{"id":"W2076332856","doi":"10.1016/j.tre.2013.06.001","title":"An heuristic search for the routing of heterogeneous trucks with single and double container loads","year":2013,"lang":"en","type":"article","venue":"Transportation Research Part E Logistics and Transportation Review","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":67,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Truck; Container (type theory); Metaheuristic; Routing (electronic design automation); Computer science; Heuristic; Port (circuit theory); Vehicle routing problem; Mathematical optimization; Operations research; Engineering; Computer network; Algorithm; Mathematics; Automotive engineering; Artificial intelligence","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":[0.001075443,0.0001750951,0.0003335802,0.00007920459,0.0001928393,0.00006412791,0.0001241182,0.00006944665,0.00006960062],"category_scores_gemma":[0.00002568781,0.0001279743,0.0000438441,0.0003237614,0.0002608402,0.0001510954,0.000001111622,0.0002092871,0.000001234963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001480919,"about_ca_system_score_gemma":0.00003607364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002145111,"about_ca_topic_score_gemma":0.0003256719,"domain_scores_codex":[0.9983567,0.00009869521,0.0005807236,0.0002656091,0.000372965,0.0003252918],"domain_scores_gemma":[0.9983182,0.0005713561,0.00007891518,0.0002317674,0.0006536352,0.0001461059],"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":[0.0003517185,0.0003225425,0.04176904,0.02463264,0.0004875116,0.00002742569,0.003561273,0.817708,0.003389883,0.03345794,0.0003981057,0.07389389],"study_design_scores_gemma":[0.009940036,0.003475168,0.2417648,0.00683636,0.001712109,0.00002335181,0.002119068,0.7075835,0.01142572,0.001591932,0.01139569,0.002132308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1820315,0.01673686,0.7960624,0.0005192876,0.00007189222,0.004057277,0.0002562072,0.000154846,0.0001097222],"genre_scores_gemma":[0.9775578,0.01243713,0.009250665,0.0000553196,0.00002734321,0.0003239251,0.0002808673,0.00004364219,0.00002335571],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7955263,"threshold_uncertainty_score":0.5218639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09280986466183076,"score_gpt":0.3560358834065543,"score_spread":0.2632260187447235,"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."}}