{"id":"W2975579666","doi":"10.1016/j.orl.2019.09.009","title":"Assortment optimization under the multinomial logit model with product synergies","year":2019,"lang":"en","type":"article","venue":"Operations Research Letters","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Cornell University","keywords":"Attractiveness; Mathematical optimization; Product (mathematics); Combinatorics; Treewidth; Computer science; Mathematics; Path (computing); Graph; Pathwidth","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006123065,0.001829948,0.004823233,0.002286271,0.001160621,0.005051138,0.002621167,0.003845171,0.0161404],"category_scores_gemma":[0.01087455,0.0024468,0.002570166,0.003767094,0.001832596,0.005864604,0.002736792,0.002347653,0.00144425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00372713,"about_ca_system_score_gemma":0.002990434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02135779,"about_ca_topic_score_gemma":0.01555555,"domain_scores_codex":[0.9964599,0.00186124,0.0001572176,0.0005293344,0.0003487615,0.0006434587],"domain_scores_gemma":[0.9913787,0.006679506,0.0008606591,0.0002489698,0.0004333063,0.0003988953],"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":[0.0001365067,0.00006742212,0.0005232837,0.00006934376,0.00006751317,0.0001113381,0.00003359323,0.9728901,0.0001637746,0.02211907,0.0005868603,0.003231107],"study_design_scores_gemma":[0.00001841901,0.00003526751,0.0002590294,0.000007603843,0.00002185937,0.00002091186,0.00002458414,0.9868709,0.0000590934,0.01241854,0.0002441739,0.00001958518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2646248,0.002385868,0.6919902,0.004362924,0.0003027144,0.0003623764,0.002350867,0.0007697605,0.03285049],"genre_scores_gemma":[0.9154918,0.0008520483,0.0291248,0.0002128998,0.0001039515,0.0002402137,0.000568643,0.0001835209,0.05322198],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02135779,"threshold_uncertainty_score":0.05399501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05254429056906057,"score_gpt":0.2816712590283065,"score_spread":0.2291269684592459,"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."}}