{"id":"W2010794153","doi":"10.1007/s00170-008-1439-z","title":"Bicriteria scheduling of a two-machine flowshop with sequence-dependent setup times","year":2008,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"Engineering and Physical Sciences Research Council","keywords":"Job shop scheduling; Simulated annealing; Mathematical optimization; Pareto optimal; Scheduling (production processes); Pareto principle; Computer science; Sequence (biology); Genetic algorithm; Multi-objective optimization; Algorithm; Mathematics; Schedule","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":[0.001011795,0.0007032211,0.001123011,0.0008065869,0.0007223131,0.0009153247,0.001078042,0.0007283661,0.002255544],"category_scores_gemma":[0.002599854,0.0005854844,0.0004269703,0.0009211172,0.0005771261,0.0006438111,0.0007510999,0.0007772353,0.000412892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008715522,"about_ca_system_score_gemma":0.001136074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003634978,"about_ca_topic_score_gemma":0.005301097,"domain_scores_codex":[0.999302,0.0002754581,0.00002863725,0.0001119917,0.0001354923,0.0001464622],"domain_scores_gemma":[0.9987305,0.0006060307,0.0002028514,0.0001326244,0.0002015808,0.0001264617],"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":[0.001459171,0.0001628724,0.0008097856,0.0001179714,0.000054466,0.0002566191,0.0001551866,0.9318342,0.01230912,0.01730402,0.001708991,0.03382753],"study_design_scores_gemma":[0.00002955129,0.00009262461,0.0002657362,0.000005760071,0.000008841687,0.00003635713,0.00001926868,0.9937855,0.001361107,0.003917641,0.0004659942,0.00001151318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2894209,0.0004306301,0.6977015,0.0003840839,0.0001541101,0.0001306922,0.0001663865,0.0003615291,0.01125021],"genre_scores_gemma":[0.8934308,0.0001181215,0.1026314,0.00005523159,0.00003676818,0.0001063264,0.0001375901,0.00007535792,0.003408241],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003634978,"threshold_uncertainty_score":0.00754559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01123895451792771,"score_gpt":0.2464225447654612,"score_spread":0.2351835902475335,"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."}}