{"id":"W3083618710","doi":"10.1016/j.eswa.2020.113959","title":"Waiting strategy for the vehicle routing problem with simultaneous pickup and delivery using genetic algorithm","year":2020,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":106,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"National Research Foundation of Korea","keywords":"Computer science; Genetic algorithm; Pickup; Routing (electronic design automation); Vehicle routing problem; Operations research; Point (geometry); Delivery Performance; Set (abstract data type); Decision maker; Artificial intelligence; Industrial engineering; Machine learning; Computer network","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":[0.001667267,0.001276082,0.00202742,0.001143238,0.0006379365,0.001485731,0.002533065,0.002459269,0.004666914],"category_scores_gemma":[0.00305635,0.000978991,0.001078369,0.001421371,0.0008792417,0.001333799,0.0008431355,0.001598711,0.0004250456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001647727,"about_ca_system_score_gemma":0.002390942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0160911,"about_ca_topic_score_gemma":0.00703777,"domain_scores_codex":[0.9993985,0.0001851846,0.00002467598,0.00009792721,0.0001364272,0.0001572949],"domain_scores_gemma":[0.9984292,0.001134745,0.0001172698,0.00003207372,0.0001847783,0.0001019378],"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":[0.0000679053,0.00005064107,0.0001487832,0.00004968229,0.00002656944,0.00004202109,0.00003193432,0.9816524,0.0005594489,0.007670757,0.0007168961,0.008982969],"study_design_scores_gemma":[0.00001093595,0.00001822454,0.00002620069,0.000002960918,0.000005445397,0.000003731208,0.00000457982,0.9984953,0.00007071105,0.001251847,0.0001069192,0.000002983666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03314631,0.0003937221,0.9611732,0.0003599099,0.00008511525,0.000103866,0.00007286629,0.0002062434,0.004458806],"genre_scores_gemma":[0.7513312,0.0006166831,0.2319515,0.0002762946,0.00009702049,0.0003835097,0.0002761665,0.0002015054,0.01486611],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0160911,"threshold_uncertainty_score":0.03199488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02939381609448962,"score_gpt":0.259616303172509,"score_spread":0.2302224870780194,"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."}}