689027309417725},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.35710784792900085},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.20479071140289307},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.18403387069702148}],"concepts":[{"id":"https://openalex.org/C160671074","wikidata":"https://www.wikidata.org/wiki/Q267131","display_name":"Wafer","level":2,"score":0.5569326877593994},{"id":"https://openalex.org/C108225325","wikidata":"https://www.wikidata.org/wiki/Q11456","display_name":"Semiconductor","level":2,"score":0.5062138438224792},{"id":"https://openalex.org/C66018809","wikidata":"https://www.wikidata.org/wiki/Q1570432","display_name":"Semiconductor device fabrication","level":3,"score":0.50138258934021},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.48649996519088745},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4712272882461548},{"id":"https://openalex.org/C35750839","wikidata":"https://www.wikidata.org/wiki/Q7959421","display_name":"Wafer fabrication","level":3,"score":0.4385088086128235},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.40689027309417725},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.35710784792900085},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.20479071140289307},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.18403387069702148}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tase.2023.3320189","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tase.2023.3320189","pdf_url":null,"source":{"id":"https://openalex.org/S34881539","display_name":"IEEE Transactions on Automation Science and Engineering","issn_l":"1545-5955","issn":["1545-5955","1558-3783"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Automation Science and Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3288028215","display_name":"CDS&E/Collaborative Research: Local Gaussian Process Approaches for Predicting Jump Behaviors of Engineering Systems","funder_award_id":"2152655","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320321284","display_name":"Pusan National University","ror":"https://ror.org/01an57a31"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W1480330138","https://openalex.org/W1502922572","https://openalex.org/W1510052597","https://openalex.org/W1964243794","https://openalex.org/W1982447381","https://openalex.org/W1982595090","https://openalex.org/W1985270437","https://openalex.org/W2001510991","https://openalex.org/W2003643153","https://openalex.org/W2009470228","https://openalex.org/W2012218926","https://openalex.org/W2042101375","https://openalex.org/W2044634403","https://openalex.org/W2045142606","https://openalex.org/W2045958049","https://openalex.org/W2059819642","https://openalex.org/W2084977128","https://openalex.org/W2088178728","https://openalex.org/W2088477973","https://openalex.org/W2092262344","https://openalex.org/W2108209131","https://openalex.org/W2132066557","https://openalex.org/W2145493798","https://openalex.org/W2164747895","https://openalex.org/W2165008631","https://openalex.org/W2233759382","https://openalex.org/W2299997501","https://openalex.org/W2491710580","https://openalex.org/W2756659234","https://openalex.org/W2758069629","https://openalex.org/W2763920787","https://openalex.org/W2793351526","https://openalex.org/W2884995040","https://openalex.org/W2945320416","https://openalex.org/W2981237414","https://openalex.org/W2996417001","https://openalex.org/W3024688472","https://openalex.org/W3092477802","https://openalex.org/W3112623968","https://openalex.org/W3119344262","https://openalex.org/W3134493930","https://openalex.org/W3144471408","https://openalex.org/W3157720597","https://openalex.org/W4238746485","https://openalex.org/W4255654664","https://openalex.org/W4287685979","https://openalex.org/W4289656833","https://openalex.org/W4296250256","https://