{"id":"W4401650586","doi":"10.1016/j.ifacol.2024.07.111","title":"Optimizing a Capacitated Vehicle Routing Problem with Scheduled Arrival, Split Deliveries within Time Windows and Emission Consideration","year":2024,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Transport Canada","funders":"","keywords":"Vehicle routing problem; Computer science; Routing (electronic design automation); Arrival time; Operations research; Transport engineering; Computer network; Engineering","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":[0.001796208,0.002180805,0.001768181,0.001270309,0.0005958306,0.002070537,0.001860323,0.002765252,0.002550426],"category_scores_gemma":[0.003243841,0.001082676,0.00137355,0.00209815,0.0009185746,0.001505556,0.001100283,0.001344896,0.0003746476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001857058,"about_ca_system_score_gemma":0.002830192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01307933,"about_ca_topic_score_gemma":0.007983144,"domain_scores_codex":[0.9987878,0.0005327159,0.00005067023,0.0002195365,0.0001392538,0.0002699135],"domain_scores_gemma":[0.9980201,0.001436757,0.0001821347,0.00006554587,0.0001699957,0.0001254771],"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":[0.00003592375,0.00002683538,0.0001484697,0.00003552355,0.0000186078,0.00005519222,0.00001111976,0.995225,0.0002492963,0.001657324,0.0001695794,0.002367177],"study_design_scores_gemma":[0.000009849187,0.00003764786,0.00008787238,0.00000373492,0.000009337224,0.00001404462,0.00001720637,0.9978484,0.0001884917,0.001617866,0.0001602949,0.000005268091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1636048,0.0009588266,0.8226417,0.0006791753,0.0001190694,0.000237457,0.0006423407,0.0003308632,0.01078574],"genre_scores_gemma":[0.8715549,0.0006823934,0.1192678,0.00008239541,0.00006892672,0.0003054646,0.0005513903,0.000130196,0.007356661],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01307933,"threshold_uncertainty_score":0.02600634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00838501993474476,"score_gpt":0.2063876695272527,"score_spread":0.1980026495925079,"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."}}