{"id":"W2978059148","doi":"10.1016/j.tre.2019.09.016","title":"An exact algorithm for the inventory routing problem with logistic ratio","year":2019,"lang":"en","type":"article","venue":"Transportation Research Part E Logistics and Transportation Review","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Time horizon; Mathematical optimization; Routing (electronic design automation); Algorithm; Computer science; Function (biology); Distribution (mathematics); 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":[0.00133414,0.001495734,0.001851332,0.001236684,0.0009471199,0.001931315,0.002851304,0.001970442,0.009293293],"category_scores_gemma":[0.004300249,0.001014487,0.00140046,0.002062524,0.001126672,0.00319261,0.002276518,0.002155729,0.001904654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00186967,"about_ca_system_score_gemma":0.003554905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006399784,"about_ca_topic_score_gemma":0.004677951,"domain_scores_codex":[0.9988901,0.0002719825,0.0000616278,0.0002291271,0.0003841446,0.0001630334],"domain_scores_gemma":[0.9987973,0.0007274263,0.00007840082,0.0001680098,0.0001762739,0.00005264917],"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":[0.0002791519,0.0002514159,0.0004415621,0.0002957064,0.00007174485,0.0001116796,0.0001241366,0.567972,0.002186024,0.04903813,0.01108961,0.3681388],"study_design_scores_gemma":[0.0001771934,0.00007918031,0.0001546707,0.00002631955,0.00002659056,0.0001153976,0.00003594366,0.9571487,0.0006250246,0.03745065,0.004137165,0.00002316187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00477812,0.0004201304,0.9869354,0.0002304223,0.0001390032,0.0001118659,0.00009214279,0.0008763951,0.006416628],"genre_scores_gemma":[0.07888181,0.0005055573,0.9154876,0.0001537972,0.0001242902,0.0003024013,0.00020699,0.0002461778,0.004091347],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009293293,"threshold_uncertainty_score":0.03108913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09468338248980823,"score_gpt":0.3723578190204224,"score_spread":0.2776744365306142,"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."}}