{"id":"W2919819692","doi":"10.1016/j.omega.2019.03.001","title":"Reformulation, linearization, and decomposition techniques for balanced distributed operating room scheduling","year":2019,"lang":"en","type":"article","venue":"Omega","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":48,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto General Hospital; University Health Network; University of Toronto","funders":"","keywords":"Linearization; Macro; Mathematical optimization; Scheduling (production processes); Integer programming; Computer science; Suite; Cutting-plane method; Penalty method; Linear programming; Integer (computer science); Operations research; Mathematics; Nonlinear system","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":[0.001156428,0.000978854,0.0009076409,0.0005078132,0.000408427,0.0009076016,0.0009248331,0.0004966971,0.005574234],"category_scores_gemma":[0.002953166,0.0004210466,0.0007518812,0.0007225743,0.0006285809,0.001316031,0.001326909,0.001628755,0.0008027029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007906352,"about_ca_system_score_gemma":0.001595959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006263575,"about_ca_topic_score_gemma":0.005863445,"domain_scores_codex":[0.9993825,0.0002465746,0.00002070378,0.00007583623,0.0001489521,0.0001254614],"domain_scores_gemma":[0.9991747,0.0004746563,0.00006459688,0.00007765047,0.0001683046,0.00004013666],"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":[0.0001536721,0.0001115224,0.000231826,0.0001249786,0.00002454421,0.00005012733,0.00008710794,0.8942626,0.002809742,0.04481576,0.004162315,0.05316568],"study_design_scores_gemma":[0.0000155072,0.0000220213,0.00005318258,0.000007833226,0.000005832745,0.000006453299,0.00002039956,0.9860517,0.0004441781,0.01278588,0.0005830037,0.000004012638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006265055,0.000130615,0.9904169,0.0001950807,0.00004658864,0.00002580386,0.00006558127,0.0001345162,0.002720057],"genre_scores_gemma":[0.5485407,0.0004855217,0.4410834,0.0002525899,0.0002212021,0.0002349576,0.0004348815,0.000336448,0.008410314],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006263575,"threshold_uncertainty_score":0.01864767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03017583421218678,"score_gpt":0.4026499025833016,"score_spread":0.3724740683711148,"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."}}