{"id":"W1541832321","doi":"","title":"Multiobjective Optimization in Health Care Management. A metaheuristic and simulation approach.","year":2008,"lang":"en","type":"article","venue":"Algorithmic operations research","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Metaheuristic; Context (archaeology); Computer science; Mathematical optimization; Management science; Operations research; Multi-objective optimization; Quality (philosophy); Health care; Mathematics; Machine learning; Artificial intelligence; Engineering; Economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.002056968,0.0001676605,0.0002854732,0.0008779905,0.003894308,0.00004770028,0.0001350012,0.0001847809,0.00009731655],"category_scores_gemma":[0.0004289165,0.0001655662,0.00002697495,0.001413993,0.0001448238,0.0004211792,0.000120164,0.000907457,0.00005008653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007375629,"about_ca_system_score_gemma":0.0008693545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002671561,"about_ca_topic_score_gemma":0.0006198575,"domain_scores_codex":[0.9951115,0.002348779,0.0007990669,0.0005601257,0.0005211746,0.0006593714],"domain_scores_gemma":[0.9979626,0.0003904917,0.00005399017,0.0003481319,0.00105128,0.0001935397],"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":[0.00001956365,0.00009120272,0.00117699,0.0002016468,0.00001400679,0.000005952965,0.01940558,0.9741662,0.000002122971,0.001808736,0.00007988182,0.003028179],"study_design_scores_gemma":[0.001090279,0.00009549333,0.003839452,0.00008746172,0.000003949626,0.000004325886,0.01421646,0.9799133,9.438473e-7,0.0000190315,0.0005882987,0.0001409858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03947434,0.001265535,0.9436286,0.001633738,0.0002248706,0.006535706,0.00005465845,0.0001449188,0.007037645],"genre_scores_gemma":[0.7172505,0.001349372,0.2783641,0.0002193195,0.0001329436,0.0009490846,0.0003822113,0.00003670507,0.001315723],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6777762,"threshold_uncertainty_score":0.9974025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1406919612659554,"score_gpt":0.4994470119753868,"score_spread":0.3587550507094314,"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."}}