openalex.org/W4317792385","https://openalex.org/W6638209102","https://openalex.org/W6678911119","https://openalex.org/W6734918142","https://openalex.org/W6739951569","https://openalex.org/W6781940814"],"related_works":["https://openalex.org/W2170726572","https://openalex.org/W2146435486","https://openalex.org/W2006086900","https://openalex.org/W1483119123","https://openalex.org/W2377558694","https://openalex.org/W2992897358","https://openalex.org/W2394172622","https://openalex.org/W1594978932","https://openalex.org/W2083418455","https://openalex.org/W2025046394"],"abstract_inverted_index":{"Many":[0],"semiconductor":[1,211],"fabrication":[2],"plants":[3],"(fabs)":[4],"prefer":[5],"simulation-based":[6,201],"decision":[7,202],"making":[8,203],"for":[9,204,233,247],"vehicle":[10,50,86,107,169,206,235,290,299],"dwelling":[11,23,51,87,108,170,207,236,291],"policies":[12,24,52,109,208],"because":[13,70],"it":[14,71],"can":[15],"capture":[16],"a":[17,76,85,91,112,133,140,184,210,229,234,263,268],"fab\u2019s":[18,213],"scalability":[19],"and":[20,30,115,149,188,250,298],"complexity.":[21],"Vehicle":[22],"assign":[25],"idle":[26],"vehicles":[27],"to":[28,39,42,48,74,166,191,242,287],"intra-bay":[29],"outer":[31],"loops":[32],"in":[33,60,200,209,228,262,294],"automated":[34],"material":[35],"handling":[36],"systems":[37],"(AMHSs)":[38],"respond":[40],"quickly":[41],"transportation":[43],"demands.":[44],"Fabs":[45],"are":[46],"motivated":[47,196],"control":[49,260],"when":[53],"fabs":[54],"experience":[55],"significant":[56],"fluctuations,":[57],"i.e.,":[58],"changes":[59],"product":[61],"mix.":[62],"Fab":[63],"operators":[64],"evaluate":[65],"manually":[66],"designed":[67],"candidate":[68],"solutions":[69],"is":[72,179,195,238],"time-intensive":[73],"run":[75],"large-scale":[77],"simulation":[78,92,163,223,231,248,269,285],"with":[79,100,176,255,274],"numerous":[80],"potential":[81],"solutions.":[82],"To":[83],"determine":[84,167],"policy,":[88],"we":[89,266],"propose":[90],"optimization":[93,98,232,270],"approach":[94,271,279],"based":[95,110],"on":[96,111,225],"Bayesian":[97],"(BO)":[99],"class-based":[101,177,275],"clustering.":[102,276],"BO":[103,175,273],"adaptively":[104],"traces":[105],"efficient":[106,168,289],"surrogate":[113],"model":[114],"an":[116],"acquisition":[117],"function.":[118],"Class-based":[119],"clustering":[120,178],"alleviates":[121],"the":[122,126,143,146,150,153,160,198,220,243,256,282],"high":[123],"dimensionality":[124],"of":[125,136,145,152,162,222,259,284],"design":[127,147,253],"space":[128,148,254],"by":[129,197],"grouping":[130],"bays":[131],"into":[132],"small":[134],"number":[135,161,258,283],"classes.":[137],"By":[138],"striking":[139],"balance":[141],"between":[142],"complexity":[144],"quality":[151],"solutions,":[154],"our":[155],"proposed":[156,278],"policy":[157,237],"significantly":[158,280],"reduces":[159,281],"runs":[164,249,286],"required":[165],"policies.":[171],"We":[172],"conclude":[173],"that":[174],"more":[180],"advantageous":[181],"than":[182],"using":[183,189,272],"genetic":[185],"algorithm":[186],"(GA)":[187],"heuristics.Note":[190],"Practitioners\u2014":[192],"This":[193],"study":[194],"difficulties":[199],"optimal":[205],"wafer":[212],"AMHS.":[214],"While":[215],"existing":[216],"research":[217],"has":[218],"demonstrated":[219],"effectiveness":[221],"analysis":[224],"operational":[226],"planning":[227],"fab,":[230],"still":[239],"problematical":[240],"due":[241],"heavy":[244],"computation":[245],"burden":[246],"its":[251],"large":[252],"increased":[257],"variables":[261],"fab.":[264],"Therefore,":[265],"develop":[267],"The":[277],"obtain":[288],"policies,":[292],"resulting":[293],"decreased":[295],"delivery":[296],"times":[297],"utilization":[300],"rates.":[301]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}
