{"id":"W3025438315","doi":"10.1080/23248378.2020.1756475","title":"Modelling and solving an integrated freight train scheduling and trip planning problem with hazardous materials","year":2020,"lang":"en","type":"article","venue":"International Journal of Rail Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Scheduling (production processes); Integer programming; Hazardous waste; Schedule; Mathematical optimization; Limiting; Linear programming; Computer science; Operations research; Job shop scheduling; Heuristic; Population; Engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001249196,0.001768539,0.00196119,0.0009319934,0.000773619,0.002130191,0.001738159,0.003231335,0.00415092],"category_scores_gemma":[0.002529482,0.001322106,0.002131535,0.001397312,0.001051441,0.001422973,0.001435678,0.002092094,0.0002905307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001827894,"about_ca_system_score_gemma":0.00405852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03972636,"about_ca_topic_score_gemma":0.02524427,"domain_scores_codex":[0.9992138,0.0002881672,0.00002923939,0.0001503624,0.0001183801,0.0001999931],"domain_scores_gemma":[0.9982083,0.001357496,0.0001882079,0.00003672346,0.000117107,0.00009209931],"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":[0.00001373904,0.00001910988,0.0001059899,0.00001841407,0.00001315984,0.00002843299,0.00001312152,0.9972837,0.00009407935,0.0013126,0.00007321074,0.001024486],"study_design_scores_gemma":[0.000009201155,0.00002336989,0.00006047905,0.000003172916,0.00001000906,0.000005579026,0.00001662717,0.9983196,0.00007610612,0.001292624,0.0001800181,0.00000324915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1337444,0.0009930372,0.851737,0.0007125509,0.0001348992,0.0002536466,0.0004585198,0.0002659933,0.01169996],"genre_scores_gemma":[0.8307694,0.001023226,0.1541667,0.0001407658,0.0001163234,0.0007648483,0.0006152639,0.0001221015,0.0122814],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03972636,"threshold_uncertainty_score":0.07899028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03328663891641936,"score_gpt":0.2804914759642945,"score_spread":0.2472048370478752,"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."}}