{"id":"https://openalex.org/W2145225561","doi":"https://doi.org/10.1109/robot.1996.506586","title":"Genetic reinforcement learning for scheduling heterogeneous machines","display_name":"Genetic reinforcement learning for scheduling heterogeneous machines","publication_year":2002,"publication_date":"2002-12-23","ids":{"openalex":"https://openalex.org/W2145225561","doi":"https://doi.org/10.1109/robot.1996.506586","mag":"2145225561"},"language":"en","primary_location":{"id":"doi:10.1109/robot.1996.506586","is_oa":false,"landing_page_url":"https://doi.org/10.1109/robot.1996.506586","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of IEEE International Conference on Robotics and Automation","raw_type":"proceedings-article"},"type":"conference-paper","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/A5066482007","display_name":"Gyumin Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"G.H. Kim","raw_affiliation_strings":["School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA","Sch. of Electr. & Comput. engineering, Purdue Univ., West Lafayette, IN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]},{"raw_affiliation_string":"Sch. of Electr. & Comput. engineering, Purdue Univ., West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111939835","display_name":"C.S.G. Lee","orcid":null},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"C.S.G. Lee","raw_affiliation_strings":["School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA","Sch. of Electr. & Comput. engineering, Purdue Univ., West Lafayette, IN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]},{"raw_affiliation_string":"Sch. of Electr. & Comput. engineering, Purdue Univ., West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I219193219"],"apc_list":null,"apc_paid":null,"fwci":1.2035,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.77631357,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"3","issue":null,"first_page":"2798","last_page":"2803"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10551","display_name":"Scheduling and Optimization Algorithms","score":0.9998000264167786,"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":0.9998000264167786,"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/T12782","display_name":"Assembly Line Balancing Optimization","score":0.996399998664856,"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":0.9527000188827515,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.8011248111724854},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.747911810874939},{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.5498625040054321},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44590699672698975},{"id":"https://openalex.org/keywords/genetic-programming","display_name":"Genetic programming","score":0.4354539215564728},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.32814687490463257},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.281681090593338},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09352925419807434}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8011248111724854},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.747911810874939},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.5498625040054321},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44590699672698975},{"id":"https://openalex.org/C110332635","wikidata":"https://www.wikidata.org/wiki/Q629498","display_name":"Genetic programming","level":2,"score":0.4354539215564728},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.32814687490463257},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.281681090593338},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09352925419807434}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/robot.1996.506586","is_oa":false,"landing_page_url":"https://doi.org/10.1109/robot.1996.506586","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of IEEE International Conference on Robotics and Automation","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","score":0.6000000238418579,"display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W32403112","https://openalex.org/W1488296983","https://openalex.org/W1491906529","https://openalex.org/W1562488420","https://openalex.org/W1600841622","https://openalex.org/W1853508273","https://openalex.org/W1979571777","https://openalex.org/W2078213402","https://openalex.org/W2097506967","https://openalex.org/W2097911714","https://openalex.org/W2130138066","https://openalex.org/W2142650416","https://openalex.org/W2145079270","https://openalex.org/W3036035835","https://openalex.org/W4232335189","https://openalex.org/W4246060548","https://openalex.org/W6629236123","https://openalex.org/W6629342597","https://openalex.org/W6634003155"],"related_works":["https://openalex.org/W4306904969","https://openalex.org/W2138720691","https://openalex.org/W4362501864","https://openalex.org/W4380318855","https://openalex.org/W2031695474","https://openalex.org/W2024136090","https://openalex.org/W2586732548","https://openalex.org/W3049728571","https://openalex.org/W2964765435","https://openalex.org/W2961374456"],"abstract_inverted_index":{"Concerns":[0],"the":[1,27,39,42,46,64,82,108,118,127,131,149,153,156,160,171,188,205],"development":[2],"of":[3,24,41,58,85,110,133,167,174,186,190,199,204],"a":[4,21,33,56,72],"learning-based":[5],"heuristic":[6],"for":[7,20,104,202],"scheduling":[8,12,67,105,162],"heterogeneous":[9,28,150],"machines.":[10],"List":[11],"methods":[13],"are":[14],"flexible":[15],"enough":[16],"to":[17,81,117,145,197],"be":[18,69,102],"used":[19],"large":[22,83],"class":[23],"problems,":[25],"including":[26],"machine":[29,151],"problem.":[30,43],"However,":[31],"designing":[32],"priority":[34,52],"rule":[35],"requires":[36],"insight":[37],"into":[38,113],"characteristics":[40],"We":[44,60],"propose":[45],"iterative":[47,65],"list":[48,66],"scheduling,":[49,152,155,158],"which":[50,91,123,169],"refines":[51],"rules":[53],"while":[54],"generating":[55],"number":[57,84],"schedules.":[59],"also":[61],"show":[62],"that":[63,198],"can":[68],"formulated":[70],"as":[71,148],"reinforcement":[73,88,111,120],"learning":[74,89,112,121],"problem,":[75],"defining":[76],"states":[77],"and":[78,159],"actions.":[79],"Due":[80],"possible":[86],"states,":[87],"algorithms":[90,115],"use":[92],"value":[93],"functions":[94],"in":[95],"constructing":[96],"an":[97],"optimal":[98],"policy":[99],"may":[100],"not":[101],"suitable":[103],"problems.":[106,163],"Encoding":[107],"policies":[109,128],"genetic":[114,119],"leads":[116],"(GRL),":[122],"directly":[124],"works":[125],"with":[126,179],"rather":[129],"than":[130],"values":[132],"states.":[134],"A":[135],"GRL-based":[136],"scheduler,":[137],"EVIS":[138,194],"(Evolutionary":[139],"Intracell":[140],"Scheduler),":[141],"has":[142,170],"been":[143],"applied":[144],"problems":[146],"such":[147],"job-shop":[154],"flow-shop":[157],"open-shop":[161],"The":[164],"proposed":[165],"model":[166],"EVIS,":[168,187],"linear":[172],"order":[173],"population-fitness":[175],"convergence,":[176],"was":[177,195],"verified":[178],"computer":[180],"experiments.":[181],"Even":[182],"without":[183],"fine":[184],"tuning":[185],"quality":[189],"solutions":[191],"found":[192],"by":[193],"comparable":[196],"problem-tailored":[200],"heuristics":[201],"most":[203],"problem":[206],"instances.":[207]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}