{"id":"W4292533090","doi":"","title":"A Metaheuristic for Service Network Design with Revenue Management for Freight Intermodal Transport","year":2015,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Maritime Ports and Logistics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Revenue; Service (business); Computer science; Transport engineering; Revenue management; Metaheuristic; Traffic management; Business; Operations research; Engineering; Marketing; Artificial intelligence","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":[0.00222904,0.001782896,0.001541373,0.001895891,0.0006228758,0.001431273,0.002129376,0.002678188,0.004343297],"category_scores_gemma":[0.004409029,0.0009441111,0.001860035,0.00185016,0.0009183042,0.001082497,0.001372168,0.001999558,0.0003567883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002251207,"about_ca_system_score_gemma":0.002453762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01072662,"about_ca_topic_score_gemma":0.007957308,"domain_scores_codex":[0.9991524,0.0004549621,0.0000291197,0.0001003941,0.0001215579,0.0001414945],"domain_scores_gemma":[0.9981934,0.001325186,0.0001255952,0.0000613263,0.0001926547,0.0001018564],"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":[0.00004539369,0.00004890418,0.0001245363,0.00004188297,0.00003921355,0.0000282262,0.00001945042,0.980866,0.0002245952,0.004668829,0.0007957252,0.01309725],"study_design_scores_gemma":[0.00001933223,0.00002001366,0.00002664214,0.000007544907,0.00000983289,0.000005384631,0.000009776116,0.9974493,0.00007194916,0.00206854,0.0003093272,0.000002325404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03833261,0.0008587308,0.9484929,0.0006573577,0.0001957647,0.0002801373,0.0002330058,0.0003812393,0.0105682],"genre_scores_gemma":[0.4044347,0.0004926129,0.5875618,0.0003745143,0.000177912,0.0005850188,0.0003133446,0.0002523658,0.005807727],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01072662,"threshold_uncertainty_score":0.02132839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02502662176615957,"score_gpt":0.2146339073354761,"score_spread":0.1896072855693166,"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."}}