{"id":"W2084084982","doi":"10.1007/s10951-007-0043-7","title":"Improving simulated annealing with variable neighborhood search to solve the resource-constrained scheduling problem","year":2007,"lang":"en","type":"article","venue":"Journal of Scheduling","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Tabu search; Variable neighborhood search; Simulated annealing; Mathematical optimization; Hill climbing; Guided Local Search; Computer science; Local search (optimization); Scheduling (production processes); Beam search; Metaheuristic; Variable (mathematics); Search 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003492246,0.0003232999,0.0004174862,0.0004535156,0.0003499702,0.0002920385,0.0004872427,0.0001791935,0.00003678554],"category_scores_gemma":[0.0003014996,0.0002321652,0.0001236324,0.001274915,0.00006485251,0.0003678839,0.00006184979,0.001169702,0.00001040533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001728133,"about_ca_system_score_gemma":0.0002029625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001476697,"about_ca_topic_score_gemma":0.000003490986,"domain_scores_codex":[0.9972396,0.00004010116,0.001005599,0.0002468957,0.0006902737,0.0007775149],"domain_scores_gemma":[0.9979221,0.0004549067,0.0002705251,0.0002934679,0.0006478081,0.0004112127],"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":[0.0001307503,0.00002616682,0.0002628716,0.00005918859,0.00015572,0.00004909863,0.001342479,0.9825495,0.007502632,0.0003205155,0.000005868872,0.007595181],"study_design_scores_gemma":[0.001285672,0.0002152057,0.00006578019,0.0004961484,0.0000910727,0.000256919,0.004170003,0.9846833,0.008178281,0.00006668456,0.0001279663,0.0003629074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2247381,0.0004442638,0.7729392,0.0002561776,0.0002490896,0.0002389174,0.000002170603,0.0001591503,0.0009729506],"genre_scores_gemma":[0.5031011,0.000007722463,0.4961869,0.0001334962,0.0004912182,0.000001118341,0.000002007984,0.00006368511,0.0000127212],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.278363,"threshold_uncertainty_score":0.9467422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01020203630421521,"score_gpt":0.2294490232960321,"score_spread":0.2192469869918169,"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."}}