{"id":"W2024949764","doi":"10.1007/s10729-013-9253-z","title":"Dynamic scheduling with due dates and time windows: an application to chemotherapy patient appointment booking","year":2013,"lang":"en","type":"article","venue":"Health Care Management Science","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":102,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Sylhet Agricultural University Research System","keywords":"Computer science; Dynamic programming; Scheduling (production processes); Markov decision process; Job shop scheduling; Heuristic; Mathematical optimization; Health informatics; Dynamic priority scheduling; Markov process; Operations research; Medicine; Artificial intelligence; Algorithm; Schedule; Public health; Mathematics; Nursing","routes":{"ca_aff":true,"ca_fund":true,"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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0009540826,0.0001827532,0.0002172407,0.0002982262,0.002377059,0.0001165005,0.0002361544,0.00005237048,0.00007756153],"category_scores_gemma":[0.00002100497,0.0001521172,0.00001176435,0.0007398533,0.00009776007,0.0005642434,0.000144202,0.0001965386,0.0002011889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006917396,"about_ca_system_score_gemma":0.0003808875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006995992,"about_ca_topic_score_gemma":0.0001627588,"domain_scores_codex":[0.9974268,0.0001661024,0.000499724,0.000716463,0.0004891171,0.0007017669],"domain_scores_gemma":[0.9983553,0.00003832103,0.0002026851,0.0005307074,0.0003828157,0.0004901686],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001016143,0.0002367357,0.007881527,0.001687459,0.00003175023,0.000004280066,0.06739519,0.04631166,0.001022883,0.01114918,0.0006428477,0.8635349],"study_design_scores_gemma":[0.002552995,0.002840285,0.09851196,0.001609585,0.00002928941,0.000008419949,0.09102339,0.7852011,0.00009970868,0.0003418428,0.01654663,0.001234845],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7932277,0.000237273,0.1720004,0.01976938,0.0002121009,0.01108719,0.00001165932,0.0002861524,0.00316815],"genre_scores_gemma":[0.8287892,0.00008408861,0.1576341,0.01199314,0.00003131754,0.001221647,0.00005979508,0.00002833108,0.0001583787],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8623,"threshold_uncertainty_score":0.9989217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0116977249723783,"score_gpt":0.3482357395932508,"score_spread":0.3365380146208725,"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."}}