{"id":"W2088165054","doi":"10.1016/j.disopt.2009.04.004","title":"Local search intensified: Very large-scale variable neighborhood search for the multi-resource generalized assignment problem","year":2009,"lang":"en","type":"article","venue":"Discrete Optimization","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Heuristics; Heuristic; Benchmark (surveying); Mathematical optimization; Variable neighborhood search; Mathematics; Local search (optimization); Variable (mathematics); Scale (ratio); Incremental heuristic search; Beam search; Algorithm; Search algorithm; Computer science; Metaheuristic","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":[0.001268183,0.0004891827,0.001100804,0.0003947146,0.0003181224,0.0006698811,0.001298691,0.0009909996,0.002547392],"category_scores_gemma":[0.003043305,0.0003452066,0.0004712148,0.0005862466,0.0006437862,0.001134277,0.001207967,0.001122974,0.0002684759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005437686,"about_ca_system_score_gemma":0.0006434228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002282237,"about_ca_topic_score_gemma":0.002897135,"domain_scores_codex":[0.9996009,0.0002289536,0.00001040435,0.00004095149,0.00008543257,0.00003341104],"domain_scores_gemma":[0.999306,0.0004457675,0.00004905074,0.00007846679,0.00008571293,0.00003496264],"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":[0.0001195456,0.00007553633,0.0002640191,0.00007634157,0.00003640832,0.00005064513,0.00005192422,0.9273531,0.001157863,0.02025425,0.002718127,0.04784226],"study_design_scores_gemma":[0.00001587738,0.00001847466,0.0000363319,0.000003315518,0.000003576301,0.000006299374,0.000004341279,0.996494,0.0001225662,0.003068461,0.0002250047,0.00000184437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0313113,0.0004066845,0.9636924,0.0002229319,0.00005531803,0.00004651879,0.00004892509,0.0002503719,0.003965503],"genre_scores_gemma":[0.6736957,0.0002778553,0.3203297,0.0002043533,0.00008212448,0.000261501,0.0001490282,0.000148044,0.004851568],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002547392,"threshold_uncertainty_score":0.008521855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02201980069949839,"score_gpt":0.2781955289469787,"score_spread":0.2561757282474803,"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."}}