{"id":"W2117650498","doi":"10.1287/opre.51.1.137.12796","title":"Dynamic Airline Revenue Management with Multiple Semi-Markov Demand","year":2003,"lang":"en","type":"article","venue":"Operations Research","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Revenue management; Markov decision process; Revenue; Computer science; Markov chain; Markov process; Operations research; Counterexample; Function (biology); Process (computing); Yield management; Value (mathematics); Business; Mathematical optimization; Microeconomics; Economics; Mathematics; Finance","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":[0.002631285,0.0008843935,0.00140642,0.0006776562,0.000751711,0.002172543,0.002316316,0.001354945,0.004619836],"category_scores_gemma":[0.005222583,0.001180808,0.0008918895,0.0007500426,0.001193125,0.002523464,0.001418113,0.001605859,0.0005892625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002867483,"about_ca_system_score_gemma":0.001540396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0101928,"about_ca_topic_score_gemma":0.007608346,"domain_scores_codex":[0.998106,0.0006240122,0.00008902702,0.0002945316,0.0002877206,0.0005986868],"domain_scores_gemma":[0.9943618,0.003297936,0.0008903294,0.0003665338,0.0004878249,0.0005956749],"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":[0.0002813038,0.00008284257,0.001240543,0.0000368895,0.00004024297,0.0002159554,0.00007073903,0.9661682,0.0008779786,0.02521241,0.0007522048,0.005020707],"study_design_scores_gemma":[0.00002647155,0.00002163747,0.0002405794,0.000002845224,0.000007086778,0.00002444121,0.00001797551,0.9913725,0.0001489503,0.007965284,0.0001633527,0.000009025553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5703729,0.0005057318,0.4140071,0.001499123,0.00009592149,0.0001925175,0.0009198575,0.0005975179,0.01180936],"genre_scores_gemma":[0.9910311,0.00009173049,0.006519779,0.00003439149,0.00002284278,0.00004236903,0.0001265879,0.00002004661,0.002111284],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0101928,"threshold_uncertainty_score":0.02080518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03165271470679881,"score_gpt":0.2987809569820656,"score_spread":0.2671282422752668,"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."}}