{"id":"W2049723152","doi":"10.5267/j.dsl.2013.04.005","title":"Scheduling stochastic two-machine flow shop problems to minimize expected makespan","year":2013,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Job shop scheduling; Mathematical optimization; Flow shop scheduling; Minification; Scheduling (production processes); Computer science; Heuristic; Stochastic process; Mathematics; Schedule; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004689111,0.0002317515,0.0002182243,0.0005951994,0.0002405812,0.0004594633,0.0006706529,0.00004702033,0.0002942797],"category_scores_gemma":[0.0005323048,0.0002065003,0.00005763127,0.001908172,0.000130481,0.0005275669,0.0001011997,0.0002075775,0.000927787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001143426,"about_ca_system_score_gemma":0.00002669496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001594212,"about_ca_topic_score_gemma":0.000002642739,"domain_scores_codex":[0.997682,0.00001712887,0.0003916474,0.0005027507,0.0008305233,0.0005759117],"domain_scores_gemma":[0.998733,0.0001987091,0.00003928172,0.0004836615,0.0001554713,0.0003898711],"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":[0.000003064572,0.00000883868,0.00006556718,0.000002476231,0.000003762785,0.000002073512,0.0004359871,0.9478954,0.03103836,0.000005896119,0.000386552,0.02015202],"study_design_scores_gemma":[0.0004289266,0.00001259101,0.0005909571,0.00006301166,0.000004363565,0.000009684894,0.0001366087,0.9973301,0.001042438,0.00004539968,0.00005173473,0.0002841752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2926759,0.0000376339,0.7047004,0.001099355,0.000792105,0.0002697125,0.000002528889,0.0003284267,0.00009391097],"genre_scores_gemma":[0.4467993,0.000001410764,0.5516944,0.001306631,0.0000873005,0.00005826392,0.000003252658,0.00002846995,0.00002100888],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1541234,"threshold_uncertainty_score":0.9998501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01372341305622452,"score_gpt":0.2477688652740083,"score_spread":0.2340454522177838,"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."}}