{"id":"https://openalex.org/W2944690703","doi":null,"title":"Integrated Machine Learning and Optimization Frameworks with Applications in Operations Management","display_name":"Integrated Machine Learning and Optimization Frameworks with Applications in Operations Management","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2944690703","mag":"2944690703"},"language":"en","primary_location":{"id":"pmh:oai:deepblue.lib.umich.edu:2027.42/145936","doi":null,"is_oa":true,"landing_page_url":"https://hdl.handle.net/2027.42/145936","pdf_url":"https://hdl.handle.net/2027.42/145936","source":{"id":"https://openalex.org/S4306400393","display_name":"Deep Blue (University of Michigan)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I27837315","host_organization_name":"University of Michigan","host_organization_lineage":["https://openalex.org/I27837315"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://hdl.handle.net/2027.42/145936","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5048507418","display_name":"Amirhossein Meisami","orcid":"https://orcid.org/0000-0003-3493-4410","observed_orcids":["https://orcid.org/0000-0003-3493-4410"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Amirhossein Meisami","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-3493-4410","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5048507418"],"corresponding_institution_ids":[],"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11773","display_name":"Healthcare Operations and Scheduling Optimization","score":0.9994999766349792,"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"}},"topics":[{"id":"https://openalex.org/T11773","display_name":"Healthcare Operations and Scheduling Optimization","score":0.9994999766349792,"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/T10551","display_name":"Scheduling and Optimization Algorithms","score":0.0,"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/T10328","display_name":"Supply Chain and Inventory Management","score":0.0,"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/intensive-care-unit","display_name":"intensive care unit","score":0.8450000286102295},{"id":"https://openalex.org/keywords/quantile-regression-forest","display_name":"quantile regression forest","score":0.9049999713897705},{"id":"https://openalex.org/keywords/stochastic-optimization","display_name":"stochastic optimization","score":0.6980000138282776},{"id":"https://openalex.org/keywords/robust-optimization","display_name":"robust optimization","score":0.9800000190734863},{"id":"https://openalex.org/keywords/distributionally-robust-optimization","display_name":"distributionally robust optimization","score":1.0},{"id":"https://openalex.org/keywords/thompson-sampling","display_name":"Thompson sampling","score":0.9259999990463257},{"id":"https://openalex.org/keywords/chance-constrained-programming","display_name":"chance-constrained programming","score":0.6809999942779541},{"id":"https://openalex.org/keywords/scenario-generation","display_name":"scenario generation","score":0.46000000834465027},{"id":"https://openalex.org/keywords/hospital-operations","display_name":"hospital operations","score":0.3230000138282776},{"id":"https://openalex.org/keywords/data-driven-decision-making","display_name":"data-driven decision making","score":0.5370000004768372}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5933481454849243},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41398999094963074},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.403360515832901}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:deepblue.lib.umich.edu:2027.42/145936","doi":null,"is_oa":true,"landing_page_url":"https://hdl.handle.net/2027.42/145936","pdf_url":"https://hdl.handle.net/2027.42/145936","source":{"id":"https://openalex.org/S4306400393","display_name":"Deep Blue (University of Michigan)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I27837315","host_organization_name":"University of Michigan","host_organization_lineage":["https://openalex.org/I27837315"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"},{"id":"mag:2944690703","doi":null,"is_oa":true,"landing_page_url":"https://hdl.handle.net/2027.42/145936","pdf_url":null,"source":{"id":"https://openalex.org/S4306400393","display_name":"Deep Blue (University of Michigan)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I27837315","host_organization_name":"University of Michigan","host_organization_lineage":["https://openalex.org/I27837315"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Deep Blue (University of Michigan)","raw_type":null}],"best_oa_location":{"id":"pmh:oai:deepblue.lib.umich.edu:2027.42/145936","doi":null,"is_oa":true,"landing_page_url":"https://hdl.handle.net/2027.42/145936","pdf_url":"https://hdl.handle.net/2027.42/145936","source":{"id":"https://openalex.org/S4306400393","display_name":"Deep Blue (University of Michigan)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I27837315","host_organization_name":"University