{"id":"W2070637511","doi":"10.1007/s10489-006-6926-z","title":"Multi-Objective Genetic Algorithms for Vehicle Routing Problem with Time Windows","year":2006,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":510,"is_retracted":false,"has_abstract":false,"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vehicle routing problem; Benchmark (surveying); Computer science; Set (abstract data type); Pareto principle; Mathematical optimization; Genetic algorithm; Extension (predicate logic); Ranking (information retrieval); Multi-objective optimization; Routing (electronic design automation); Algorithm; Mathematics; Artificial intelligence","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":[0.00135668,0.0009050563,0.001000009,0.0008686719,0.0003910174,0.0008595379,0.001169374,0.001478309,0.001461781],"category_scores_gemma":[0.002717255,0.0005728148,0.0007285036,0.001194851,0.0005284584,0.001095934,0.0006886353,0.001183992,0.0001843571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001053405,"about_ca_system_score_gemma":0.001034193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006558589,"about_ca_topic_score_gemma":0.004396129,"domain_scores_codex":[0.9996213,0.0001642523,0.00001584932,0.00005376617,0.00009953821,0.00004528891],"domain_scores_gemma":[0.9991801,0.0005960017,0.0000835316,0.00002649733,0.00008545754,0.00002847049],"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":[0.00002724512,0.00002301454,0.00009112348,0.00002262306,0.00002332644,0.00001333753,0.00001527527,0.9799255,0.0003079183,0.004330155,0.0002264353,0.01499414],"study_design_scores_gemma":[0.000007221969,0.00001088752,0.000027032,0.000003079813,0.000005179429,0.000002934247,0.000002558517,0.9981552,0.00009255905,0.00156403,0.000127857,0.000001535105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02364123,0.0008109584,0.9725008,0.0002189972,0.00006182963,0.00004207285,0.00003486887,0.0001104216,0.002578751],"genre_scores_gemma":[0.5437388,0.001035542,0.4491156,0.0001380981,0.000104541,0.0003089215,0.0001227472,0.00009396898,0.005341719],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006558589,"threshold_uncertainty_score":0.01304084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01597300508623297,"score_gpt":0.2513967951754557,"score_spread":0.2354237900892227,"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."}}