{"id":"W3094839603","doi":"","title":"Data-driven distributionally robust capacitated facility location problem","year":2020,"lang":"en","type":"article","venue":"RePEc: Research Papers in Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal; Dalhousie University","funders":"","keywords":"Facility location problem; Mathematical optimization; Lagrangian relaxation; Column generation; Robust optimization; Benchmark (surveying); Relaxation (psychology); Mathematics; Ambiguity; Decision maker; Set (abstract data type); Linear programming; Optimization problem; Computer science; Operations research","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":[0.003915607,0.0001332544,0.0002643198,0.0001917136,0.0002115747,0.0002835485,0.001350366,0.0001210052,0.0002702352],"category_scores_gemma":[0.005157749,0.0001206239,0.00004666619,0.0009362889,0.0002540152,0.0006963384,0.00042888,0.0004039647,0.0002196016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002297754,"about_ca_system_score_gemma":0.0005108593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004069345,"about_ca_topic_score_gemma":0.0002184636,"domain_scores_codex":[0.9966764,0.0004161557,0.0008246585,0.0009265894,0.0007328087,0.0004233534],"domain_scores_gemma":[0.9972141,0.0007850631,0.0001796413,0.001006901,0.0005515872,0.0002626493],"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":[0.0001432854,0.0001051007,0.04361369,0.00001078957,0.00002406494,0.00001058866,0.0005227755,0.6830269,0.00008417224,0.000853434,0.004402529,0.2672027],"study_design_scores_gemma":[0.0005555399,0.00009670735,0.02462746,0.00001199311,0.000003908427,0.00000447101,0.001017745,0.7989279,0.00004984484,0.001879585,0.1725734,0.0002515028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7899292,0.0002145374,0.02779123,0.02394027,0.0006121899,0.003455556,0.008081723,0.0003027598,0.1456726],"genre_scores_gemma":[0.9929802,0.001290107,0.003298663,0.0001501614,0.00009902396,0.00003374811,0.001512019,0.00001209339,0.0006240107],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2669512,"threshold_uncertainty_score":0.617468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1976636215452192,"score_gpt":0.3841665745723868,"score_spread":0.1865029530271676,"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."}}