{"id":"W4392135127","doi":"10.1016/j.eswa.2024.123561","title":"An efficient hybrid adaptive large neighborhood search method for the capacitated team orienteering problem","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval; Air Liquide (Canada)","funders":"","keywords":"Orienteering; Computer science; Mathematical optimization; Artificial intelligence; 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":[0.0007157121,0.0006051201,0.001117363,0.0004905963,0.0003826213,0.0005869522,0.001465142,0.0013021,0.003088802],"category_scores_gemma":[0.001369939,0.0003995857,0.0005162508,0.0005358821,0.0004277032,0.0007319308,0.0009273434,0.0007004334,0.0004037155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004938818,"about_ca_system_score_gemma":0.0009120383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006193301,"about_ca_topic_score_gemma":0.005477173,"domain_scores_codex":[0.9997624,0.00008722825,0.000008467551,0.00003719379,0.00007696633,0.00002772255],"domain_scores_gemma":[0.999546,0.000265518,0.00003014306,0.00002064937,0.0001059182,0.00003177984],"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":[0.00009509247,0.0000596176,0.000215402,0.00006150285,0.00003205796,0.00004377636,0.00004159475,0.930845,0.001570995,0.007779963,0.001716506,0.05753852],"study_design_scores_gemma":[0.000006159117,0.00001134099,0.00001478144,0.000001579081,0.00000163116,0.000003375526,0.000002445551,0.9993161,0.00005298011,0.0004073681,0.0001807206,0.000001467047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01309151,0.0002395284,0.9830301,0.0001063347,0.00007301567,0.00004049716,0.00002291247,0.0001209747,0.003275044],"genre_scores_gemma":[0.4767528,0.0003159608,0.5113813,0.0001859795,0.0001073692,0.0004093256,0.0001637349,0.0001646986,0.01051883],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006193301,"threshold_uncertainty_score":0.0123145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0174822844066089,"score_gpt":0.3035203629416793,"score_spread":0.2860380785350704,"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."}}