{"id":"W3121305672","doi":"10.1287/opre.2021.2228","title":"Vessel Service Planning in Seaports","year":2022,"lang":"en","type":"article","venue":"Operations Research","topic":"Maritime Ports and Logistics","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"Office of Planning, Research and Evaluation; Strong; Texas Department of Transportation; U.S. Department of Transportation","keywords":"Pilotage; Operations research; Service (business); Benders' decomposition; Computer science; Supply chain; Column generation; Operations management; Mathematical optimization; Engineering; Business; Mathematics","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":[0.0006408179,0.001182238,0.0008982328,0.0008516472,0.0007223688,0.002000987,0.001211497,0.001035818,0.00621287],"category_scores_gemma":[0.001026066,0.0008694979,0.00102887,0.001999032,0.0006392134,0.001825354,0.001309728,0.001142368,0.0004210983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001382896,"about_ca_system_score_gemma":0.002076929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01967602,"about_ca_topic_score_gemma":0.02630496,"domain_scores_codex":[0.9993344,0.0002000536,0.00003159008,0.0001419612,0.0001295509,0.0001624815],"domain_scores_gemma":[0.9996252,0.0001282339,0.00005969307,0.00004093159,0.00006868324,0.00007731003],"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":[0.00005493266,0.00007072366,0.0006604917,0.00004467003,0.00003437982,0.0001434404,0.00005101925,0.9709032,0.00118085,0.005002058,0.0009294399,0.02092488],"study_design_scores_gemma":[0.00001829937,0.00007122453,0.000403521,0.00001144268,0.00001980263,0.00003975748,0.0000876268,0.9925538,0.000752168,0.004246565,0.001786487,0.000009296116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1762166,0.0005839124,0.7972713,0.0005733368,0.00008237261,0.0003211051,0.0006621013,0.0009226294,0.02336675],"genre_scores_gemma":[0.7587143,0.0005499405,0.2333592,0.00007462664,0.00002893375,0.0001839416,0.000660964,0.0001360099,0.006292],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01967602,"threshold_uncertainty_score":0.039123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09651698364591516,"score_gpt":0.3624815964644001,"score_spread":0.2659646128184849,"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."}}