{"id":"W2621009047","doi":"","title":"Variable Neighborhood Search for Minimum Cost Berth Allocation","year":2003,"lang":"en","type":"article","venue":"RePEc: Research Papers in Economics","topic":"Maritime Ports and Logistics","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Variable neighborhood search; Mathematical optimization; Tardiness; Minification; Heuristic; Variable (mathematics); Computer science; Genetic algorithm; Memetic algorithm; Local search (optimization); Function (biology); Retard; Mathematics; Metaheuristic; Job shop scheduling","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":[0.0007665276,0.0004414949,0.001115709,0.0007489905,0.0003710208,0.000560694,0.000760434,0.0008134495,0.002090983],"category_scores_gemma":[0.002078029,0.000417238,0.0003394162,0.0008198434,0.000492912,0.000659132,0.0004281435,0.0005534139,0.0002476403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007999742,"about_ca_system_score_gemma":0.0007963068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00603787,"about_ca_topic_score_gemma":0.004942014,"domain_scores_codex":[0.9996073,0.0002063529,0.00001100344,0.00005273621,0.000072209,0.0000505246],"domain_scores_gemma":[0.9992773,0.0005651104,0.00005259747,0.00001907883,0.00005908067,0.00002685243],"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":[0.00006132554,0.00003516176,0.0002920017,0.00003125432,0.00001991217,0.00003146684,0.00002737038,0.9733006,0.0003315661,0.009592718,0.001033549,0.01524311],"study_design_scores_gemma":[0.00001369357,0.00001467884,0.00005353601,0.000003080185,0.000002700026,0.000006085822,0.000005606998,0.9962215,0.00007641995,0.003293168,0.0003075301,0.000001886122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1222652,0.001418048,0.8636299,0.000527014,0.00009110507,0.0001069795,0.0001706362,0.0004747236,0.01131637],"genre_scores_gemma":[0.7604664,0.0004106026,0.2311174,0.0001071708,0.00004893816,0.000287144,0.0002832595,0.0001040611,0.007175024],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00603787,"threshold_uncertainty_score":0.01200545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02626859360425234,"score_gpt":0.2750029009678145,"score_spread":0.2487343073635622,"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."}}