{"id":"W4390539045","doi":"10.1287/ijoc.2022.0010","title":"A Column Generation Scheme for Distributionally Robust Multi-Item Newsvendor Problems","year":2024,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Solver; Mathematical optimization; Computer science; Ambiguity; Newsvendor model; Column generation; Linear programming; Event (particle physics); Operations research; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.003359595,0.00156606,0.001665183,0.0007169197,0.0005775157,0.00150912,0.00169306,0.00155703,0.00633542],"category_scores_gemma":[0.008521216,0.0008091192,0.001219311,0.0009915592,0.001193351,0.00188397,0.001879245,0.002878426,0.001222113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008869374,"about_ca_system_score_gemma":0.001431095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001695912,"about_ca_topic_score_gemma":0.001768718,"domain_scores_codex":[0.9983056,0.0008637681,0.00008878909,0.0002816458,0.0002846857,0.0001756028],"domain_scores_gemma":[0.9943973,0.003787857,0.0004883893,0.0004424863,0.0006056994,0.0002782575],"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":[0.0001784286,0.0001164864,0.0005637161,0.0001461353,0.00006051791,0.0001501852,0.00009930386,0.8876078,0.00184916,0.04204494,0.003921031,0.06326235],"study_design_scores_gemma":[0.00002167393,0.00003760423,0.00004220026,0.00001003987,0.00000727084,0.0000224956,0.00001156465,0.9854383,0.0004009665,0.01346738,0.0005331393,0.000007174526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004740573,0.0001539497,0.9934501,0.0001550543,0.00004038158,0.00007851359,0.00007899148,0.0001722419,0.001130156],"genre_scores_gemma":[0.3367112,0.000539268,0.6554166,0.0004865932,0.0002093955,0.0005787813,0.0008278306,0.0003065947,0.004923776],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00633542,"threshold_uncertainty_score":0.02119404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2094707748557991,"score_gpt":0.398550081017716,"score_spread":0.1890793061619169,"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."}}