{"id":"W3009064098","doi":"10.1016/j.omega.2020.102304","title":"Inventory routing under stochastic supply and demand","year":2020,"lang":"en","type":"article","venue":"Omega","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Supply chain; Computer science; Context (archaeology); Stochastic programming; Reservation; Operations research; Mathematical optimization; Routing (electronic design automation); Benchmark (surveying); Inventory theory; Heuristic; Set (abstract data type); Inventory control; Mathematics; Business; Artificial intelligence","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":[0.0001216162,0.00008544396,0.0001034575,0.00002806105,0.00004215732,0.00003351998,0.0000547468,0.00004381852,0.00003009894],"category_scores_gemma":[0.00008536461,0.00009397302,0.00001650705,0.0001318317,0.00001899956,0.00007286989,0.00003191949,0.0001100138,0.00001842277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002052985,"about_ca_system_score_gemma":0.000007507251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000016671,"about_ca_topic_score_gemma":6.89318e-7,"domain_scores_codex":[0.9995129,0.00002838888,0.0001180783,0.0001180163,0.00007612172,0.0001465537],"domain_scores_gemma":[0.9997361,0.0000545967,0.00001542084,0.00007119233,0.00001214287,0.0001106051],"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":[0.000002624913,0.000002535277,0.002238681,0.00004174405,0.00001518453,0.000001520898,0.0008061677,0.992066,0.001640498,0.0006742665,0.0004115479,0.002099181],"study_design_scores_gemma":[0.0003107059,0.00001499872,0.002894833,0.00001867768,0.00001410724,0.000004463485,0.0001183631,0.9955819,0.0003784216,0.0001284499,0.0003853763,0.0001497215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2003963,0.0003283725,0.7969544,0.0004099529,0.0001102885,0.00008018909,0.000002243092,0.0003676025,0.001350611],"genre_scores_gemma":[0.982213,0.000008731831,0.01732349,0.0002776139,0.00009585442,0.000003398155,0.000002595605,0.00003026029,0.00004502297],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7818167,"threshold_uncertainty_score":0.3832109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02333842429590025,"score_gpt":0.2443219923711894,"score_spread":0.2209835680752892,"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."}}