{"id":"W1994652380","doi":"10.1080/00207540412331327754","title":"A robust cell formation approach for varying product demands","year":2005,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tabu search; Cellular manufacturing; Mathematical optimization; Product (mathematics); Probabilistic logic; Heuristic; Cell formation; Computer science; Mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00113995,0.00007857456,0.00009577776,0.0004490158,0.0000889943,0.00007621426,0.0002428008,0.00003613156,0.00001304312],"category_scores_gemma":[0.000386087,0.00007252505,0.000052388,0.0001336933,0.00003223681,0.0006765134,0.00002241586,0.0002995645,0.000004824391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002592799,"about_ca_system_score_gemma":0.00003149585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.964288e-7,"about_ca_topic_score_gemma":5.944083e-7,"domain_scores_codex":[0.998744,0.0000341999,0.0003638841,0.0001233496,0.0005563332,0.0001782423],"domain_scores_gemma":[0.9983135,0.0000530231,0.0001059255,0.0001033878,0.001375931,0.00004817106],"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":[0.00006899719,0.00006657151,0.000009385055,0.00005631853,0.00002783977,8.417449e-7,0.0002181069,0.974633,0.003754864,0.0001187419,0.005106785,0.01593854],"study_design_scores_gemma":[0.0009860004,0.0001157305,0.00006265305,0.00005429093,0.00001733332,0.0002027616,0.000218325,0.6506211,0.3131302,0.001212277,0.03318078,0.0001985772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006662138,0.0003382165,0.9885939,0.000971342,0.0009670728,0.0002452116,0.000003872356,0.00004831588,0.002169876],"genre_scores_gemma":[0.8074506,0.0002631687,0.1893276,0.00000805645,0.00244042,0.00001992099,0.00002129616,0.00001855446,0.0004503536],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8007885,"threshold_uncertainty_score":0.2957486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1055623290013169,"score_gpt":0.3403410734151013,"score_spread":0.2347787444137844,"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."}}