{"id":"W351801676","doi":"10.1007/978-3-319-07046-9_32","title":"A Constraint Programming-Based Column Generation Approach for Operating Room Planning and Scheduling","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal; Transport Canada","funders":"","keywords":"Computer science; Column generation; Constraint programming; Scheduling (production processes); Column (typography); Constraint logic programming; Constraint satisfaction; Mathematical optimization; Artificial intelligence; Stochastic programming; Telecommunications; 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":[0.00106981,0.001259336,0.001402334,0.001242271,0.0008361561,0.001518625,0.003083784,0.001256274,0.009496293],"category_scores_gemma":[0.002692907,0.001091384,0.001735449,0.002872935,0.0005369272,0.001164683,0.001230671,0.002253928,0.001111978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001186795,"about_ca_system_score_gemma":0.002404623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02581651,"about_ca_topic_score_gemma":0.02766611,"domain_scores_codex":[0.9990941,0.0002799133,0.00004581004,0.000135097,0.000339268,0.0001056702],"domain_scores_gemma":[0.9984144,0.0009561801,0.00007844868,0.0001118704,0.0003657551,0.00007333059],"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":[0.00008256507,0.0001489408,0.0001908859,0.0001416347,0.00005736185,0.0001059443,0.00005165746,0.8557234,0.002198723,0.01147489,0.007510066,0.1223139],"study_design_scores_gemma":[0.00001010219,0.00001177931,0.00003372604,0.000005899973,0.00000760483,0.00001179545,0.000006657183,0.9961393,0.000340899,0.002648592,0.0007774567,0.00000613172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002468676,0.000146983,0.9935548,0.0001197841,0.00005727124,0.0001173095,0.0002177544,0.0006992227,0.002618152],"genre_scores_gemma":[0.06237333,0.0002058702,0.9339846,0.0001520399,0.00005333862,0.000300478,0.0005437224,0.0002746068,0.002112012],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02581651,"threshold_uncertainty_score":0.05133247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08566073471279212,"score_gpt":0.3750879163069074,"score_spread":0.2894271815941153,"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."}}