{"id":"https://openalex.org/W2785747086","doi":"https://doi.org/10.1109/ssci.2017.8285403","title":"Multi-objective optimization of single machine scheduling with energy consumption constraints","display_name":"Multi-objective optimization of single machine scheduling with energy consumption constraints","publication_year":2017,"publication_date":"2017-11-01","ids":{"openalex":"https://openalex.org/W2785747086","doi":"https://doi.org/10.1109/ssci.2017.8285403","mag":"2785747086"},"language":"en","primary_location":{"id":"doi:10.1109/ssci.2017.8285403","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci.2017.8285403","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Symposium Series on Computational Intelligence (SSCI)","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/A5016227654","display_name":"Xiaoya Liao","orcid":"https://orcid.org/0000-0002-9378-2886"},"institutions":[{"id":"https://openalex.org/I78757542","display_name":"University of Newcastle Australia","ror":"https://ror.org/00eae9z71","country_code":"AU","type":"education","lineage":["https://openalex.org/I78757542"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Xiaoya Liao","raw_affiliation_strings":["School of Electrical Engineering & Computing, The University of Newcastle, Callaghan, NSW, Australia"],"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering & Computing, The University of Newcastle, Callaghan, NSW, Australia","institution_ids":["https://openalex.org/I78757542"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100422055","display_name":"Rui Zhang","orcid":"https://orcid.org/0000-0002-5339-6454"},"institutions":[{"id":"https://openalex.org/I75867142","display_name":"Xiamen University of Technology","ror":"https://ror.org/01285e189","country_code":"CN","type":"education","lineage":["https://openalex.org/I75867142"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rui Zhang","raw_affiliation_strings":["School of Economics & Management, Xiamen University of Technology, Xiamen, PR China"],"affiliations":[{"raw_affiliation_string":"School of Economics & Management, Xiamen University of Technology, Xiamen, PR China","institution_ids":["https://openalex.org/I75867142"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027845071","display_name":"Raymond Chiong","orcid":"https://orcid.org/0000-0002-8285-1903"},"institutions":[{"id":"https://openalex.org/I78757542","display_name":"University of Newcastle Australia","ror":"https://ror.org/00eae9z71","country_code":"AU","type":"education","lineage":["https://openalex.org/I78757542"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Raymond Chiong","raw_affiliation_strings":["School of Electrical Engineering & Computing, The University of Newcastle, Callaghan, NSW, Australia"],"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering & Computing, The University of Newcastle, Callaghan, NSW, Australia","institution_ids":["https://openalex.org/I78757542"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5016227654"],"corresponding_institution_ids":["https://openalex.org/I78757542"],"apc_list":null,"apc_paid":null,"fwci":1.2269,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.84126676,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10551","display_name":"Scheduling and Optimization Algorithms","score":0.9994000196456909,"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":0.9994000196456909,"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9994000196456909,"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/T10791","display_name":"Advanced Control Systems Optimization","score":0.9933000206947327,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/tardiness","display_name":"Tardiness","score":0.8546620607376099},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.7461738586425781},{"id":"https://openalex.org/keywords/taguchi-methods","display_name":"Taguchi methods","score":0.7101253867149353},{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.6871725916862488},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6446420550346375},{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.5538119077682495},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.5161503553390503},{"id":"https://openalex.org/keywords/multi-objective-optimization","display_name":"Multi-objective optimization","score":0.4945838153362274},{"id":"https://openalex.org/keywords/single-machine-scheduling","display_name":"Single-machine scheduling","score":0.4327438175678253},{"id":"https://openalex.org/keywords/job-shop-scheduling","display_name":"Job shop scheduling","score":0.3810366988182068},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1918594241142273},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.15622681379318237},{"id":"https://openalex.org/keywords/schedule","display_name":"Schedule","score":0.10247069597244263},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.08811914920806885}],"concepts":[{"id":"https://openalex.org/C2778047078","wikidata":"https://www.wikidata.org/wiki/Q82299449","display_name":"Tardiness","level":4,"score":0.8546620607376099},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.7461738586425781},{"id":"https://openalex.org/C83469408","wikidata":"https://www.wikidata.org/wiki/Q2036525","display_name":"Taguchi