{"id":"W2902906171","doi":"10.1287/inte.2018.0959","title":"Metro Uses a Simulation-Optimization Approach to Improve Fare-Collection Shift Scheduling","year":2018,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Integer programming; Scheduling (production processes); Discrete event simulation; Computer science; Operations research; Mathematical optimization; Simulation; Engineering; Mathematics; Algorithm","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007463495,0.000153877,0.0001817439,0.0004962003,0.001218879,0.0004164493,0.0001827475,0.0001534557,0.00008856366],"category_scores_gemma":[0.0002545689,0.0001374324,0.0000767263,0.001185148,0.00008624846,0.0004033253,0.000008253694,0.0002567293,0.00004480984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002285836,"about_ca_system_score_gemma":0.000228173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004582685,"about_ca_topic_score_gemma":0.00007355955,"domain_scores_codex":[0.9983385,0.00002671832,0.0004887357,0.0001888102,0.0006400191,0.0003171716],"domain_scores_gemma":[0.9987949,0.0001090487,0.0003188599,0.0001243609,0.000400561,0.0002522522],"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":[0.00009440189,0.00004878768,0.0007383068,0.000004137268,0.00003365667,3.871545e-7,0.006496057,0.9824785,0.000005308117,0.008519519,0.0000844541,0.001496495],"study_design_scores_gemma":[0.0007496339,0.0002143042,0.001366073,0.00003152827,0.00007652809,8.748121e-7,0.004391205,0.9862216,0.00009700397,0.000759677,0.005739756,0.0003518186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01681863,0.000004117675,0.9310983,0.0002419706,0.0003557685,0.0002828231,0.000004898056,0.0001113175,0.05108219],"genre_scores_gemma":[0.9130614,0.0000166291,0.08522644,0.0006487643,0.0006581506,0.000008220616,0.00002630641,0.00001768009,0.0003364235],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8962427,"threshold_uncertainty_score":0.9374753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0263144784945257,"score_gpt":0.3024653403689792,"score_spread":0.2761508618744534,"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."}}