{"id":"W2605057693","doi":"10.1016/j.cie.2017.04.002","title":"An effective and efficient heuristic for no-wait flow shop production to minimize total completion time","year":2017,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Agency for Healthcare Research and Quality; University of Calgary; University of Kentucky","keywords":"Heuristics; Flow shop scheduling; Computation; Mathematical optimization; Computer science; Idle; Scheduling (production processes); Heuristic; Minification; Job shop scheduling; Mathematics; Algorithm; Schedule","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":[0.0009353318,0.0008671903,0.001490277,0.0008198579,0.0008278074,0.0009727353,0.001515899,0.001046681,0.00214704],"category_scores_gemma":[0.001733402,0.0005940355,0.0007399578,0.0007368364,0.0006521151,0.0009272313,0.0007154894,0.0006242329,0.0002277845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008929584,"about_ca_system_score_gemma":0.002746222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004494037,"about_ca_topic_score_gemma":0.005646152,"domain_scores_codex":[0.9994466,0.000159487,0.00002358087,0.00007849803,0.0001623398,0.0001294873],"domain_scores_gemma":[0.9993377,0.0003852857,0.00006314764,0.00004264812,0.0001079502,0.00006322079],"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":[0.0003953989,0.0002888954,0.0003609187,0.0002829855,0.00005061241,0.0001361466,0.00007601953,0.8932749,0.007480132,0.006476769,0.00253191,0.08864528],"study_design_scores_gemma":[0.00008848892,0.000136549,0.0001592594,0.000008965866,0.00002709209,0.00002511925,0.00001456231,0.9952011,0.001561743,0.002101494,0.0006644767,0.00001124784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1005862,0.0008170336,0.8886559,0.0003431938,0.0003495266,0.0003469566,0.0001234938,0.0009856209,0.007792071],"genre_scores_gemma":[0.7083747,0.0002545058,0.2870945,0.000142637,0.00007657135,0.0002558056,0.0001433892,0.000105635,0.00355228],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004494037,"threshold_uncertainty_score":0.00893575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01478704902265844,"score_gpt":0.2233551509942481,"score_spread":0.2085681019715897,"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."}}