{"id":"W1428828117","doi":"10.1016/j.ejor.2015.06.073","title":"The multi-vehicle traveling purchaser problem with pairwise incompatibility constraints and unitary demands: A branch-and-price approach","year":2015,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Column generation; Mathematical optimization; Pairwise comparison; Vehicle routing problem; Purchasing; Computer science; Branch and bound; Scheduling (production processes); Traveling purchaser problem; Shortest path problem; Branch and price; Branch and cut; Integer programming; Graph; Operations research; Routing (electronic design automation); Mathematics; Combinatorial optimization; 2-opt; Economics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003529325,0.00208559,0.00520861,0.002528647,0.001264252,0.003807356,0.005052066,0.00540799,0.01332373],"category_scores_gemma":[0.008070973,0.003064493,0.002568346,0.003951815,0.002735144,0.006186115,0.0027184,0.003507609,0.0009440437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002604146,"about_ca_system_score_gemma":0.002735205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009222946,"about_ca_topic_score_gemma":0.008013498,"domain_scores_codex":[0.9983419,0.0008693913,0.00005654942,0.0002585997,0.000241819,0.000231693],"domain_scores_gemma":[0.9948227,0.004319007,0.0002342139,0.0001077939,0.000216617,0.0002995899],"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":[0.0001665936,0.0002106953,0.0005051026,0.0002807311,0.0001153336,0.0002900798,0.00007945618,0.9125244,0.0004995706,0.06681696,0.00287012,0.01564091],"study_design_scores_gemma":[0.00003133171,0.000037754,0.00009399009,0.00001621809,0.00002724354,0.00003551002,0.00003407613,0.9735108,0.00008663687,0.02563074,0.0004827019,0.00001300828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04598344,0.001549912,0.9316581,0.001435333,0.0001488859,0.0003245334,0.0004071275,0.0001483363,0.01834418],"genre_scores_gemma":[0.5895522,0.002626107,0.3787647,0.0003793983,0.0004090063,0.0006990686,0.0007131679,0.0003714117,0.02648494],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01332373,"threshold_uncertainty_score":0.04457229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1067445357993932,"score_gpt":0.3311792832241026,"score_spread":0.2244347474247094,"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."}}