{"id":"W2769656321","doi":"10.5539/ibr.v10n12p197","title":"A bi-objective simulation-optimization approach for solving a no-wait two stages flexible flow shop scheduling problem with rework ability","year":2017,"lang":"en","type":"article","venue":"International Business Research","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Tardiness; Mathematical optimization; Job shop scheduling; Flow shop scheduling; Sorting; Computer science; Multi-objective optimization; Pareto principle; Rework; Metaheuristic; Heuristics; Genetic algorithm; Evolutionary algorithm; Scheduling (production processes); Mathematics; Algorithm; Schedule","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":[],"consensus_categories":[],"category_scores_codex":[0.001123287,0.0002306889,0.0002313741,0.0004001112,0.0008331439,0.000969602,0.0006763446,0.0001302685,0.0001162957],"category_scores_gemma":[0.001813213,0.0002154366,0.00006075908,0.0004589048,0.0001836589,0.0009084246,0.0001444335,0.0004001633,0.00001564496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003126393,"about_ca_system_score_gemma":0.000170772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007542063,"about_ca_topic_score_gemma":0.00001275774,"domain_scores_codex":[0.9977598,0.00004621536,0.0003457037,0.00051325,0.0008722659,0.0004627182],"domain_scores_gemma":[0.9944063,0.0004578549,0.0001107339,0.0005283897,0.004392129,0.0001045934],"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":[0.0001883105,0.00008538695,0.003919995,0.0001437414,0.0001111256,0.000001935698,0.0001996324,0.9930816,0.0001099212,0.000132702,0.00003225504,0.001993449],"study_design_scores_gemma":[0.001177463,0.00003110223,0.001738276,0.0001657435,0.00001049155,0.000002830573,0.0001007748,0.995918,0.0002833253,0.0002188591,0.00009691011,0.0002561853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005424849,0.00005497273,0.9858336,0.0003162335,0.0004110895,0.0008692736,0.00004847189,0.0002981919,0.006743309],"genre_scores_gemma":[0.4163865,0.00002558217,0.5822054,0.000008729101,0.0004818105,0.0002747413,0.0001630465,0.00005952953,0.0003946421],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4109617,"threshold_uncertainty_score":0.9349895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05297152091454681,"score_gpt":0.354457608274066,"score_spread":0.3014860873595192,"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."}}