{"id":"W3171114511","doi":"10.5267/j.jpm.2021.5.002","title":"Solving open travelling salesman subset-tour problem through a hybrid genetic algorithm","year":2021,"lang":"en","type":"article","venue":"Journal of Project Management","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Travelling salesman problem; Tree traversal; Crossover; Christofides algorithm; Benchmark (surveying); 2-opt; Computer science; Selection (genetic algorithm); Genetic algorithm; Traverse; Set (abstract data type); Bottleneck traveling salesman problem; Lin–Kernighan heuristic; Nearest neighbour algorithm; Mathematical optimization; Mutation; Permutation (music); Algorithm; Mathematics; Artificial intelligence; Machine learning; Geography; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009163294,0.0002057086,0.0003591932,0.0001812362,0.0001014176,0.0003213566,0.0005276342,0.00004282824,0.00007745965],"category_scores_gemma":[0.00002447593,0.0002059647,0.0001269083,0.0004558487,0.00001551213,0.0003864062,0.0002303356,0.0003006378,0.000009578644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001694159,"about_ca_system_score_gemma":0.00008105096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008548479,"about_ca_topic_score_gemma":0.000002829769,"domain_scores_codex":[0.9981588,0.000157049,0.0007548955,0.0002106729,0.0003767286,0.0003419233],"domain_scores_gemma":[0.9991972,0.00004796178,0.0002437879,0.0002657349,0.0001828981,0.00006235475],"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":[0.00001674701,0.0001619768,0.0001920596,0.0006416574,0.0008865672,0.002104637,0.001793437,0.7009419,0.0006409853,0.0004110046,0.00916117,0.2830479],"study_design_scores_gemma":[0.003837195,0.0002379505,0.001450452,0.001268313,0.0006025511,0.001377383,0.002366064,0.887586,0.01253726,0.003210872,0.08443755,0.001088393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003262521,0.0008675035,0.978422,0.0001065881,0.000463716,0.0004715994,0.000003438574,0.00007711601,0.01632553],"genre_scores_gemma":[0.01271418,0.001105926,0.9849982,0.00009115367,0.0002218966,0.00001553386,0.000004198618,0.00006938275,0.0007795474],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2819594,"threshold_uncertainty_score":0.8398994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03150600752234208,"score_gpt":0.2963904838218729,"score_spread":0.2648844762995309,"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."}}