{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009298541,0.0002339053,0.0002679732,0.0006305331,0.0002759344,0.0002358705,0.0004255279,0.0001048931,0.00009299366],"category_scores_gemma":[0.0001427094,0.0002254639,0.00004733949,0.003011273,0.0001358945,0.0008515706,0.00009494613,0.0002821542,0.00003089953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003084639,"about_ca_system_score_gemma":0.00007369794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004216381,"about_ca_topic_score_gemma":0.000009519728,"domain_scores_codex":[0.9978528,0.00004814949,0.0005392185,0.0005241177,0.0004622372,0.0005734911],"domain_scores_gemma":[0.9990993,0.0001194648,0.0001271,0.0003551652,0.0001640566,0.0001349419],"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":[0.000008115951,0.00001153682,0.003024737,0.000005524816,0.000003917499,0.000008243514,0.0003782568,0.9807576,0.009043079,0.000004807095,0.00002440738,0.006729771],"study_design_scores_gemma":[0.000531862,0.00003519972,0.001433866,0.0001885998,0.000007939349,0.000007753702,0.0001558739,0.9966978,0.0005669534,0.00005688373,0.00002573087,0.0002915548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4010241,0.00001480142,0.5979307,0.00001955535,0.0006567147,0.0002291608,0.00000164137,0.00006855075,0.00005478561],"genre_scores_gemma":[0.5345064,0.000007971452,0.4651203,0.0002750977,0.00004426611,0.000004683175,0.000009443041,0.000027058,0.000004663683],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1334824,"threshold_uncertainty_score":0.9194152,"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."}}