{"id":"W106072245","doi":"10.1007/978-3-319-13132-0_9","title":"Investigating Metaheuristics Applications for Capacitated Location Allocation Problem on Logistics Networks","year":2014,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Metaheuristic; Tabu search; Computer science; Simulated annealing; Operations research; Ant colony optimization algorithms; Facility location problem; Genetic algorithm; Service (business); Service provider; Revenue; Mathematical optimization; Business; Engineering; Artificial intelligence; 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":[0.0006782279,0.00106544,0.0005854598,0.0008186856,0.0003798986,0.001893409,0.001202387,0.001374495,0.004308765],"category_scores_gemma":[0.001809055,0.0004094533,0.001019405,0.002615773,0.0004111128,0.001419936,0.0008035792,0.001551371,0.0006061687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001098937,"about_ca_system_score_gemma":0.0006147769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002781063,"about_ca_topic_score_gemma":0.002408809,"domain_scores_codex":[0.9996948,0.0001426774,0.000009062215,0.0000375362,0.00007428937,0.00004164661],"domain_scores_gemma":[0.9995099,0.0003612432,0.00002425818,0.00002485956,0.00006328905,0.00001642975],"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":[0.00009101244,0.0001680964,0.0005491588,0.0005360143,0.00009804586,0.0001611064,0.0001423569,0.6818177,0.003052685,0.1410782,0.01109358,0.1612122],"study_design_scores_gemma":[0.00001019483,0.00005078764,0.0002518288,0.00009281453,0.00002615885,0.00005882229,0.0001043358,0.9386089,0.001169156,0.04453841,0.01507842,0.00001013537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06115718,0.02864512,0.7601236,0.002239305,0.0007597005,0.0001512447,0.0001813997,0.0003121492,0.1464304],"genre_scores_gemma":[0.4583476,0.02922538,0.4543943,0.0005793446,0.000749867,0.0002550188,0.0004314857,0.000367431,0.05564956],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004308765,"threshold_uncertainty_score":0.01441425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1222328767096315,"score_gpt":0.3550664046330425,"score_spread":0.2328335279234109,"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."}}