of Michigan","host_organization_lineage":["https://openalex.org/I27837315"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.6240000128746033,"display_name":"Good health and well-being"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2944690703.pdf","grobid_xml":"https://content.openalex.org/works/W2944690703.grobid-xml"},"referenced_works_count":105,"referenced_works":["https://openalex.org/W60915869","https://openalex.org/W107452617","https://openalex.org/W165068132","https://openalex.org/W197520142","https://openalex.org/W1484181293","https://openalex.org/W1504694836","https://openalex.org/W1504946885","https://openalex.org/W1507350577","https://openalex.org/W1542523037","https://openalex.org/W1571154989","https://openalex.org/W1573647811","https://openalex.org/W1573871938","https://openalex.org/W1595030384","https://openalex.org/W1598891955","https://openalex.org/W1603462117","https://openalex.org/W1678356000","https://openalex.org/W1710423945","https://openalex.org/W1763335505","https://openalex.org/W1767463261","https://openalex.org/W1782199458","https://openalex.org/W1820129507","https://openalex.org/W1859853441","https://openalex.org/W1911551976","https://openalex.org/W1966666421","https://openalex.org/W1967729795","https://openalex.org/W1970325223","https://openalex.org/W1976209404","https://openalex.org/W1976466849","https://openalex.org/W1977366185","https://openalex.org/W1978084456","https://openalex.org/W1988068488","https://openalex.org/W1989119634","https://openalex.org/W1989755892","https://openalex.org/W1992050102","https://openalex.org/W1994071747","https://openalex.org/W2003777645","https://openalex.org/W2004910511","https://openalex.org/W2011066205","https://openalex.org/W2012224392","https://openalex.org/W2012251130","https://openalex.org/W2014838276","https://openalex.org/W2029598996","https://openalex.org/W2032860812","https://openalex.org/W2033691722","https://openalex.org/W2038676869","https://openalex.org/W2038746872","https://openalex.org/W2041774785","https://openalex.org/W2042752006","https://openalex.org/W2047081748","https://openalex.org/W2052992999","https://openalex.org/W2058351437","https://openalex.org/W2060189758","https://openalex.org/W2066778083","https://openalex.org/W2068490100","https://openalex.org/W2069099760","https://openalex.org/W2071680386","https://openalex.org/W2072492033","https://openalex.org/W2082202862","https://openalex.org/W2082302018","https://openalex.org/W2083350518","https://openalex.org/W2086770994","https://openalex.org/W2092660864","https://openalex.org/W2094344546","https://openalex.org/W2096355537","https://openalex.org/W2101134120","https://openalex.org/W2103428320","https://openalex.org/W2106504075","https://openalex.org/W2110005947","https://openalex.org/W2112198937","https://openalex.org/W2115339997","https://openalex.org/W2122915505","https://openalex.org/W2123377387","https://openalex.org/W2125417745","https://openalex.org/W2127513097","https://openalex.org/W2128318543","https://openalex.org/W2131039757","https://openalex.org/W2132670515","https://openalex.org/W2135046866","https://openalex.org/W2135243999","https://openalex.org/W2136654437","https://openalex.org/W2140006863","https://openalex.org/W2145769891","https://openalex.org/W2149706766","https://openalex.org/W2157100129","https://openalex.org/W2164814309","https://openalex.org/W2167412190","https://openalex.org/W2168405694","https://openalex.org/W2272081875","https://openalex.org/W2293326121","https://openalex.org/W2338776209","https://openalex.org/W2403090768","https://openalex.org/W2529626366","https://openalex.org/W2536486196","https://openalex.org/W2555763236","https://openalex.org/W2605835605","https://openalex.org/W2911964244","https://openalex.org/W2962719995","https://openalex.org/W3122167207","https://openalex.org/W3124121179","https://openalex.org/W3124323365","https://openalex.org/W3124891448","https://openalex.org/W3141474945","https://openalex.org/W4245505859","https://openalex.org/W4247165901","https://openalex.org/W4294541781"],"related_works":["https://openalex.org/W2529626366","https://openalex.org/W4385720257","https://openalex.org/W3200221534","https://openalex.org/W4205161874","https://openalex.org/W4294885164","https://openalex.org/W7006161737","https://openalex.org/W3092451543","https://openalex.org/W1545619031","https://openalex.org/W4400850604","https://openalex.org/W2525688594"],"abstract_inverted_index":{"Incorporation":[0],"of":[1,8,15,51,68,139,169,175,195,203,214,221,234,241,246,279,334,356,396],"contextual":[2],"inference":[3],"in":[4,29,108,146,191,238,255,304,342,379,459,479],"the":[5,66,121,127,167,200,208,227,232,239,276,305,313,320,332,363,377,393,401,438,453],"optimality":[6],"analysis":[7],"operational":[9,83,123,257,292],"problems":[10,407],"is":[11,80,251],"a":[12,72,75,81,99,109,130,136,180,192,252,261,423,430],"canonical":[13],"characteristic":[14],"data-informed":[16],"decision":[17,47,67],"making":[18,48],