{"id":"W1515177345","doi":"10.1023/a:1009621410177","title":"Solving Vehicle Routing Problems Using Constraint Programming and Metaheuristics","year":2000,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":178,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kellogg's (Canada)","funders":"European Commission","keywords":"Guided Local Search; Mathematical optimization; Tabu search; Local search (optimization); Constraint programming; Maxima and minima; Computer science; Hill climbing; Heuristics; Iterated local search; Benchmark (surveying); Local optimum; Beam search; Search algorithm; 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":[0.001015245,0.0001812076,0.0003479701,0.0001143489,0.0001317288,0.0001404421,0.0001303079,0.00009124875,0.00005202568],"category_scores_gemma":[0.0003216853,0.0001786945,0.00007169534,0.000209097,0.00008563602,0.0001640764,0.00002285728,0.0004285641,0.000002170511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008139743,"about_ca_system_score_gemma":0.00004900748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003198757,"about_ca_topic_score_gemma":4.804691e-7,"domain_scores_codex":[0.9984272,0.00008993466,0.0007865925,0.0001109973,0.0002649236,0.0003204248],"domain_scores_gemma":[0.9990805,0.0002191347,0.0002188067,0.0001232017,0.0001998761,0.000158499],"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":[0.000009850206,0.00003778525,0.003460797,0.0001668851,0.0001178888,0.00008510362,0.0007677027,0.8669691,0.003427205,0.0001842984,0.0000664462,0.1247069],"study_design_scores_gemma":[0.0004902911,0.00006868417,0.0004311892,0.0002281461,0.0001511554,0.0006687663,0.0001613983,0.9945974,0.000554718,0.0002246125,0.00218262,0.0002410258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4922488,0.001217024,0.5050896,0.00003928661,0.0003657861,0.0001486483,0.000005683218,0.0001276137,0.000757554],"genre_scores_gemma":[0.6166497,0.0001809702,0.382922,0.0000156792,0.0001737375,4.272322e-7,5.184464e-7,0.00003837526,0.00001853225],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1276283,"threshold_uncertainty_score":0.728695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02357958217797698,"score_gpt":0.2642158582860454,"score_spread":0.2406362761080685,"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."}}