{"id":"W3197924939","doi":"10.1080/00207543.2021.1967500","title":"Robust facility layout design for flexible manufacturing: a doe-based heuristic","year":2021,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Page layout; Tabu search; Genetic algorithm; Computer science; Mathematical optimization; Product (mathematics); Heuristic; Point (geometry); Industrial engineering; Engineering; Algorithm; Mathematics","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":[0.001126757,0.00009579933,0.0001328947,0.0003041328,0.00009066639,0.0001012999,0.000236883,0.00005588368,0.00008807839],"category_scores_gemma":[0.001455153,0.00009271245,0.00007717909,0.0001101495,0.00005668022,0.0001834121,0.00002629269,0.0003649175,0.00001085879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002762441,"about_ca_system_score_gemma":0.0001597076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002306305,"about_ca_topic_score_gemma":0.000001730579,"domain_scores_codex":[0.9985214,0.00009762187,0.0003442232,0.0001768775,0.0006496696,0.00021019],"domain_scores_gemma":[0.9975301,0.0002719054,0.00007625532,0.0001561693,0.001890158,0.00007540864],"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":[0.000156306,0.00006525942,0.00002151658,0.00005173854,0.00006977705,0.00002793214,0.00004471952,0.9883652,0.00178216,0.00008383758,0.004973323,0.004358209],"study_design_scores_gemma":[0.001127237,0.0001593987,0.0005481472,0.0001104185,0.00002328148,0.0001670125,0.000173545,0.09279498,0.8613625,0.007199057,0.03610333,0.0002310474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008468698,0.0001809302,0.9876249,0.001310662,0.002000962,0.0001601083,0.00002457075,0.00006486425,0.0001643326],"genre_scores_gemma":[0.903972,0.0001009044,0.09402912,0.00002228719,0.0008442851,0.00002036173,0.00003949794,0.00002126074,0.0009503083],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8955702,"threshold_uncertainty_score":0.3780704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2086586568965277,"score_gpt":0.3728293786653843,"score_spread":0.1641707217688567,"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."}}