{"id":"W4404864841","doi":"10.1080/01605682.2024.2432605","title":"Adaptive large neighbourhood search for the multi-depot arc routing problem with flexible assignment of end depot and different arc types","year":2024,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Group for Research in Decision Analysis; Université du Québec à Trois-Rivières","funders":"Mitacs","keywords":"Depot; Arc (geometry); Arc routing; Neighbourhood (mathematics); Computer science; Operations research; Routing (electronic design automation); Mathematical optimization; Computer network; Engineering; Mathematics; Geography; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004133506,0.0001216531,0.0001798556,0.00005458612,0.0004240148,0.0002131005,0.000280689,0.00006146758,0.00004108665],"category_scores_gemma":[0.0001925702,0.00006124572,0.0001524066,0.0002947411,0.0001371012,0.0001723744,0.0001335844,0.0007597986,6.182137e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002241621,"about_ca_system_score_gemma":0.0003063986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009938138,"about_ca_topic_score_gemma":0.000008566193,"domain_scores_codex":[0.9979266,0.0002704388,0.0003486506,0.0001354546,0.0009992775,0.0003196134],"domain_scores_gemma":[0.9975795,0.001592353,0.00005690312,0.0001357427,0.0005544784,0.00008102305],"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":[0.0001161961,0.0001506984,0.003578903,0.0003446254,0.001082479,0.000002653859,0.005883439,0.946239,0.02382451,0.01147677,0.001261952,0.006038765],"study_design_scores_gemma":[0.0006610666,0.0001863566,0.004798242,0.0002831045,0.00004028324,0.00002663718,0.001334068,0.9742039,0.01792596,0.0002160198,0.0002407835,0.00008358171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06996908,0.001968214,0.9230626,0.003640574,0.00015745,0.0008567155,0.00005489189,0.00002894343,0.0002615215],"genre_scores_gemma":[0.9074079,0.0002649965,0.09176348,0.00002841977,0.0001717943,0.000027682,0.000001570158,0.00003204163,0.0003020694],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8374389,"threshold_uncertainty_score":0.3300989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07232362554463277,"score_gpt":0.3580735006696667,"score_spread":0.2857498751250339,"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."}}