{"id":"W2056557846","doi":"10.1002/atr.5670360103","title":"Application of genetic algorithm for scheduling and schedule coordination problems","year":2002,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Schedule; Computer science; Operations research; Scheduling (production processes); Profit (economics); Operator (biology); Genetic algorithm; Mathematical optimization; Job shop scheduling; Engineering; Economics; Mathematics; Machine learning; Microeconomics; Operating system","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":[0.001300811,0.000927176,0.001041874,0.001333662,0.0005662428,0.0009424296,0.0009934509,0.001463027,0.001802616],"category_scores_gemma":[0.002861273,0.0004707537,0.0005977169,0.001660135,0.000705611,0.0005708644,0.000727946,0.001011328,0.0002443735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001223886,"about_ca_system_score_gemma":0.001784121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01337114,"about_ca_topic_score_gemma":0.006060163,"domain_scores_codex":[0.9991548,0.0004604899,0.00002820805,0.00009622632,0.0001846253,0.0000756713],"domain_scores_gemma":[0.9988316,0.000847717,0.00007766705,0.00003536461,0.0001696642,0.0000379941],"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":[0.00001540904,0.00002268996,0.000220715,0.00002771859,0.00002318985,0.00002749051,0.00002320186,0.9755719,0.0002294997,0.004168781,0.0004372128,0.0192323],"study_design_scores_gemma":[0.00001205832,0.00001726483,0.00005205434,0.000005031745,0.0000055875,0.000008098486,0.000007673067,0.9968557,0.00009538815,0.002430644,0.0005082994,0.000002171507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03228929,0.0007198247,0.957617,0.0005074525,0.000107899,0.0001609894,0.0000672952,0.0003806622,0.008149561],"genre_scores_gemma":[0.4988706,0.001012295,0.4950573,0.0001993547,0.0001196422,0.000471215,0.0002592611,0.00008583696,0.003924506],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01337114,"threshold_uncertainty_score":0.02658665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01358676169243758,"score_gpt":0.2720677205323505,"score_spread":0.258480958839913,"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."}}