{"id":"https://openalex.org/W7162118400","doi":"https://doi.org/10.48550/arxiv.2605.22773","title":"Deep Reinforcement Learning for Flexible Job Shop Scheduling with Random Job Arrivals","display_name":"Deep Reinforcement Learning for Flexible Job Shop Scheduling with Random Job Arrivals","publication_year":2026,"publication_date":"2026-05-21","ids":{"openalex":"https://openalex.org/W7162118400","doi":"https://doi.org/10.48550/arxiv.2605.22773"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.22773","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.22773","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.22773","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136754964","display_name":"Yu Tang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tang, Yu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136765546","display_name":"Muhammad Zakwan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zakwan, Muhammad","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136740511","display_name":"Efe Balta","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Balta, Efe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007359599","display_name":"John Lygeros","orcid":"https://orcid.org/0000-0002-6159-1962"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lygeros, John","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136803102","display_name":"Alisa Rupenyan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rupenyan, Alisa","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":null,"has_fulltext":false,"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/T10551","display_name":"Scheduling and Optimization Algorithms","score":0.7400000095367432,"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":0.7400000095367432,"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/T12288","display_name":"Optimization and Search Problems","score":0.0649000033736229,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10715","display_name":"Distributed and Parallel Computing Systems","score":0.02800000086426735,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/job-shop","display_name":"Job shop","score":0.6011000275611877},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5845999717712402},{"id":"https://openalex.org/keywords/job-shop-scheduling","display_name":"Job shop scheduling","score":0.5526999831199646},{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.5224999785423279},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.48330000042915344},{"id":"https://openalex.org/keywords/linear-programming","display_name":"Linear programming","score":0.4348999857902527},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4000999927520752},{"id":"https://openalex.org/keywords/job-scheduler","display_name":"Job scheduler","score":0.3617999851703644}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7050999999046326},{"id":"https://openalex.org/C2777243215","wikidata":"https://www.wikidata.org/wiki/Q1493226","display_name":"Job shop","level":5,"score":0.6011000275611877},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5845999717712402},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5703999996185303},{"id":"https://openalex.org/C55416958","wikidata":"https://www.wikidata.org/wiki/Q6206757","display_name":"Job shop scheduling","level":3,"score":0.5526999831199646},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.5224999785423279},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.48330000042915344},{"id":"https://openalex.org/C41045048","wikidata":"https://www.wikidata.org/wiki/Q202843","display_name":"Linear programming","level":2,"score":0.4348999857902527},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4000999927520752},{"id":"https://openalex.org/C111873713","wikidata":"https://www.wikidata.org/wiki/Q1641413","display_name":"Job scheduler","level":3,"score":0.3617999851703644},{"id":"https://openalex.org/C2781011336","wikidata":"https://www.wikidata.org/wiki/Q1465133","display_name":"Time limit","level":2,"score":0.3492000102996826},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.3368000090122223},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3154999911785126},{"id":"https://openalex.org/C158336966","wikidata":"https://www.wikidata.org/wiki/Q3074426","display_name":"Flow shop scheduling","level":4,"score":0.3019999861717224},{"id":"https://openalex.org/C311688","wikidata":"https://www.wikidata.org/wiki/Q2393193","display_name":"Time complexity","level":2,"score":0.2987000048160553},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.28459998965263367},{"id":"https://openalex.org/C206952183","wikidata":"https://www.wikidata.org/wiki/Q1193100","display_name":"Preemption","level":2,"score":0.2800000011920929},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.27880001068115234},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.2712000012397766},{"id":"https://openalex.org/C3017552255","wikidata":"https://www.wikidata.org/wiki/Q4135208","display_name":"Arrival time","level":2,"score":0.2696000039577484},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2669999897480011}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.22773","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.22773","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.22773","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.22773","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.6923897862434387,"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"Flexible":[1],"Job":[2],"Shop":[3],"Scheduling":[4],"Problem":[5],"(FJSP)":[6],"is":[7],"the":[8,24,31,35,63,75,80,89,97,101,123,157],"optimal":[9],"allocation":[10],"of":[11,14,27,34,84,110,122],"a":[12,108],"set":[13,109],"jobs":[15,29],"to":[16,53,73,92,104],"machines.":[17],"Two":[18],"primary":[19],"challenges":[20],"persist":[21],"in":[22],"FJSP:":[23],"unpredictable":[25],"arrival":[26,134],"future":[28],"and":[30,68,99,132,147],"combinatorial":[32],"complexity":[33],"problem,":[36],"rendering":[37],"it":[38],"intractable":[39],"for":[40,78],"conventional":[41],"mixed-integer":[42,143],"linear":[43,144],"programming":[44,145],"solvers.":[45],"This":[46],"paper":[47],"proposes":[48],"an":[49,141],"event-based":[50],"\\gls{DRL}":[51,76,118,139],"approach":[52,119],"solve":[54],"FJSP":[55],"with":[56,129],"random":[57],"job":[58,133],"arrivals.":[59],"Specifically,":[60],"we":[61],"employ":[62],"Proximal":[64],"Policy":[65],"Optimization":[66],"algorithm":[67],"use":[69],"lightweight":[70],"Multi-Layer":[71],"Perceptrons":[72],"train":[74],"agent":[77,103],"minimizing":[79],"total":[81],"completion":[82],"time":[83],"all":[85],"jobs.":[86],"We":[87,136],"design":[88],"state":[90],"representation":[91],"be":[93],"directly":[94],"accessible":[95],"from":[96,106],"environment,":[98],"limit":[100],"learning":[102],"selecting":[105],"among":[107],"well-established":[111],"dispatching":[112,125],"rules.":[113],"Simulations":[114],"show":[115,148],"that":[116,149],"our":[117,138,150],"outperforms":[120],"any":[121],"individual":[124],"rules":[126],"on":[127],"datasets":[128,158],"varying":[130],"heterogeneity":[131],"rates.":[135],"benchmark":[137],"against":[140],"arrival-triggered":[142],"solution":[146],"method":[151],"achieves":[152],"good":[153],"performance":[154],"especially":[155],"when":[156],"are":[159],"heterogeneous.":[160]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-23T00:00:00"}