{"id":"W2109523309","doi":"10.1115/msec2015-9273","title":"Improved Bi-Level Mathematical Programming and Heuristics for the Cellular Manufacturing Facility Layout Problem","year":2015,"lang":"en","type":"article","venue":"Volume 2: Materials; Biomanufacturing; Properties, Applications and Systems; Sustainable Manufacturing","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Heuristics; Cellular manufacturing; Computer science; Mathematical optimization; Heuristic; Constraint (computer-aided design); Aisle; Nonlinear programming; Integer programming; Reduction (mathematics); Plan (archaeology); Process (computing); Page layout; Industrial engineering; Nonlinear system; Engineering; Algorithm; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001198628,0.0008674396,0.0008359742,0.0002713079,0.001141679,0.001351677,0.0005172059,0.0003620847,0.00001463266],"category_scores_gemma":[0.00008024136,0.0006575267,0.00009777321,0.0001113388,0.0003502941,0.0005035019,0.0004171561,0.0003488832,0.00001669904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004135593,"about_ca_system_score_gemma":0.00008817473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004761722,"about_ca_topic_score_gemma":0.00001174193,"domain_scores_codex":[0.9960294,0.0001012834,0.001185188,0.0009567149,0.0003993841,0.001327967],"domain_scores_gemma":[0.9978774,0.0001739868,0.0003156161,0.0009450009,0.0002466303,0.0004413622],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00136652,0.00089798,0.0002083994,0.0822262,0.002351586,0.0001177903,0.01070115,0.7374827,0.02981362,0.01066634,0.00322033,0.1209473],"study_design_scores_gemma":[0.00288914,0.0003311997,0.0003512531,0.0003195581,0.0005425717,0.0001897526,0.01066378,0.08991557,0.6801867,0.004627509,0.2071656,0.002817337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2618723,0.003141276,0.7245082,0.0001972376,0.0004634034,0.008001706,0.0003786235,0.001229186,0.0002080891],"genre_scores_gemma":[0.9825021,0.0001585416,0.01053276,0.00002281644,0.0003512414,0.003735002,0.0001697067,0.0001613278,0.002366499],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7206298,"threshold_uncertainty_score":0.999685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03040982364087604,"score_gpt":0.213383973661963,"score_spread":0.182974150021087,"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."}}