{"id":"W2780812725","doi":"10.1016/j.trb.2018.05.011","title":"Multiple depot vehicle scheduling with controlled trip shifting","year":2018,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"TRIPS architecture; Column generation; Scheduling (production processes); Computer science; Integer programming; Public transport; Context (archaeology); Operations research; Heuristic; Transport engineering; Vehicle routing problem; Mathematical optimization; Engineering; Mathematics; Routing (electronic design automation)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003604954,0.0001919341,0.0004026341,0.0002312766,0.0003469741,0.00005406266,0.0001906683,0.0001698399,0.0005064319],"category_scores_gemma":[0.0006737428,0.000150186,0.00009205619,0.001034037,0.0003979865,0.0001888585,0.000002583106,0.0005984991,0.00007719161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004005327,"about_ca_system_score_gemma":0.00006228439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004449702,"about_ca_topic_score_gemma":0.0008594924,"domain_scores_codex":[0.9973313,0.0004738946,0.000642322,0.0003636488,0.0006104155,0.0005784141],"domain_scores_gemma":[0.9968427,0.002054788,0.00005184089,0.0002561026,0.0006121201,0.0001824595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.01234853,0.001290041,0.3529361,0.0007067288,0.001404991,0.0003065104,0.01305398,0.1364696,0.3798592,0.06638132,0.00250979,0.03273327],"study_design_scores_gemma":[0.0167132,0.001188938,0.8512139,0.000158582,0.0001246528,0.000004453214,0.002390891,0.0465854,0.06505686,0.00189779,0.01375717,0.0009081503],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.838676,0.00004584631,0.1583531,0.0002863137,0.0001334019,0.0007090819,0.0000274564,0.0005241377,0.001244699],"genre_scores_gemma":[0.9095659,0.00001955007,0.0896327,0.00007836975,0.0001806035,0.0003039258,0.00009898905,0.00003095301,0.00008901634],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4982778,"threshold_uncertainty_score":0.6124406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3399829489445791,"score_gpt":0.4341285298538593,"score_spread":0.09414558090928021,"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."}}