{"id":"W3167044988","doi":"10.1007/s10287-021-00402-y","title":"A hybrid dynamic programming - Tabu Search approach for the long-term hydropower scheduling problem","year":2021,"lang":"en","type":"article","venue":"Computational Management Science","topic":"Water resources management and optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hydro-Québec; Polytechnique Montréal","funders":"Mitacs","keywords":"Dynamic programming; Mathematical optimization; Computer science; Stochastic programming; Markov decision process; Hydropower; Hydroelectricity; Scheduling (production processes); Tabu search; Markov process; Engineering; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009184469,0.000637334,0.00125527,0.0008627545,0.0006049803,0.001157121,0.001752915,0.001631487,0.005690578],"category_scores_gemma":[0.001713275,0.0006721408,0.0007608554,0.001546864,0.0004493035,0.0008460979,0.0009240038,0.001000853,0.0005971361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009396573,"about_ca_system_score_gemma":0.002046597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01041723,"about_ca_topic_score_gemma":0.01162997,"domain_scores_codex":[0.9995822,0.0001773082,0.00001262096,0.00005641685,0.0001024528,0.00006891169],"domain_scores_gemma":[0.9993206,0.0004565034,0.00003838772,0.00002943597,0.0001045169,0.00005067295],"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":[0.0000376249,0.00004267474,0.00009614132,0.00003686931,0.00002884267,0.00002699948,0.00001268032,0.968989,0.0003428036,0.003409028,0.001049049,0.0259284],"study_design_scores_gemma":[0.00001138954,0.00001555289,0.00003052493,0.000003062324,0.000004542434,0.000005664208,0.000004573945,0.9983214,0.0000552203,0.00125578,0.0002897072,0.000002598366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03023857,0.0007403265,0.9569083,0.000423568,0.0001310329,0.000109202,0.0002052212,0.0005668052,0.01067706],"genre_scores_gemma":[0.5412199,0.0005396888,0.447087,0.0003420164,0.0001614934,0.0004804227,0.0004422136,0.0003079224,0.009419233],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01041723,"threshold_uncertainty_score":0.02071315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01350856464324632,"score_gpt":0.2466731746684196,"score_spread":0.2331646100251733,"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."}}