{"id":"W3121561504","doi":"","title":"Appointment Scheduling with Multiple Providers and Stochastic Service Times","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Service provider; Computer science; Overtime; Schedule; Heuristic; Scheduling (production processes); Operations research; Job shop scheduling; Markov process; Markov chain; Service system; Service (business); Mathematical optimization; Business; Artificial intelligence; Engineering; Machine learning; Marketing; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001428158,0.0001421075,0.0001903985,0.00009578108,0.000834516,0.00003638333,0.00009145593,0.0001098786,0.0001029485],"category_scores_gemma":[0.00008600511,0.0001056691,0.00002130552,0.0001865768,0.00001577146,0.0002578866,0.00003086387,0.001953772,0.000143264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006076989,"about_ca_system_score_gemma":0.0046112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003003964,"about_ca_topic_score_gemma":0.002655349,"domain_scores_codex":[0.9975154,0.0002216011,0.0003420357,0.0002197505,0.0002040984,0.001497049],"domain_scores_gemma":[0.9990646,0.0001706236,0.000178406,0.0001518531,0.0003000412,0.0001344791],"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":[0.001886036,0.0004347189,0.2238309,0.001111495,0.0009717169,0.000009401157,0.02433544,0.4620519,0.001443982,0.2441622,0.0001665773,0.0395956],"study_design_scores_gemma":[0.01637089,0.003754633,0.007170992,0.001968562,0.0002274262,0.0005963341,0.1601687,0.7786665,0.00002790504,0.02737437,0.002217231,0.001456451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8794339,0.0006941388,0.1109198,0.007249823,0.0001805539,0.001054658,0.000002126199,0.00005368795,0.0004113063],"genre_scores_gemma":[0.9922262,0.0002825561,0.004982322,0.0008204614,0.0001553711,0.0000375266,0.00001141391,0.00003272258,0.001451405],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3166146,"threshold_uncertainty_score":0.8488275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01679617007042817,"score_gpt":0.318791522590797,"score_spread":0.3019953525203689,"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."}}