{"id":"W90079504","doi":"10.1080/03155986.2005.11732719","title":"Modeling and Analysis of Multicommodity Network Flows Via Goal Programming","year":2005,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Multi-commodity flow problem; Flow network; Mathematical optimization; Linear programming; Lagrangian relaxation; Minimum-cost flow problem; Robustness (evolution); Computer science; Relaxation (psychology); Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002124867,0.001283744,0.0007046823,0.0009847756,0.0005721047,0.002036569,0.001397543,0.001130708,0.001886397],"category_scores_gemma":[0.004229687,0.0007027669,0.0009759064,0.001162942,0.001241657,0.002744683,0.000998899,0.001683903,0.0004447667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001619701,"about_ca_system_score_gemma":0.001880838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004475198,"about_ca_topic_score_gemma":0.002607094,"domain_scores_codex":[0.9986472,0.0006731799,0.0000398759,0.0001693778,0.0003458749,0.0001246311],"domain_scores_gemma":[0.9985232,0.001052625,0.0001775411,0.00007946174,0.0001315029,0.00003565491],"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":[0.0000115583,0.00002338444,0.0002131385,0.00006009921,0.00001821566,0.0000429194,0.0000477571,0.8428807,0.0002732691,0.1471834,0.0004686657,0.008776867],"study_design_scores_gemma":[0.000002884332,0.000007477071,0.00004656842,0.00001433777,0.000006237612,0.00001048382,0.0000122122,0.9338284,0.0002047017,0.06405865,0.001803564,0.000004544429],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003882353,0.0002323289,0.9921628,0.0002400162,0.00001643608,0.00003219606,0.00004902688,0.0001054493,0.003279416],"genre_scores_gemma":[0.4029795,0.002324131,0.5877211,0.0001593723,0.0001141795,0.0004871656,0.0002917764,0.0001811835,0.005741498],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004475198,"threshold_uncertainty_score":0.01175177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03883840468365876,"score_gpt":0.3158162269583731,"score_spread":0.2769778222747143,"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."}}