{"id":"W2807264637","doi":"10.1007/978-3-319-93031-2_30","title":"Modelling and Solving the Senior Transportation Problem","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Mathematical optimization; Decomposition; Integer programming; Heuristic; Transportation theory; Routing (electronic design automation); Operations research; Profit (economics); Heuristics; Constraint (computer-aided design); Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005858925,0.0008294383,0.0007225988,0.0004845256,0.0005408567,0.001876134,0.001128304,0.001540005,0.009966363],"category_scores_gemma":[0.001713848,0.0004401558,0.001074633,0.001153424,0.0007170543,0.002190372,0.001342274,0.001401725,0.001545481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001078024,"about_ca_system_score_gemma":0.001448849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008225892,"about_ca_topic_score_gemma":0.006451118,"domain_scores_codex":[0.9996575,0.000121172,0.00001686465,0.00007647057,0.00006549276,0.00006240655],"domain_scores_gemma":[0.9997104,0.0001247609,0.00003627567,0.00003120119,0.00005450362,0.0000429484],"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":[0.00004178456,0.00005246599,0.0006015296,0.0001718511,0.00002803978,0.0001053959,0.0001329056,0.4917316,0.0005782932,0.437753,0.01613565,0.05266755],"study_design_scores_gemma":[0.00001080457,0.00002960578,0.0002078734,0.00003961699,0.00001054451,0.00006982916,0.00008892504,0.6693085,0.0002989862,0.301164,0.0287571,0.00001415215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02857216,0.001650806,0.8961756,0.002117958,0.0003754758,0.00005855169,0.0006710639,0.0002179347,0.07016055],"genre_scores_gemma":[0.6007746,0.004308734,0.2565726,0.0004031266,0.0004888435,0.0002550414,0.002310745,0.0003563083,0.1345299],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009966363,"threshold_uncertainty_score":0.03334081,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01759663678366322,"score_gpt":0.2364923174511141,"score_spread":0.2188956806674509,"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."}}