{"id":"W2753930878","doi":"10.1287/opre.2018.1825","title":"Exact First-Choice Product Line Optimization","year":2019,"lang":"en","type":"article","venue":"Operations Research","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Benders' decomposition; Mathematical optimization; Computer science; Exploit; Product (mathematics); Integer programming; Integer (computer science); Computation; Set (abstract data type); Product line; Optimization problem; Ranking (information retrieval); Line (geometry); Mathematics; Algorithm; Artificial intelligence; Engineering","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":[0.003033934,0.001657118,0.002913066,0.0007542842,0.0005273123,0.002072979,0.001675236,0.002054918,0.01180673],"category_scores_gemma":[0.009179996,0.001125208,0.001378677,0.001753774,0.001200869,0.002720848,0.001161623,0.002105937,0.001108184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001820659,"about_ca_system_score_gemma":0.001608156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006415522,"about_ca_topic_score_gemma":0.005666925,"domain_scores_codex":[0.997865,0.0009597228,0.00007907081,0.0004203472,0.0004198044,0.0002560822],"domain_scores_gemma":[0.9944923,0.004492013,0.0002740983,0.0002827905,0.0003022279,0.0001566368],"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":[0.0001603134,0.0001211794,0.0006223979,0.0001652096,0.00006649016,0.00007786019,0.00005202598,0.946831,0.0003470488,0.02035301,0.004275877,0.02692763],"study_design_scores_gemma":[0.00002814083,0.00003114853,0.0001233315,0.000009754155,0.000008303023,0.00001683073,0.00001998537,0.9817286,0.0001216096,0.01723357,0.0006713095,0.000007443101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05518677,0.001585438,0.9178835,0.001608075,0.0002110619,0.0002727692,0.001200492,0.0004882432,0.02156365],"genre_scores_gemma":[0.6481171,0.0008234713,0.3332677,0.0006542972,0.0001485031,0.0003701033,0.001453142,0.0002184093,0.01494728],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01180673,"threshold_uncertainty_score":0.03949744,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0720320964244561,"score_gpt":0.3290472654477125,"score_spread":0.2570151690232564,"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."}}