{"id":"W2544503045","doi":"10.1109/iscbi.2013.41","title":"Air Cargo Scheduling Using Genetic Algorithms","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"Universidade de Macau","keywords":"Scheduling (production processes); Genetic algorithm; Computer science; Profit (economics); China; Southern china; Fitness function; Air cargo; Air travel; Operations research; Mathematical optimization; Engineering; Aviation; Operations management; Transport engineering; Aerospace engineering; Mathematics; Machine learning; Economics; Geography","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":[0.00051932,0.0005934974,0.0005710113,0.0009072017,0.0004353134,0.0008936849,0.0005325429,0.000686041,0.00136379],"category_scores_gemma":[0.001340171,0.0003235805,0.0006145454,0.000945807,0.0004181981,0.0005742375,0.0003497335,0.0004678085,0.0002290817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001064636,"about_ca_system_score_gemma":0.001511227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01288171,"about_ca_topic_score_gemma":0.007254669,"domain_scores_codex":[0.9996839,0.0001319844,0.000009647491,0.00005271159,0.00008040194,0.00004133129],"domain_scores_gemma":[0.9997163,0.0001637914,0.00003548443,0.00002067048,0.00005168663,0.0000119645],"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":[0.0000162162,0.0000193059,0.0002769636,0.00001926204,0.00001935605,0.00001632994,0.00001683539,0.969263,0.0005517547,0.005793322,0.0004311371,0.02357654],"study_design_scores_gemma":[0.00001147701,0.00001543585,0.00009491437,0.000005328295,0.000006142681,0.000005949774,0.000006793819,0.9950368,0.0002789141,0.003679902,0.0008551566,0.000003088257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07026344,0.0008993419,0.9153452,0.0003089332,0.00009214901,0.0001102141,0.0001169441,0.0007654707,0.01209831],"genre_scores_gemma":[0.6644329,0.001120969,0.3276045,0.0001365123,0.00006110671,0.0002474343,0.0002776867,0.0001198743,0.00599911],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01288171,"threshold_uncertainty_score":0.02561343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01391616184303958,"score_gpt":0.2195331816101424,"score_spread":0.2056170197671028,"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."}}