{"id":"W2136871443","doi":"10.1287/trsc.1060.0188","title":"A Branch-and-Cut Algorithm for a Vendor-Managed Inventory-Routing Problem","year":2007,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":426,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"","keywords":"Mathematical optimization; Stockout; Integer programming; Operations research; Vendor; Time horizon; Purchasing; Computer science; Economic order quantity; Linear programming; Set (abstract data type); Order (exchange); Vendor-managed inventory; Routing (electronic design automation); Branch and cut; Supply chain; Vehicle routing problem; Product (mathematics); Supply chain management; Mathematics; Operations management; Economics; Business","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002009491,0.000104135,0.0001138451,0.0001787241,0.0001962249,0.0000511731,0.0001487127,0.00003984186,0.000006410761],"category_scores_gemma":[0.00004412441,0.0001142856,0.00003202151,0.000723274,0.0001298066,0.0003544675,0.000002734656,0.00008608729,0.000002425478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000484565,"about_ca_system_score_gemma":0.00003312047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008994615,"about_ca_topic_score_gemma":0.00003000298,"domain_scores_codex":[0.9988627,0.000009434393,0.0002996196,0.0002384567,0.0002587336,0.0003310249],"domain_scores_gemma":[0.9995298,0.00008988586,0.0000522956,0.0001128641,0.0001053398,0.0001098468],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001990157,0.0000399215,0.01002302,0.0002790896,0.00003080353,0.000008822553,0.0129037,0.150538,0.04697737,0.00880303,0.0000505317,0.7703258],"study_design_scores_gemma":[0.0008905999,0.00003695191,0.03891905,0.000059054,0.00002722189,0.000003857834,0.0003034193,0.9332026,0.0246431,0.0008615832,0.0007227837,0.000329837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1324579,0.00003404282,0.8662319,0.00002580487,0.0001393666,0.0002873233,0.000007614211,0.0002675178,0.000548568],"genre_scores_gemma":[0.6410026,0.000005742243,0.3588545,0.00003469192,0.00002818887,0.00001550157,0.000006564468,0.00001571241,0.00003647392],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7826645,"threshold_uncertainty_score":0.4660432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02032142200990912,"score_gpt":0.2890078836574357,"score_spread":0.2686864616475266,"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."}}