methods","level":2,"score":0.7101253867149353},{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.6871725916862488},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6446420550346375},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.5538119077682495},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.5161503553390503},{"id":"https://openalex.org/C68781425","wikidata":"https://www.wikidata.org/wiki/Q2052203","display_name":"Multi-objective optimization","level":2,"score":0.4945838153362274},{"id":"https://openalex.org/C2780271269","wikidata":"https://www.wikidata.org/wiki/Q19295956","display_name":"Single-machine scheduling","level":4,"score":0.4327438175678253},{"id":"https://openalex.org/C55416958","wikidata":"https://www.wikidata.org/wiki/Q6206757","display_name":"Job shop scheduling","level":3,"score":0.3810366988182068},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1918594241142273},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.15622681379318237},{"id":"https://openalex.org/C68387754","wikidata":"https://www.wikidata.org/wiki/Q7271585","display_name":"Schedule","level":2,"score":0.10247069597244263},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.08811914920806885},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/ssci.2017.8285403","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci.2017.8285403","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Symposium Series on Computational Intelligence (SSCI)","raw_type":"proceedings-article"},{"id":"pmh:oai:rune.une.edu.au:1959.11/61448","is_oa":false,"landing_page_url":"https://hdl.handle.net/1959.11/61448","pdf_url":null,"source":{"id":"https://openalex.org/S7407055448","display_name":"RUNE (Research UNE)","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":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference Publication"},{"id":"pmh:oai:vitalprd00.priv:uon:33507","is_oa":false,"landing_page_url":"http://hdl.handle.net/1959.13/1393244","pdf_url":null,"source":{"id":"https://openalex.org/S4377196612","display_name":"NOVA (University of Newcastle Australia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I78757542","host_organization_name":"University of Newcastle Australia","host_organization_lineage":["https://openalex.org/I78757542"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"conference paper"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.9100000262260437,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1416338368","https://openalex.org/W1488422606","https://openalex.org/W1513595735","https://openalex.org/W1577668191","https://openalex.org/W1810830047","https://openalex.org/W1970320473","https://openalex.org/W1993084500","https://openalex.org/W2023042072","https://openalex.org/W2024983677","https://openalex.org/W2030250600","https://openalex.org/W2115230253","https://openalex.org/W2116850883","https://openalex.org/W2122967269","https://openalex.org/W2125213524","https://openalex.org/W2164087149","https://openalex.org/W2165171393","https://openalex.org/W2165708968","https://openalex.org/W2278372069","https://openalex.org/W2329354063","https://openalex.org/W2568704089","https://openalex.org/W2962202409","https://openalex.org/W3179420158","https://openalex.org/W6634715273"],"related_works":["https://openalex.org/W3097192092","https://openalex.org/W3029619930","https://openalex.org/W2061005265","https://openalex.org/W2162966494","https://openalex.org/W2017557756","https://openalex.org/W2951717752","https://openalex.org/W4251798786","https://openalex.org/W2153799489","https://openalex.org/W2381796141","https://openalex.org/W2128887355"],"abstract_inverted_index":{"A":[0,87],"bi-objective":[1],"single":[2],"machine":[3],"scheduling":[4],"problem":[5,112],"with":[6,146],"energy":[7],"consumption":[8],"constraints":[9],"is":[10,42,62,78,98,132,167],"studied,":[11],"in":[12,68],"which":[13],"the":[14,18,24,29,33,46,49,53,75,83,95,104,116,128,138,141,152,172],"objective":[15],"functions":[16],"are":[17,85],"total":[19,25],"weighted":[20,26],"completion":[21],"time":[22],"and":[23,110,160,164],"tardiness.":[27],"Given":[28],"NP-hard":[30],"nature":[31],"of":[32,52,82,89,103,140],"problem,":[34],"a":[35,123],"multi-objective":[36,125],"particle":[37],"swarm":[38],"optimization":[39,59],"(MOPSO)":[40],"algorithm":[41,76,106],"adopted":[43],"to":[44,64,70,100,122,171],"solve":[45],"problem.":[47],"Since":[48],"original":[50],"version":[51],"MOPSO":[54,84,105,154],"was":[55],"designed":[56],"for":[57,107,136],"continuous":[58],"problems,":[60],"it":[61,121],"crucial":[63],"decode":[65],"its":[66,165],"results":[67],"order":[69],"obtain":[71],"feasible":[72],"schedules.":[73],"After":[74],"framework":[77],"determined,":[79],"key":[80],"parameters":[81,102,139],"analyzed.":[86],"design":[88],"experiments":[90,145],"(DOE)":[91],"approach":[92],"based":[93],"on":[94,157],"Taguchi":[96],"method":[97],"used":[99],"optimize":[101],"both":[108,158],"small-scale":[109,159],"large-scale":[111,161],"instances.":[113],"To":[114],"assess":[115],"algorithm's":[117],"performance,":[118],"we":[119],"compare":[120],"well-known":[124],"evolutionary":[126],"algorithm,":[127],"NSGA-II.":[129,142,173],"DOE":[130],"analysis":[131],"also":[133],"carried":[134],"out":[135],"tuning":[137],"Comprehensive":[143],"computational":[144],"different":[147],"performance":[148,166],"measures":[149],"confirm":[150],"that":[151],"modified":[153],"performs":[155],"well":[156],"instances":[162],"tested,":[163],"often":[168],"superior":[169],"compared":[170]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}