{"id":"https://openalex.org/W4317793535","doi":"https://doi.org/10.1109/wsc57314.2022.10015467","title":"Optimal Computing Budget Allocation for Multi-Objective Ranking and Selection Under Bernoulli Distribution","display_name":"Optimal Computing Budget Allocation for Multi-Objective Ranking and Selection Under Bernoulli Distribution","publication_year":2022,"publication_date":"2022-12-11","ids":{"openalex":"https://openalex.org/W4317793535","doi":"https://doi.org/10.1109/wsc57314.2022.10015467"},"language":"en","primary_location":{"id":"doi:10.1109/wsc57314.2022.10015467","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/wsc57314.2022.10015467","pdf_url":null,"source":{"id":"https://openalex.org/S4363607869","display_name":"2022 Winter Simulation Conference (WSC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 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/A5008722002","display_name":"Tianlang Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tianlang Zhao","raw_affiliation_strings":["College of Economic and Management Nanjing University of Aeronautics and Astronautics,Nanjing,CHINA,211106"],"affiliations":[{"raw_affiliation_string":"College of Economic and Management Nanjing University of Aeronautics and Astronautics,Nanjing,CHINA,211106","institution_ids":["https://openalex.org/I9842412"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039890630","display_name":"Jin Xiao","orcid":"https://orcid.org/0000-0003-2462-4996"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao Jin","raw_affiliation_strings":["Centre for Next Generation Logistics National Univeristy of Singapore,Singapore,SINGAPORE,117602"],"affiliations":[{"raw_affiliation_string":"Centre for Next Generation Logistics National Univeristy of Singapore,Singapore,SINGAPORE,117602","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007751109","display_name":"Loo Hay Lee","orcid":"https://orcid.org/0000-0001-9359-0027"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Loo Hay Lee","raw_affiliation_strings":["College of Design and Engineering, National Univeristy of Singapore,Singapore,SINGAPORE,117576"],"affiliations":[{"raw_affiliation_string":"College of Design and Engineering, National Univeristy of Singapore,Singapore,SINGAPORE,117576","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5008722002"],"corresponding_institution_ids":["https://openalex.org/I9842412"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.25330033,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"10","issue":null,"first_page":"3122","last_page":"3133"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11195","display_name":"Simulation Techniques and Applications","score":0.9886000156402588,"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/T11443","display_name":"Advanced Statistical Process Monitoring","score":0.9843999743461609,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"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/bernoullis-principle","display_name":"Bernoulli's principle","score":0.7676820158958435},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.7283701300621033},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.6776807308197021},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.6352546811103821},{"id":"https://openalex.org/keywords/frequentist-inference","display_name":"Frequentist inference","score":0.5990064740180969},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5297868251800537},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5070023536682129},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5045546293258667},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.46332401037216187},{"id":"https://openalex.org/keywords/pareto-principle","display_name":"Pareto principle","score":0.43903979659080505},{"id":"https://openalex.org/keywords/bernoulli-distribution","display_name":"Bernoulli distribution","score":0.422853022813797},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3455222547054291},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.25803670287132263},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.256198525428772},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.17215636372566223},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.16690915822982788},{"id":"https://openalex.org/keywords/random-variable","display_name":"Random variable","score":0.12284570932388306},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.11193248629570007},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10859799385070801}],"concepts":[{"id":"https://openalex.org/C152361515","wikidata":"https://www.wikidata.org/wiki/Q181328","display_name":"Bernoulli's