{"id":"W2471040427","doi":"10.1111/poms.12584","title":"Scheduling Methods for Efficient Stamping Operations at an Automotive Company","year":2016,"lang":"en","type":"article","venue":"Production and Operations Management","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Job shop scheduling; Computer science; Automotive industry; Scheduling (production processes); Minification; Mathematical optimization; Time horizon; Schedule; Integer programming; Stamping; Operations research; Algorithm; Mathematics; Engineering","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":[0.001050707,0.0008936912,0.0006289995,0.0009921069,0.0005747385,0.0008181437,0.0006841485,0.0007373439,0.004782105],"category_scores_gemma":[0.001774944,0.0004931674,0.0009502328,0.00109748,0.0003495434,0.0007483724,0.0005017453,0.0006881311,0.0004841194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001142215,"about_ca_system_score_gemma":0.001935832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005483465,"about_ca_topic_score_gemma":0.005588634,"domain_scores_codex":[0.9995294,0.0001559593,0.00002656624,0.00006934281,0.00014654,0.00007218437],"domain_scores_gemma":[0.9992948,0.0003823587,0.00009242166,0.0000444989,0.0001408411,0.00004508798],"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":[0.00007823847,0.00008822347,0.0004400178,0.000254537,0.00003659415,0.00009376762,0.000127969,0.8799704,0.007977095,0.01271163,0.00178252,0.096439],"study_design_scores_gemma":[0.00002905911,0.00005487868,0.0001555672,0.00001033176,0.00001222158,0.00002898253,0.00003656897,0.9928238,0.00158825,0.002798089,0.002455264,0.000007000015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04903231,0.00061668,0.9437707,0.0002386156,0.00009155209,0.0001802469,0.00009033494,0.0004688909,0.00551068],"genre_scores_gemma":[0.3597964,0.0007231572,0.6345788,0.00006516694,0.0000809786,0.0002978681,0.00024964,0.0001375675,0.004070363],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005483465,"threshold_uncertainty_score":0.01599777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02514915830401562,"score_gpt":0.3096852936058592,"score_spread":0.2845361353018436,"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."}}