{"id":"W1978976115","doi":"10.1287/trsc.1090.0262","title":"An Approximate Dynamic Programming Approach to Network Revenue Management with Customer Choice","year":2009,"lang":"en","type":"article","venue":"Transportation Science","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":225,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Mathematical optimization; Heuristics; Linear programming; Revenue management; Column generation; Computer science; Heuristic; Markov decision process; Bellman equation; Multinomial logistic regression; Affine transformation; Revenue; Operations research; Markov process; Mathematics; Economics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001885785,0.001041111,0.001494719,0.0008544901,0.0005693061,0.001817009,0.001996069,0.001436202,0.004779652],"category_scores_gemma":[0.005504318,0.0009001884,0.0008760217,0.001640271,0.001148316,0.002011775,0.001179119,0.001732594,0.0003720029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002684568,"about_ca_system_score_gemma":0.002289945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01331386,"about_ca_topic_score_gemma":0.009778366,"domain_scores_codex":[0.9986759,0.0006734658,0.00003079353,0.0001743475,0.0002652158,0.0001801519],"domain_scores_gemma":[0.9980021,0.001523205,0.0001423394,0.00009004406,0.0001509153,0.00009135134],"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":[0.00001601929,0.0000203613,0.00009435453,0.00001599651,0.000009752812,0.00001726939,0.00001359707,0.9826041,0.00006870903,0.01278364,0.0002528099,0.004103381],"study_design_scores_gemma":[0.00000418034,0.000006293299,0.00001279909,0.00000199301,0.000002061704,0.00000390202,0.000004229455,0.9937402,0.00003666201,0.006008324,0.0001776519,0.000001632643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01593851,0.0002806533,0.9779772,0.0003942056,0.00003149531,0.00009149776,0.0001081239,0.0001523578,0.005026092],"genre_scores_gemma":[0.627753,0.0006339215,0.3637281,0.0002199002,0.00009389759,0.0005189269,0.0002483618,0.0001290951,0.006674801],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01331386,"threshold_uncertainty_score":0.02647275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01442272745720435,"score_gpt":0.2521892451499171,"score_spread":0.2377665176927128,"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."}}