{"id":"https://openalex.org/W7131786205","doi":"https://doi.org/10.1007/s44196-026-01216-z","title":"Research on the Impact of Scheduling Efficiency on Production Costs in Pharmaceutical Intelligent Manufacturing Workshops Based on Improved Particle Swarm Optimization Algorithm","display_name":"Research on the Impact of Scheduling Efficiency on Production Costs in Pharmaceutical Intelligent Manufacturing Workshops Based on Improved Particle Swarm Optimization Algorithm","publication_year":2026,"publication_date":"2026-02-27","ids":{"openalex":"https://openalex.org/W7131786205","doi":"https://doi.org/10.1007/s44196-026-01216-z"},"language":"en","primary_location":{"id":"doi:10.1007/s44196-026-01216-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44196-026-01216-z","pdf_url":null,"source":{"id":"https://openalex.org/S190680769","display_name":"International Journal of Computational Intelligence Systems","issn_l":"1875-6883","issn":["1875-6883","1875-6891"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Computational Intelligence Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1007/s44196-026-01216-z","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100413598","display_name":"Ang Li","orcid":"https://orcid.org/0000-0002-1935-4509"},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ang Li","raw_affiliation_strings":["College of Mechanical and Electrical Engineering, Beijing Information Science and Technology University, Beijing, 102200, China"],"affiliations":[{"raw_affiliation_string":"College of Mechanical and Electrical Engineering, Beijing Information Science and Technology University, Beijing, 102200, China","institution_ids":["https://openalex.org/I78675632"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5100413598"],"corresponding_institution_ids":["https://openalex.org/I78675632"],"apc_list":{"value":1390,"currency":"GBP","value_usd":1704},"apc_paid":{"value":1390,"currency":"GBP","value_usd":1704},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.63729268,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"19","issue":"1","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":0.3028999865055084,"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":0.3028999865055084,"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/T10763","display_name":"Digital Transformation in Industry","score":0.1729000061750412,"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":0.11590000241994858,"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/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.6018999814987183},{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.508400022983551},{"id":"https://openalex.org/keywords/flow-shop-scheduling","display_name":"Flow shop scheduling","score":0.42340001463890076},{"id":"https://openalex.org/keywords/job-shop-scheduling","display_name":"Job shop scheduling","score":0.4097999930381775},{"id":"https://openalex.org/keywords/pharmaceutical-manufacturing","display_name":"Pharmaceutical manufacturing","score":0.4011000096797943},{"id":"https://openalex.org/keywords/metaheuristic","display_name":"Metaheuristic","score":0.36899998784065247},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.3619999885559082},{"id":"https://openalex.org/keywords/rate-of-convergence","display_name":"Rate of convergence","score":0.3506999909877777}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6894999742507935},{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.6018999814987183},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5311999917030334},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.508400022983551},{"id":"https://openalex.org/C158336966","wikidata":"https://www.wikidata.org/wiki/Q3074426","display_name":"Flow shop scheduling","level":4,"score":0.42340001463890076},{"id":"https://openalex.org/C55416958","wikidata":"https://www.wikidata.org/wiki/Q6206757","display_name":"Job shop scheduling","level":3,"score":0.4097999930381775},{"id":"https://openalex.org/C2778938233","wikidata":"https://www.wikidata.org/wiki/Q7180757","display_name":"Pharmaceutical manufacturing","level":2,"score":0.4011000096797943},{"id":"https://openalex.org/C109718341","wikidata":"https://www.wikidata.org/wiki/Q1385229","display_name":"Metaheuristic","level":2,"score":0.36899998784065247},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3619999885559082},{"id":"https://openalex.org/C57869625","wikidata":"https://www.wikidata.org/wiki/Q1783502","display_name":"Rate of convergence","level":3,"score":0.3506999909877777},{"id":"https://openalex.org/C107568181","wikidata":"https://www.wikidata.org/wiki/Q5319000","display_name":"Dynamic priority scheduling","level":3,"score":0.3447999954223633},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.32019999623298645},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.31630000472068787},{"id":"https://openalex.org/C2776043813","wikidata":"https://www.wikidata.org/wiki/Q1759113","display_name":"Batch production","level":2,"score":0.30799999833106995},{"id":"https://openalex.org/C83469408","wikidata":"https://www.wikidata.org/wiki/Q2036525","display_name":"Taguchi methods","level":2,"score":0.2824000120162964},{"id":"https://openalex.org/C182299520","wikidata":"https://www.wikidata.org/wiki/Q1289588","display_name":"Total cost","level":2,"score":0.27889999747276306},{"id":"https://openalex.org/C13736549","wikidata":"https://www.wikidata.org/wiki/Q4489420","display_name":"Industrial engineering","level":1,"score":0.27489998936653137},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.27480000257492065},{"id":"https://openalex.org/C99862985","wikidata":"https://www.wikidata.org/wiki/Q10858068","display_name":"Production line","level":2,"score":0.2727000117301941},{"id":"https://openalex.org/C2779652045","wikidata":"https://www.wikidata.org/wiki/Q507443","display_name":"Pharmaceutical