{"id":"W2973467733","doi":"10.5267/j.dsl.2019.7.002","title":"Bi-objective freight scheduling optimization in an integrated forward/reverse logistic network using non-dominated sorting genetic algorithm-II","year":2019,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Optimization and Packing Problems","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sorting; Genetic algorithm; Sorting algorithm; Scheduling (production processes); Computer science; Mathematical optimization; Multi-objective optimization; Reverse logistics; Algorithm; Operations research; Mathematics; Business; Supply chain","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009530762,0.0009836752,0.00153794,0.0009459579,0.0005978819,0.001306189,0.001034521,0.001237055,0.001896985],"category_scores_gemma":[0.0008048732,0.0006643381,0.0009240298,0.001265401,0.0005170765,0.0007040107,0.0007434807,0.0008257913,0.0001877374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001405902,"about_ca_system_score_gemma":0.002341152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0191168,"about_ca_topic_score_gemma":0.01116239,"domain_scores_codex":[0.9996747,0.00009597177,0.00001317193,0.00005994441,0.00006857171,0.00008761544],"domain_scores_gemma":[0.9997136,0.0001491931,0.00003828784,0.00001285068,0.0000533479,0.0000327231],"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":[0.00003831374,0.00003858501,0.0001696548,0.00001783092,0.00002241324,0.00002053244,0.00001036267,0.9915895,0.0004204269,0.001065826,0.0002193936,0.006387219],"study_design_scores_gemma":[0.000009868698,0.0000178055,0.00004919689,0.00000241163,0.000005656247,0.000003029676,0.000004844661,0.9993055,0.00009725399,0.0004191893,0.00008334735,0.000001921494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1529934,0.0004851203,0.8378256,0.0003201609,0.00007943909,0.0001912824,0.0001473062,0.0002668397,0.007690856],"genre_scores_gemma":[0.7489427,0.0003411364,0.2416093,0.0001358241,0.00003876935,0.0003310917,0.0002428218,0.00007119631,0.00828715],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0191168,"threshold_uncertainty_score":0.03801101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01896477374702423,"score_gpt":0.2672555993696925,"score_spread":0.2482908256226683,"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."}}