{"id":"W2012738417","doi":"10.1007/s10732-007-9033-3","title":"Worst case analysis of Max-Regret, Greedy and other heuristics for Multidimensional Assignment and Traveling Salesman Problems","year":2007,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"Deutsche Forschungsgemeinschaft","keywords":"Travelling salesman problem; Heuristics; Regret; Greedy algorithm; Mathematical optimization; Computer science; Mathematics; Combinatorics; Machine learning","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":[0.01839074,0.003302779,0.003046851,0.003696318,0.001965426,0.004626996,0.004822563,0.002542764,0.006413789],"category_scores_gemma":[0.04050989,0.001660013,0.003220798,0.004041984,0.003315029,0.004928199,0.002089442,0.003406068,0.0005239167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006684405,"about_ca_system_score_gemma":0.003948153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008566026,"about_ca_topic_score_gemma":0.008626375,"domain_scores_codex":[0.9891566,0.006563739,0.0002663996,0.0006551163,0.001641237,0.001716843],"domain_scores_gemma":[0.9433042,0.04963005,0.001896956,0.001670343,0.002323858,0.001174559],"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":[0.0005966058,0.0001846287,0.0007226073,0.0002251282,0.0001377444,0.00007773147,0.0000589706,0.9591969,0.0003508529,0.0249467,0.003654822,0.009847184],"study_design_scores_gemma":[0.00002698604,0.00008616134,0.0002255498,0.00002000256,0.00005717424,0.00004001105,0.00004293795,0.9864563,0.0002275344,0.01246888,0.0003346449,0.00001384827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1635286,0.01115781,0.7803921,0.002879893,0.0005847751,0.0003698657,0.001032992,0.001016491,0.03903745],"genre_scores_gemma":[0.8187441,0.002412678,0.1670296,0.0006910245,0.0005678729,0.0003278446,0.0008297047,0.0007218417,0.008675385],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01839074,"threshold_uncertainty_score":0.09726071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0272815811744743,"score_gpt":0.2899186233637731,"score_spread":0.2626370421892987,"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."}}