{"id":"W1978402429","doi":"10.1016/j.ijpe.2011.01.003","title":"An integrated product planning model for pricing and bundle selection using Markov decision processes and data envelope analysis","year":2011,"lang":"en","type":"article","venue":"International Journal of Production Economics","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"","keywords":"Markov decision process; Bundle; Computer science; Time horizon; Envelope (radar); Mathematical optimization; Operations research; Product (mathematics); Markov chain; State space; Selection (genetic algorithm); Markov process; Markov model; Dynamic programming; Mathematics; Artificial intelligence; Algorithm; Machine learning","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":[0.0009018125,0.0001151616,0.0001742547,0.0007677006,0.0001111901,0.0002776409,0.0003088714,0.0000271169,0.00002120718],"category_scores_gemma":[0.0002898002,0.000108463,0.00002982264,0.0002025261,0.00003271411,0.003583017,0.0001223149,0.00007778682,5.216426e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007792701,"about_ca_system_score_gemma":0.00005793238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007429747,"about_ca_topic_score_gemma":0.00008889625,"domain_scores_codex":[0.9990293,0.000007223002,0.00042498,0.0003287423,0.0001072545,0.0001024652],"domain_scores_gemma":[0.9985741,0.00001639138,0.00057409,0.0001445368,0.000673799,0.0000170754],"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":[0.00290974,0.001025138,0.3091038,0.0004479828,0.003845166,0.000006270704,0.002590712,0.5188294,0.001719041,0.003069967,0.00214739,0.1543054],"study_design_scores_gemma":[0.0003700013,0.00002889359,0.003947056,0.00005214798,0.0003640364,0.00002928121,0.0005432392,0.9888561,0.0003311429,0.002274699,0.003045317,0.0001581101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7944823,0.00007839101,0.2042815,0.0002077347,0.0007140884,0.0001543462,0.000005501815,0.00001481428,0.0000613778],"genre_scores_gemma":[0.9630639,0.00008353018,0.03545462,0.0001379772,0.00115061,0.000002866653,0.00004793234,0.00001579394,0.00004276779],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4700267,"threshold_uncertainty_score":0.4422995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1014220311618837,"score_gpt":0.3050685451783738,"score_spread":0.20364651401649,"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."}}