{"id":"W3134208350","doi":"10.1155/2021/5537114","title":"Optimization Approach for Yard Crane Scheduling Problem with Uncertain Parameters in Container Terminals","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Maritime Ports and Logistics","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China","keywords":"Yard; Truck; Container (type theory); Scheduling (production processes); Computer science; Job shop scheduling; Operations research; Mathematical optimization; Port (circuit theory); Scheme (mathematics); Adaptability; Engineering; Computer network; Mathematics; Automotive engineering","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":[0.001221322,0.001461823,0.001919688,0.0007698826,0.0006278083,0.001786418,0.001473784,0.001707024,0.00251591],"category_scores_gemma":[0.00184074,0.0009003408,0.001252633,0.001107882,0.0006363571,0.0009672523,0.001037844,0.001640096,0.0001857262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0014862,"about_ca_system_score_gemma":0.002081561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01900309,"about_ca_topic_score_gemma":0.009446292,"domain_scores_codex":[0.999249,0.0002470953,0.00003655824,0.0001772067,0.0001393725,0.0001506688],"domain_scores_gemma":[0.9991832,0.0005176407,0.000113139,0.00002196007,0.0001125328,0.00005144536],"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":[0.00002151613,0.000011622,0.0001479018,0.00004423116,0.00001671327,0.00005161003,0.0000180048,0.9955878,0.0002465504,0.001679196,0.0001871136,0.001987749],"study_design_scores_gemma":[0.000006816843,0.00001596371,0.0000638426,0.000004434882,0.000005556713,0.000006146898,0.00001222315,0.9988832,0.00006673689,0.0008089791,0.0001231545,0.000003125231],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05093224,0.001263352,0.9402001,0.0005282223,0.0000815004,0.0001466993,0.0002129298,0.0001851901,0.006449719],"genre_scores_gemma":[0.8782932,0.001217482,0.1147786,0.0001428959,0.00008615566,0.0004172798,0.0002955112,0.00008396138,0.004684993],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01900309,"threshold_uncertainty_score":0.03778499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0133382094868891,"score_gpt":0.2326616337880779,"score_spread":0.2193234243011888,"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."}}