{"id":"W4307640475","doi":"10.1287/opre.2022.2361","title":"An Approximate Analysis of Dynamic Pricing, Outsourcing, and Scheduling Policies for a Multiclass Make-to-Stock Queue in the Heavy Traffic Regime","year":2022,"lang":"en","type":"article","venue":"Operations Research","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Outsourcing; Queue; Computer science; Dynamic pricing; Queueing theory; Workload; Scheduling (production processes); Mathematical optimization; Operations research; Economics; Microeconomics; Business; Mathematics","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":[0.003084676,0.001071907,0.001742206,0.0008248971,0.0007447405,0.002106974,0.001972973,0.001538681,0.003668746],"category_scores_gemma":[0.008204322,0.000740149,0.001203869,0.0007232943,0.001516561,0.002452105,0.00135906,0.002188485,0.0002314884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003233239,"about_ca_system_score_gemma":0.003149069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01459531,"about_ca_topic_score_gemma":0.005820145,"domain_scores_codex":[0.998947,0.0002931558,0.00003134202,0.0001313975,0.0002350138,0.0003620655],"domain_scores_gemma":[0.9956885,0.002942535,0.0004409229,0.0001635835,0.0004725543,0.000291848],"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":[0.0001052656,0.00007262322,0.0006103152,0.00006728456,0.00004379755,0.00006776457,0.00007550691,0.9530745,0.00128757,0.03892066,0.0008789439,0.004795808],"study_design_scores_gemma":[0.000004031898,0.00001052749,0.00005931871,0.000001814026,0.000004482697,0.000004328525,0.000007741175,0.9980337,0.00006271881,0.001739019,0.00006928085,0.000002972604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1395053,0.001406465,0.8475398,0.001654948,0.0001837164,0.0001063954,0.0001089188,0.0002280677,0.009266326],"genre_scores_gemma":[0.9708769,0.000771835,0.02230911,0.0001785097,0.0001171063,0.00005832395,0.00006708194,0.00006262034,0.005558579],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01459531,"threshold_uncertainty_score":0.02902073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06807434725239911,"score_gpt":0.3653375569868442,"score_spread":0.2972632097344451,"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."}}