{"id":"W1994668406","doi":"10.1016/s0925-5273(03)00027-6","title":"A hybrid genetic algorithm for the dynamic plant layout problem","year":2003,"lang":"en","type":"article","venue":"International Journal of Production Economics","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":150,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Simulated annealing; Computer science; Heuristics; Genetic algorithm; Mathematical optimization; Algorithm; Dynamic programming; Machine learning; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006837884,0.0007202352,0.0009741748,0.0009490073,0.0004588818,0.0008349236,0.001633645,0.002049078,0.003203569],"category_scores_gemma":[0.001523685,0.0005400191,0.0006110884,0.001105002,0.0006242248,0.0007627427,0.0009402851,0.000801787,0.000448438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007344392,"about_ca_system_score_gemma":0.0009935938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006905408,"about_ca_topic_score_gemma":0.005641914,"domain_scores_codex":[0.999728,0.00008041416,0.000009146896,0.00004719368,0.00009831506,0.00003690343],"domain_scores_gemma":[0.9994635,0.0003395757,0.00003606529,0.00003119162,0.00009739653,0.00003234161],"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":[0.00006036421,0.00005312507,0.0002516296,0.00002678452,0.0000341962,0.00004260906,0.00002587416,0.9425986,0.001169832,0.003902246,0.0008334267,0.05100137],"study_design_scores_gemma":[0.00002758541,0.00002214234,0.00004470415,0.000002886943,0.000006170576,0.00001019292,0.000003549389,0.9985531,0.0001249875,0.00088744,0.000314192,0.000003041744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03154832,0.0002870854,0.961778,0.0002038715,0.00009257075,0.00006359061,0.00005708174,0.0005188966,0.005450477],"genre_scores_gemma":[0.378078,0.0002363937,0.6148921,0.0002204696,0.00007993681,0.0002677743,0.0001976183,0.0001364821,0.005891173],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006905408,"threshold_uncertainty_score":0.01373041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008190309288441113,"score_gpt":0.2124797111280781,"score_spread":0.204289401839637,"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."}}