{"id":"W3086889218","doi":"10.1016/j.cie.2020.106832","title":"A new bi-objective vehicle routing-scheduling problem with cross-docking: Mathematical model and algorithms","year":2020,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Tardiness; Mathematical optimization; Vehicle routing problem; Sorting; Computer science; Integer programming; Scheduling (production processes); Genetic algorithm; Job shop scheduling; Multi-objective optimization; Pareto principle; Algorithm; Routing (electronic design automation); Mathematics","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":[0.002587488,0.002347071,0.003172764,0.002074577,0.0009529115,0.003891387,0.003740401,0.004611896,0.006866294],"category_scores_gemma":[0.003476872,0.001592627,0.002016645,0.003985763,0.001285268,0.003765511,0.003008384,0.002827064,0.0008179784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002032068,"about_ca_system_score_gemma":0.00196029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007621279,"about_ca_topic_score_gemma":0.006008367,"domain_scores_codex":[0.9986466,0.00045279,0.00005691732,0.0003174403,0.0003048319,0.0002214848],"domain_scores_gemma":[0.9984992,0.0007448187,0.0002561756,0.00008084217,0.0002198218,0.0001992239],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005382959,0.0000783842,0.0002036122,0.00009606416,0.0000477734,0.0001135706,0.0000183928,0.9816706,0.0004000626,0.009580513,0.001246952,0.00649029],"study_design_scores_gemma":[0.00001125033,0.00002182344,0.00006670568,0.000005029869,0.00001055787,0.00002357069,0.000008590649,0.9974186,0.00007205206,0.002019547,0.0003354155,0.000006762758],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02673207,0.001073639,0.9597747,0.0007014681,0.0003424403,0.0001444705,0.0003409977,0.000174982,0.01071536],"genre_scores_gemma":[0.6435158,0.001891856,0.3083197,0.0005746942,0.0004614391,0.000655612,0.001023319,0.0002973997,0.04326023],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007621279,"threshold_uncertainty_score":0.02297008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03696480631051983,"score_gpt":0.251097348667721,"score_spread":0.2141325423572012,"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."}}