{"id":"https://openalex.org/W7214916506","doi":"https://doi.org/10.21872/annual2025_5968","title":"Dynamic Graph-Based Reinforcement Learning for Efficient Scheduling in Large-Scale Flexible Job Shops","display_name":"Dynamic Graph-Based Reinforcement Learning for Efficient Scheduling in Large-Scale Flexible Job Shops","publication_year":2026,"publication_date":"2026-09-30","ids":{"openalex":"https://openalex.org/W7214916506","doi":"https://doi.org/10.21872/annual2025_5968"},"language":"en","primary_location":{"id":"doi:10.21872/annual2025_5968","doi":"https://doi.org/10.21872/annual2025_5968","is_oa":false,"landing_page_url":"https://doi.org/10.21872/annual2025_5968","pdf_url":null,"source":{"id":"https://openalex.org/S7407087832","display_name":"IISE Annual Conference & Expo","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"IISE Annual Conference & Expo 2025","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5153563562","display_name":"Jeongwon Park","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jeongwon Park","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5153627972","display_name":"Feng Ju","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng Ju","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.75787248,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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/T10551","display_name":"Scheduling and Optimization Algorithms","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T10551","display_name":"Scheduling and Optimization Algorithms","score":1.0,"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/T11814","display_name":"Advanced Manufacturing and Logistics Optimization","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/T11773","display_name":"Healthcare Operations and Scheduling Optimization","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/flexible-job-shop-scheduling","display_name":"flexible job shop scheduling","score":0.9980000257492065},{"id":"https://openalex.org/keywords/heterogeneous-graph-attention-network","display_name":"heterogeneous graph attention network","score":0.9010000228881836},{"id":"https://openalex.org/keywords/proximal-policy-optimization","display_name":"proximal policy optimization","score":0.8650000095367432},{"id":"https://openalex.org/keywords/markov-decision-process","display_name":"Markov decision process","score":0.8619999885559082},{"id":"https://openalex.org/keywords/makespan-minimization","display_name":"makespan minimization","score":0.8999999761581421},{"id":"https://openalex.org/keywords/large-scale-scheduling-problems","display_name":"large-scale scheduling","score":0.7540000081062317},{"id":"https://openalex.org/keywords/priority-dispatching-rules","display_name":"priority dispatching rules","score":0.8489999771118164}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7407000064849854},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7342000007629395},{"id":"https://openalex.org/C55416958","wikidata":"https://www.wikidata.org/wiki/Q6206757","display_name":"Job shop scheduling","level":3,"score":0.7039999961853027},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.6384999752044678},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.578499972820282},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.5662999749183655},{"id":"https://openalex.org/C107568181","wikidata":"https://www.wikidata.org/wiki/Q5319000","display_name":"Dynamic priority scheduling","level":3,"score":0.4790000021457672},{"id":"https://openalex.org/C2777243215","wikidata":"https://www.wikidata.org/wiki/Q1493226","display_name":"Job shop","level":5,"score":0.4602000117301941},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.43639999628067017},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.43160000443458557},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.38670000433921814},{"id":"https://openalex.org/C158336966","wikidata":"https://www.wikidata.org/wiki/Q3074426","display_name":"Flow shop scheduling","level":4,"score":0.3831999897956848},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.36340001225471497},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3549000024795532},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.3346000015735626},{"id":"https://openalex.org/C127456818","wikidata":"https://www.wikidata.org/wiki/Q238879","display_name":"Rate-monotonic