{"id":"W3108778051","doi":"10.1016/j.ejtl.2020.100024","title":"Introduction to the special issue on combining optimization and machine learning: Application in vehicle routing, network design and crew scheduling","year":2020,"lang":"en","type":"article","venue":"EURO Journal on Transportation and Logistics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; HEC Montréal","funders":"Agence Nationale de la Recherche","keywords":"Vehicle routing problem; Crew; Computer science; Crew scheduling; Scheduling (production processes); Routing (electronic design automation); Engineering; Embedded system; Aeronautics; Operations management","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001970324,0.002099518,0.002593007,0.003271267,0.000841752,0.004132914,0.001713967,0.002882243,0.04521837],"category_scores_gemma":[0.004854175,0.0006990873,0.001934213,0.003331605,0.001011585,0.003263369,0.001522301,0.005663333,0.02335609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009133527,"about_ca_system_score_gemma":0.001123025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009327391,"about_ca_topic_score_gemma":0.002133827,"domain_scores_codex":[0.9988066,0.0002078113,0.0001314717,0.0003691303,0.0004071748,0.00007786119],"domain_scores_gemma":[0.9959204,0.001921934,0.0002209352,0.0002901635,0.001165827,0.0004806597],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003948565,0.0000925723,0.0002197459,0.0006398411,0.00006722958,0.00009204683,0.00002091054,0.001481134,0.0006926211,0.006067533,0.8853824,0.1052046],"study_design_scores_gemma":[0.00001238052,0.00009686597,0.000865656,0.0003538825,0.00004632395,0.000296976,0.00002418042,0.004161607,0.0003444611,0.01163524,0.9821253,0.00003698407],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.0009014208,0.1504131,0.06497415,0.02240977,0.7217529,0.0001141947,0.0008574873,0.000583904,0.03799316],"genre_scores_gemma":[0.005355319,0.08506145,0.01903321,0.0112797,0.7651014,0.0001389888,0.001261645,0.00100964,0.1117586],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.04521837,"threshold_uncertainty_score":0.1512706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02748217703064264,"score_gpt":0.2509191413029552,"score_spread":0.2234369642723125,"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."}}