{"id":"W1630809122","doi":"10.22201/icat.16656423.2009.7.01.513","title":"Linear programming embedded particle swarm optimization for solving an extended model of dynamic virtual cellular manufacturing systems","year":2009,"lang":"en","type":"article","venue":"Journal of Applied Research and Technology","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Waterloo","keywords":"Cellular manufacturing; Particle swarm optimization; Mathematical optimization; Integer programming; Linear programming; Control reconfiguration; Computer science; Workload; Cell formation; Engineering; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004599497,0.0001093382,0.0002417566,0.0003983534,0.0001095625,0.0000300661,0.000163448,0.0001626522,8.863738e-7],"category_scores_gemma":[0.00004579595,0.0001006208,0.0000240809,0.0001638396,0.00009811942,0.0001271632,0.00002460759,0.0003600322,1.602063e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005823513,"about_ca_system_score_gemma":0.0000254599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.787977e-7,"about_ca_topic_score_gemma":5.796592e-7,"domain_scores_codex":[0.9989457,0.000008169215,0.0003799456,0.0001349428,0.0002015292,0.0003297738],"domain_scores_gemma":[0.9993699,0.00005305554,0.0001185526,0.0001531429,0.000220193,0.00008512478],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008989068,0.00006227331,3.288906e-7,0.00007142968,0.00001997264,0.000003045924,0.00009615681,0.9217718,0.04443074,0.003702958,0.000002445061,0.02974901],"study_design_scores_gemma":[0.0004580754,0.0005348332,8.596214e-7,0.00002605663,0.000009656607,0.000007400275,0.0005058566,0.7907683,0.1989118,0.00869349,0.000009682373,0.00007394019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2598572,0.0001774107,0.7395312,0.00004239701,0.00003851217,0.0002424541,0.000002283258,0.00007930589,0.00002928693],"genre_scores_gemma":[0.8713605,0.000159122,0.1283951,0.000001371537,0.00003476801,0.00001265719,0.000004728246,0.00001904052,0.00001270679],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6115034,"threshold_uncertainty_score":0.4103197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02386877253568375,"score_gpt":0.2933303196371345,"score_spread":0.2694615471014508,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}