"that":[19,39,85,111,217,264,330,375,411,432,451],"requires":[20],"interdisciplinary":[21],"research.":[22],"In":[23,60,226,392],"an":[24,161,477],"attempt":[25],"to":[26,70,74,101,105,207,271,286,299,319,416,447],"achieve":[27],"individualization":[28],"operations":[30,57,237],"management,":[31],"we":[32,64,178,230,283,399,426],"design":[33],"rigorous":[34],"and":[35,44,56,93,149,189,268,351,388,428,473],"yet":[36],"practical":[37],"mechanisms":[38],"boost":[40],"efficiency,":[41],"restrain":[42],"uncertainty":[43,303],"elevate":[45],"real-time":[46],"through":[49],"integration":[50],"ideas":[52],"from":[53],"machine":[54],"learning":[55,406,439],"research":[58],"literature.":[59],"our":[61,465],"first":[62,402],"study,":[63,229],"investigate":[65,284],"whether":[69],"admit":[71,103],"patient":[73,94,176,328],"critical":[76],"care":[77,196,205],"unit":[78,236],"which":[79],"crucial":[82,253],"problem":[84,128,310],"has":[86],"significant":[87],"influence":[88],"on":[89,404],"both":[90,266],"hospital":[91,256],"performance":[92],"outcomes.":[95],"Hospitals":[96],"currently":[97],"lack":[98],"methodology":[100,158,186,263],"selectively":[102],"patients":[104,143],"these":[106,357],"units":[107,148],"way":[110],"patient\u2019s":[112],"individual":[113],"health":[114],"metrics":[115],"can":[116,367,467],"be":[117,368,417,468],"incorporated":[118],"while":[119,198],"considering":[120],"hospital\u2019s":[122],"constraints.":[124],"We":[125,259,294,360,462],"model":[126,138,194],"as":[129],"complex":[131],"loss":[132],"queueing":[133],"network":[134,168,193],"with":[135,419,476],"stochastic":[137,384,409],"how":[140,150,362,464],"long":[141],"risk-stratified":[142],"spend":[144],"time":[145,337],"particular":[147],"they":[151],"transition":[152],"between":[153],"units.":[154,170],"A":[155],"data-driven":[156],"optimization":[157,301,373,387],"then":[159],"approximates":[160],"optimal":[162],"admission":[163,183,215],"control":[164],"policy":[165],"for":[166],"While":[171],"enforcing":[172],"low":[173],"levels":[174],"blocking,":[177],"optimize":[179],"monotonic":[181],"dual-threshold":[182],"policy.":[184],"Our":[185],"captures":[187],"utilization":[188],"accessibility":[190],"pathways":[197],"supporting":[199],"personalized":[201],"allocation":[202],"scarce":[204],"resources":[206],"neediest":[209],"patients.":[210],"The":[211,244],"interesting":[212],"benefits":[213],"thresholds":[216],"vary":[218],"by":[219,311,441],"day":[220],"week":[222],"are":[223,412],"also":[224],"examined.":[225],"second":[228],"analyze":[231,429],"efficiency":[233],"surgical":[235,247],"era":[240],"big":[242],"data.":[243],"accuracy":[245],"case":[248,336],"duration":[249],"predictions":[250,274,290],"element":[254],"performance.":[258],"propose":[260,427],"comprehensive":[262],"incorporates":[265],"structured":[267],"unstructured":[269],"data":[270,315],"generate":[272],"individualized":[273,289,335],"regarding":[275],"overall":[277],"distribution":[278],"surgery":[280,307],"durations.":[281],"Consequently,":[282],"methods":[285],"incorporate":[287],"such":[288],"into":[291,370,470],"decision-making.":[293],"introduce":[295],"novel":[296],"prescriptive":[297],"models":[298],"address":[300],"under":[302,408],"fundamental":[306],"appointment":[308],"scheduling":[309],"utilizing":[312],"multi-dimensional":[314],"features":[316,329],"available":[317],"prior":[318],"surgery.":[321],"Electronic":[322],"medical":[323],"records":[324],"systems":[325],"provide":[326,400],"detailed":[327,358],"enable":[331],"prediction":[333],"distributions;":[338],"however,":[339],"existing":[340],"approaches":[341],"this":[343,381,397],"context":[344],"usually":[345],"employ":[346],"only":[347],"limited,":[348],"aggregate":[349],"information,":[350],"do":[352],"not":[353],"take":[354],"advantages":[355],"features.":[359],"show":[361],"quantile":[364],"regression":[365],"forest,":[366],"integrated":[369,469],"three":[371],"common":[372],"formulations":[374],"capture":[376],"stochasticity":[378],"addressing":[380],"problem,":[382],"including":[383],"optimization,":[385],"robust":[386,390],"distributionally":[389],"optimization.":[391],"last":[394],"part":[395],"dissertation,":[398],"study":[403],"online":[405,480],"constraints":[410,450],"\"soft\",":[413],"i.e.,":[414],"need":[415],"satisfied":[418],"high":[420],"likelihood.":[421],"Under":[422],"Bayesian":[424],"framework,":[425],"scheme":[431,466],"provides":[433],"statistical":[434],"feasibility":[435],"guarantees":[436],"throughout":[437],"horizon,":[440],"using":[442],"posterior":[443],"Monte":[444],"Carlo":[445],"samples":[446],"form":[448],"sampled":[449],"generalize":[452],"scenario":[454],"generation":[455],"approach":[456],"commonly":[457],"used":[458],"chance-constrained":[460],"programming.":[461],"demonstrate":[463],"Thompson":[471],"sampling":[472],"illustrate":[474],"it":[475],"application":[478],"advertisement.":[481]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2019-05-16T00:00:00"}