scheduling","level":4,"score":0.31450000405311584},{"id":"https://openalex.org/C119948110","wikidata":"https://www.wikidata.org/wiki/Q7858726","display_name":"Two-level scheduling","level":4,"score":0.29010000824928284},{"id":"https://openalex.org/C17098449","wikidata":"https://www.wikidata.org/wiki/Q176814","display_name":"Partially observable Markov decision process","level":4,"score":0.2858000099658966},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.28439998626708984},{"id":"https://openalex.org/C31689143","wikidata":"https://www.wikidata.org/wiki/Q733809","display_name":"Fair-share scheduling","level":3,"score":0.2840000092983246},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2800999879837036},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2757999897003174},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.27469998598098755},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.266400009393692},{"id":"https://openalex.org/C2780682718","wikidata":"https://www.wikidata.org/wiki/Q16321575","display_name":"Production control","level":3,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21872/annual2025_5968","doi":"https://doi.org/10.21872/annual2025_5968","is_oa":false,"landing_page_url":"https://doi.org/10.21872/annual2025_5968","pdf_url":null,"source":{"id":"https://openalex.org/S7407087832","display_name":"IISE Annual Conference & Expo","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"IISE Annual Conference & Expo 2025","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7163141616","https://openalex.org/W7104178683","https://openalex.org/W4406706227","https://openalex.org/W4411406562","https://openalex.org/W4411551850","https://openalex.org/W4412980326","https://openalex.org/W4415551823","https://openalex.org/W4414239227","https://openalex.org/W4408793982","https://openalex.org/W7155700374"],"abstract_inverted_index":{"The":[0],"Flexible":[1],"Job":[2],"Shop":[3],"Scheduling":[4],"Problem":[5],"(FJSP)":[6],"is":[7,102],"an":[8],"NP-hard":[9],"optimization":[10],"challenge":[11],"with":[12,30],"significant":[13],"industrial":[14],"applications,":[15],"especially":[16],"for":[17,155,166],"large-scale":[18,54,156],"instances.":[19],"Traditional":[20],"approaches,":[21],"such":[22],"as":[23,37,60,75],"Priority":[24],"Dispatching":[25],"Rules":[26],"(PDRs),":[27],"often":[28],"struggle":[29],"time-intensive":[31],"design":[32],"processes":[33],"and":[34,64,86,144,151],"suboptimal":[35],"performance":[36],"problem":[38],"size":[39],"increases.":[40],"This":[41,159],"paper":[42],"introduces":[43],"a":[44,61,76,91,119,162],"scalable,":[45],"dynamic":[46,62],"graph-based":[47],"reinforcement":[48,107],"learning":[49,108],"framework":[50],"designed":[51],"to":[52,104,123],"address":[53],"FJSP":[55,59,168],"efficiently.":[56],"By":[57],"modeling":[58],"graph":[63],"filtering":[65],"out":[66],"irrelevant":[67],"operations,":[68],"our":[69,139],"approach":[70,160],"reformulates":[71],"the":[72,106],"scheduling":[73,130,157],"task":[74,84],"Markov":[77],"decision":[78],"process":[79],"(MDP).":[80],"To":[81],"capture":[82],"complex":[83],"dependencies":[85],"machine":[87],"constraints,":[88],"we":[89,117],"utilize":[90],"Heterogeneous":[92],"Graph":[93],"Attention":[94],"Network":[95],"(HGAT),":[96],"while":[97],"Proximal":[98],"Policy":[99],"Optimization":[100],"(PPO)":[101],"employed":[103],"drive":[105],"process,":[109],"dynamically":[110],"selecting":[111],"actions":[112],"that":[113,138],"optimize":[114],"makespan.":[115],"Additionally,":[116],"introduce":[118],"multi-action":[120],"selection":[121],"strategy":[122],"further":[124],"improve":[125],"computational":[126],"efficiency,":[127],"enabling":[128],"faster":[129],"without":[131],"compromising":[132],"solution":[133,165],"quality.":[134],"Preliminary":[135],"evaluations":[136],"demonstrate":[137],"method":[140],"surpasses":[141],"traditional":[142],"heuristics":[143],"leading":[145],"algorithms,":[146],"achieving":[147],"competitive":[148],"makespan":[149],"results":[150],"proving":[152],"its":[153],"robustness":[154],"needs.":[158],"offers":[161],"practical,":[163],"scalable":[164],"real-world":[167],"challenges.":[169]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-10-01T00:00:00"}