{"id":"W2144303296","doi":"10.1287/ijoc.1090.0341","title":"Path-Reduced Costs for Eliminating Arcs in Routing and Scheduling","year":2009,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kronos (Canada); HEC Montréal; Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Column generation; Mathematical optimization; Computer science; Speedup; Constrained Shortest Path First; Shortest path problem; Vehicle routing problem; Path (computing); Scheduling (production processes); Routing (electronic design automation); Context (archaeology); Longest path problem; K shortest path routing; Mathematics; Parallel computing; Theoretical computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001943083,0.0002049708,0.0002745479,0.0002921065,0.0002660065,0.0002490307,0.0001483421,0.0001005627,0.000001533809],"category_scores_gemma":[0.0007896409,0.0001987964,0.00006543333,0.0002841066,0.00001285977,0.0003490355,0.00002777526,0.0006141603,0.000001591942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002292637,"about_ca_system_score_gemma":0.00002896919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001116999,"about_ca_topic_score_gemma":3.801942e-7,"domain_scores_codex":[0.9984056,0.00003558228,0.0007290384,0.0001442311,0.0001968311,0.0004887187],"domain_scores_gemma":[0.9990184,0.0004540719,0.0001975865,0.00009996239,0.0001010632,0.0001289735],"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":[0.00001068125,0.000008307702,0.0007764203,0.00002334896,0.000007753692,0.000006574739,0.000704219,0.6208317,0.001014162,0.0004606972,0.000007513808,0.3761486],"study_design_scores_gemma":[0.0009452751,0.0001195464,0.004827637,0.0006767433,0.000005764798,0.0001289478,0.0003549078,0.9910816,0.001311371,0.0002655882,0.00004021063,0.0002424301],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6708685,0.00008266129,0.3274513,0.0001207754,0.0002809276,0.0001459759,6.324204e-7,0.0001376243,0.000911572],"genre_scores_gemma":[0.7451161,0.00001850748,0.2544123,0.0001904768,0.0002331138,9.053419e-7,0.000001422252,0.0000235082,0.000003696841],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3759062,"threshold_uncertainty_score":0.8106682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01740939486299027,"score_gpt":0.2875494768532688,"score_spread":0.2701400819902785,"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."}}