{"id":"W2041088973","doi":"10.1016/j.cor.2014.06.007","title":"A decomposition-based heuristic for the multiple-product inventory-routing problem","year":2014,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":80,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heuristic; Vendor; Computer science; Vehicle routing problem; Operations research; Routing (electronic design automation); Integer programming; Decomposition; Time horizon; Mathematical optimization; Product (mathematics); Linear programming; Mathematics; Algorithm; Business; Artificial intelligence; Marketing","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":[0.00105106,0.001104293,0.001495829,0.001304332,0.0006570028,0.0009576424,0.001237705,0.001428938,0.00462684],"category_scores_gemma":[0.002386474,0.0007326514,0.001354165,0.001492288,0.0004843442,0.00119669,0.001187131,0.001272943,0.0006812084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001181839,"about_ca_system_score_gemma":0.001824073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00538217,"about_ca_topic_score_gemma":0.005221654,"domain_scores_codex":[0.9994962,0.0002040514,0.00001981836,0.00006222902,0.0001062926,0.0001114506],"domain_scores_gemma":[0.9990938,0.0005361947,0.00006265466,0.00006536562,0.0001523843,0.00008971374],"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":[0.0001562038,0.0001500826,0.0002966643,0.0001367387,0.00004918154,0.00007125697,0.00004572731,0.888027,0.002487735,0.008800671,0.004139221,0.09563953],"study_design_scores_gemma":[0.00003081522,0.00003512523,0.00007229099,0.00001468546,0.00001444298,0.00002076597,0.00001381495,0.9955487,0.0003167838,0.003234965,0.0006908223,0.000006763154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02137005,0.0005252888,0.9716331,0.0002361411,0.0001609546,0.0001186567,0.0001362248,0.0003597244,0.005459846],"genre_scores_gemma":[0.2185691,0.0004338551,0.7771335,0.0001895143,0.00007022074,0.0002671645,0.0004292587,0.0001845439,0.002722776],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00538217,"threshold_uncertainty_score":0.01547831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06450626381469744,"score_gpt":0.3730753622545784,"score_spread":0.308569098439881,"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."}}