{"id":"W2516687230","doi":"10.1016/j.ejor.2016.08.024","title":"Propagating logic-based Benders’ decomposition approaches for distributed operating room scheduling","year":2016,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":105,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto General Hospital; University Health Network; University of Toronto","funders":"","keywords":"Benders' decomposition; Knapsack problem; Scheduling (production processes); Computer science; Time horizon; Integer programming; Schedule; Mathematical optimization; Algorithm; 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":[0.003683365,0.001343029,0.001368528,0.001348347,0.001011658,0.002124897,0.002435187,0.001272735,0.006406449],"category_scores_gemma":[0.005891172,0.001174901,0.002156146,0.001352314,0.001041343,0.002494992,0.002354345,0.002516759,0.0007524631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001769172,"about_ca_system_score_gemma":0.002467738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006741597,"about_ca_topic_score_gemma":0.009332403,"domain_scores_codex":[0.9981495,0.0007197059,0.0001071156,0.0002524276,0.0004701393,0.0003012098],"domain_scores_gemma":[0.9969876,0.001839509,0.0002167278,0.0002970987,0.0004980965,0.0001608319],"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":[0.0001580451,0.0001118604,0.0002459588,0.0000903547,0.00006472723,0.00004610674,0.0001144023,0.9123184,0.001803597,0.03853657,0.001600436,0.04490959],"study_design_scores_gemma":[0.00001392692,0.00001829265,0.00002816081,0.000007308576,0.00001215064,0.000004973181,0.00001643433,0.9810734,0.0003008596,0.01815731,0.000362337,0.00000486922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005077006,0.00007251104,0.9930481,0.0001171706,0.00003624458,0.00004378631,0.00006033287,0.0001765653,0.001368228],"genre_scores_gemma":[0.2466295,0.0002686725,0.7467524,0.000221778,0.00009752758,0.0001989519,0.0004494568,0.0002792627,0.005102534],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006741597,"threshold_uncertainty_score":0.02143174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4022236977940679,"score_gpt":0.5037765717942564,"score_spread":0.1015528740001885,"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."}}