{"id":"W3128540120","doi":"10.1016/j.ejor.2021.02.009","title":"Scheduled service network design with resource management for two-tier multimodal city logistics","year":2021,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Urban and Freight Transport Logistics","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Integer programming; Multimodal transport; Decomposition; Network planning and design; Consolidation (business); Operations research; Linear programming; Resource allocation; Service (business); Flow network; Mathematical optimization; Transport engineering; Computer network; Engineering; Business; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002642532,0.0001289695,0.0001724545,0.00009715101,0.0002119813,0.0001414629,0.0002921261,0.00002256896,0.0001311648],"category_scores_gemma":[0.00009828468,0.0001064918,0.0000521798,0.0003750841,0.00007622511,0.00009774909,0.00003815503,0.0004926385,0.00002393372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008216241,"about_ca_system_score_gemma":0.0001292832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.05788e-7,"about_ca_topic_score_gemma":0.000006243297,"domain_scores_codex":[0.998062,0.0003715146,0.0003864643,0.0001494264,0.000673065,0.000357592],"domain_scores_gemma":[0.9980285,0.0003076747,0.00003874186,0.0001819028,0.00127652,0.0001667018],"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":[0.0002604682,0.000062054,0.0001679097,0.00006626021,0.000189964,0.001057979,0.0001048936,0.9776687,0.0003271537,0.006600693,0.01247346,0.001020451],"study_design_scores_gemma":[0.009811937,0.001276063,0.01189585,0.0006493386,0.0002192689,0.0004556559,0.0005634211,0.6551346,0.002738083,0.001103605,0.3152756,0.0008764989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002260845,0.0004952392,0.9794711,0.0004058841,0.0001393019,0.0002223821,0.00001696333,0.00002268687,0.01696553],"genre_scores_gemma":[0.6588002,0.00006954212,0.3389601,0.0001918676,0.0008981457,0.000005435296,0.00004050519,0.00006085025,0.0009734237],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6565393,"threshold_uncertainty_score":0.4342608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1447291545781464,"score_gpt":0.3068228414253132,"score_spread":0.1620936868471668,"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."}}