principle","level":2,"score":0.7676820158958435},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.7283701300621033},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.6776807308197021},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.6352546811103821},{"id":"https://openalex.org/C162376815","wikidata":"https://www.wikidata.org/wiki/Q2158281","display_name":"Frequentist inference","level":4,"score":0.5990064740180969},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5297868251800537},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5070023536682129},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5045546293258667},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.46332401037216187},{"id":"https://openalex.org/C137635306","wikidata":"https://www.wikidata.org/wiki/Q182667","display_name":"Pareto principle","level":2,"score":0.43903979659080505},{"id":"https://openalex.org/C27956954","wikidata":"https://www.wikidata.org/wiki/Q391371","display_name":"Bernoulli distribution","level":3,"score":0.422853022813797},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3455222547054291},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.25803670287132263},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.256198525428772},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.17215636372566223},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.16690915822982788},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.12284570932388306},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.11193248629570007},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10859799385070801},{"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/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wsc57314.2022.10015467","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/wsc57314.2022.10015467","pdf_url":null,"source":{"id":"https://openalex.org/S4363607869","display_name":"2022 Winter Simulation Conference (WSC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 Winter Simulation Conference (WSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1536615069","https://openalex.org/W1977764678","https://openalex.org/W2041104376","https://openalex.org/W2072302356","https://openalex.org/W2155927283","https://openalex.org/W2484647765","https://openalex.org/W2523722340","https://openalex.org/W2531611812","https://openalex.org/W2744733893","https://openalex.org/W2752107986","https://openalex.org/W2753712420","https://openalex.org/W2763920787","https://openalex.org/W2773271314","https://openalex.org/W2782364332","https://openalex.org/W2914811027","https://openalex.org/W2963653944","https://openalex.org/W2969776220","https://openalex.org/W2969989383","https://openalex.org/W3140281130","https://openalex.org/W3142656137","https://openalex.org/W3145648774","https://openalex.org/W3148687029","https://openalex.org/W3151137153","https://openalex.org/W4212920552","https://openalex.org/W4213250811","https://openalex.org/W4231024478","https://openalex.org/W4241014718","https://openalex.org/W4250589301","https://openalex.org/W4255981611"],"related_works":["https://openalex.org/W2236428207","https://openalex.org/W4366387587","https://openalex.org/W2151706800","https://openalex.org/W2099646276","https://openalex.org/W1982788967","https://openalex.org/W4324123865","https://openalex.org/W2786287312","https://openalex.org/W4237517023","https://openalex.org/W2933707278","https://openalex.org/W3198935159"],"abstract_inverted_index":{"This":[0],"paper":[1],"studies":[2],"a":[3,40,49,71],"multi-objective":[4],"ranking":[5],"and":[6,26,97],"selection":[7,73,85],"(MORS)":[8],"issue":[9],"with":[10,23],"observations":[11],"following":[12],"Bernoulli":[13,44],"distribution.":[14],"The":[15],"Pareto-optimal":[16],"set":[17],"is":[18,35,55,90,98],"aimed":[19],"to":[20,93,100],"be":[21],"selected":[22],"each":[24],"design":[25],"performance":[27],"measure":[28],"pair":[29],"being":[30],"evaluated":[31],"separately.":[32],"Our":[33],"contribution":[34],"twofold.":[36],"(i)":[37],"We":[38],"provide":[39],"frequentist":[41],"work":[42],"under":[43],"assumption":[45],"in":[46],"MORS":[47],"where":[48],"robust":[50],"asymptotic":[51],"optimal":[52,66,103],"sampling":[53,67],"strategy":[54],"derived":[56],"based":[57,79],"on":[58,80],"large":[59],"deviation":[60],"principle":[61],"(LDP).":[62],"(ii)":[63],"From":[64],"the":[65,101],"strategy,":[68],"we":[69],"propose":[70],"sequential":[72],"procedure,":[74],"named":[75],"MOCBA-B.":[76],"Numerical":[77],"results":[78],"averaged":[81],"probability":[82],"of":[83],"correct":[84],"(PCS)":[86],"show":[87],"that":[88],"MOCBA-B":[89],"significantly":[91],"superior":[92],"equal":[94],"allocation":[95,104],"(EA)":[96],"comparable":[99],"theoretically":[102],"strategy.":[105]},"counts_by_year":[],"updated_date":"2025-12-24T23:09:58.560324","created_date":"2025-10-10T00:00:00"}