industry","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C122357587","wikidata":"https://www.wikidata.org/wiki/Q6934508","display_name":"Multi-swarm optimization","level":3,"score":0.25589999556541443},{"id":"https://openalex.org/C21880701","wikidata":"https://www.wikidata.org/wiki/Q2144042","display_name":"Process engineering","level":1,"score":0.2524999976158142}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s44196-026-01216-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44196-026-01216-z","pdf_url":null,"source":{"id":"https://openalex.org/S190680769","display_name":"International Journal of Computational Intelligence Systems","issn_l":"1875-6883","issn":["1875-6883","1875-6891"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Computational Intelligence Systems","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:9c3031e37980436faaaf93d01147397d","is_oa":true,"landing_page_url":"https://doaj.org/article/9c3031e37980436faaaf93d01147397d","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"International Journal of Computational Intelligence Systems, Vol 19, Iss 1 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1007/s44196-026-01216-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44196-026-01216-z","pdf_url":null,"source":{"id":"https://openalex.org/S190680769","display_name":"International Journal of Computational Intelligence Systems","issn_l":"1875-6883","issn":["1875-6883","1875-6891"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Computational Intelligence Systems","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.623874306678772}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W867003385","https://openalex.org/W2003746847","https://openalex.org/W2550793565","https://openalex.org/W2790368141","https://openalex.org/W2851278231","https://openalex.org/W2961578905","https://openalex.org/W2978574139","https://openalex.org/W3002719688","https://openalex.org/W3012523602","https://openalex.org/W3021534971","https://openalex.org/W3081679370","https://openalex.org/W3083951899","https://openalex.org/W3090784815","https://openalex.org/W3145485668","https://openalex.org/W4200065486","https://openalex.org/W4213283809","https://openalex.org/W4223424642","https://openalex.org/W4313420362","https://openalex.org/W4318323524","https://openalex.org/W4321240731","https://openalex.org/W4367669572","https://openalex.org/W4388904803","https://openalex.org/W4396650681","https://openalex.org/W4407876234","https://openalex.org/W4409254898","https://openalex.org/W4414077880"],"related_works":[],"abstract_inverted_index":{"The":[0,157,210],"intelligent":[1,35,253],"transformation":[2,262],"of":[3,8,136,163,183,198,212],"the":[4,66,117,161,192,196,204,264],"pharmaceutical":[5,34,113,249,265],"industry":[6],"is":[7,22,91,169],"great":[9],"significance":[10,186],"for":[11,248,259],"improving":[12],"production":[13,102,108,141],"efficiency":[14,165],"and":[15,18,61,70,88,101,138,148,154,188,195,207,233,245,255],"reducing":[16],"costs,":[17,175],"workshop":[19,37],"scheduling":[20,38,164,222],"optimization":[21,46,77,95,231],"a":[23,33,42,111,177,235],"key":[24],"approach":[25],"to":[26,251],"achieving":[27],"this":[28,213],"goal.":[29],"This":[30,240],"study":[31,214,241],"proposes":[32],"manufacturing":[36,254],"method":[39],"based":[40,105],"on":[41,106,166,173],"hybrid":[43,118,230],"particle":[44],"swarm":[45],"algorithm.":[47],"By":[48],"integrating":[49],"three":[50,217],"well-established":[51],"mechanisms":[52],"including":[53],"elite":[54],"learning":[55],"mechanism,":[56],"dynamic":[57],"inertia":[58],"weight":[59],"adjustment,":[60],"spiral":[62],"contraction":[63],"search":[64],"strategy,":[65],"algorithm\u2019s":[67,193,205],"solution":[68],"quality":[69,89],"convergence":[71],"speed":[72],"are":[73],"improved.":[74],"A":[75],"multi-objective":[76],"model":[78,223],"considering":[79],"pharmaceutical-specific":[80,225],"constraints":[81],"such":[82],"as":[83],"batch":[84],"tracing,":[85],"cleaning":[86],"validation,":[87],"inspection":[90],"constructed,":[92],"with":[93,145,176,224],"coordinated":[94],"aimed":[96],"at":[97],"minimizing":[98],"completion":[99],"time":[100,128],"costs.":[103],"Validation":[104],"actual":[107],"data":[109],"from":[110],"large":[112],"enterprise":[114],"shows":[115],"that":[116,160],"algorithm":[119],"increases":[120],"equipment":[121],"utilization":[122],"by":[123,129,143,152],"20.1%,":[124],"shortens":[125],"average":[126],"flow":[127],"18.1%,":[130],"achieves":[131],"an":[132,220,228],"on-time":[133],"delivery":[134],"rate":[135,182],"91.5%,":[137],"reduces":[139],"total":[140],"costs":[142,147,150,168],"6.3%,":[144],"energy":[146],"inventory":[149],"decreasing":[151],"14.9%":[153],"36.8%":[155],"respectively.":[156],"research":[158],"finds":[159],"impact":[162],"indirect":[167,179],"significantly":[170],"greater":[171],"than":[172],"direct":[174],"comprehensive":[178],"cost":[180],"reduction":[181],"42.3%.":[184],"Statistical":[185],"tests":[187],"ablation":[189],"studies":[190],"validate":[191],"effectiveness":[194],"contribution":[197],"each":[199],"component.":[200],"Sensitivity":[201],"analysis":[202],"validates":[203],"robustness":[206],"parameter":[208],"stability.":[209],"contributions":[211],"lie":[215],"in":[216,263],"aspects:":[218],"constructing":[219],"integrated":[221],"constraints,":[226],"developing":[227],"effective":[229],"approach,":[232],"establishing":[234],"systematic":[236],"cost-efficiency":[237],"analytical":[238],"framework.":[239],"provides":[242],"theoretical":[243],"support":[244],"practical":[246],"guidance":[247],"enterprises":[250],"implement":[252],"holds":[256],"important":[257],"value":[258],"promoting":[260],"digital":[261],"industry.":[266]},"counts_by_year":[],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2026-02-27T00:00:00"}