{"id":"W2299728052","doi":"10.1016/j.ejor.2017.01.049","title":"Mathematical optimization approaches for facility layout problems: The state-of-the-art and future research directions","year":2017,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Fundação para a Ciência e a Tecnologia; Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Conic section; Computer science; Class (philosophy); Optimization problem; Integer (computer science); Global optimization; State (computer science); Mathematics; Algorithm; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005263186,0.002731103,0.003401954,0.002420002,0.0006374625,0.005892264,0.005451682,0.0031053,0.008499015],"category_scores_gemma":[0.01093937,0.001380866,0.002853264,0.004890034,0.002438995,0.007037889,0.002474787,0.004967576,0.002534081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001923501,"about_ca_system_score_gemma":0.002229708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003532991,"about_ca_topic_score_gemma":0.003124817,"domain_scores_codex":[0.997186,0.001419946,0.0001429106,0.0004211068,0.0006639241,0.0001661884],"domain_scores_gemma":[0.9881878,0.009400312,0.0006788027,0.00047337,0.001011692,0.0002479905],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008269979,0.0003586134,0.001175303,0.003468714,0.0003897867,0.00008497473,0.0001992514,0.3561746,0.0006577781,0.3615379,0.0149835,0.2608868],"study_design_scores_gemma":[0.00002833423,0.00006783612,0.0003903444,0.0005440207,0.0000622257,0.0001028432,0.0001654544,0.6579853,0.0002657827,0.3039378,0.03639374,0.00005641522],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.003466491,0.08453292,0.8949425,0.005385755,0.0005368594,0.00005083568,0.0001546864,0.0001494648,0.01078046],"genre_scores_gemma":[0.1281424,0.2093702,0.6428962,0.00243887,0.005637628,0.0003109839,0.0007797542,0.0003695877,0.01005434],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.008499015,"threshold_uncertainty_score":0.02843207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1595511448030978,"score_gpt":0.3482414534137875,"score_spread":0.1886903086106897,"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."}}