{"id":"W2039638040","doi":"10.1016/j.cor.2014.07.001","title":"Heuristics for dynamic and stochastic inventory-routing","year":2014,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":99,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Heuristics; Mathematical optimization; Heuristic; Time horizon; Vendor; Routing (electronic design automation); Context (archaeology); Operations research; Inventory control; Inventory management; Stochastic programming; Dynamic programming; Operations management; Algorithm; Mathematics; Artificial intelligence; Economics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001329935,0.00009512134,0.00012317,0.0001986948,0.0003992726,0.000210691,0.0001608289,0.0000574535,0.000005282601],"category_scores_gemma":[0.0006526798,0.0001052135,0.00002196997,0.000260987,0.00008087097,0.00008790102,0.00008295348,0.0002244625,0.00001044945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000846489,"about_ca_system_score_gemma":0.00002975833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006490494,"about_ca_topic_score_gemma":0.00001475218,"domain_scores_codex":[0.9989365,0.000179889,0.0001935059,0.0001956432,0.0001821891,0.0003122539],"domain_scores_gemma":[0.9988736,0.0005659543,0.000006509649,0.0002153673,0.0002344003,0.0001041952],"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":[0.000001620512,0.000007641441,0.00003144834,0.00003918941,0.00001059746,1.980303e-7,0.0003093388,0.9813403,0.0004452496,0.0037279,0.0003953765,0.01369113],"study_design_scores_gemma":[0.0002694242,0.00004487641,0.0001447369,0.00003189762,0.000004214879,0.000003294672,0.00004101335,0.9986674,0.00005246966,0.0002096164,0.0004248603,0.0001062141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03274141,0.00006946189,0.9661109,0.0001738724,0.0002380773,0.0003435745,0.000005139341,0.0001640786,0.0001534587],"genre_scores_gemma":[0.7419662,0.000008726573,0.2576967,0.00002311616,0.00009796492,0.00004751319,0.00001891312,0.00003228307,0.0001085814],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7092248,"threshold_uncertainty_score":0.4290484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05070018542644834,"score_gpt":0.3683088358235626,"score_spread":0.3176086503971142,"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."}}