{"id":"W7117254448","doi":"10.5267/j.ijiec.2025.10.005","title":"Optimization of direct transshipment scheduling for river–sea intermodal transport with vessel arrival time matching","year":2025,"lang":"","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Maritime Ports and Logistics","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Transshipment (information security); Scheduling (production processes); Flexibility (engineering); Port (circuit theory); Arrival time; Multi-objective optimization; Job shop scheduling; Matching (statistics); Convergence (economics); Heuristic","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005186283,0.000349739,0.0006523182,0.0008780609,0.00007469375,0.0001008247,0.0005136282,0.0002302972,0.00007256366],"category_scores_gemma":[0.0002060729,0.0003633179,0.0003178514,0.000354998,0.00007797495,0.0003219977,0.0000277121,0.0005949172,6.372056e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003198397,"about_ca_system_score_gemma":0.0004692444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003253398,"about_ca_topic_score_gemma":0.000001465166,"domain_scores_codex":[0.9973519,0.00003118241,0.001569864,0.0002127215,0.0005560675,0.0002783049],"domain_scores_gemma":[0.9976085,0.0005416547,0.0005200738,0.0001259101,0.001073813,0.0001299834],"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":[0.0005080383,0.0001748812,0.0002481955,0.0001460992,0.001881132,0.00003391213,0.0005555022,0.9899753,0.0002986942,0.001768725,0.0001048815,0.004304587],"study_design_scores_gemma":[0.003631854,0.0002689147,0.0003736091,0.002815591,0.0005296133,0.00004587866,0.00007660938,0.9900635,0.001018132,0.000234932,0.0006229699,0.0003183974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02933957,0.000258241,0.9651701,0.0005045916,0.003659398,0.0003891868,0.0002899173,0.00004885624,0.00034009],"genre_scores_gemma":[0.9089015,0.00007896529,0.09003387,0.0000150321,0.0007500329,0.000009815044,0.0001054958,0.00005250481,0.00005274193],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.879562,"threshold_uncertainty_score":0.9998819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01560716115282327,"score_gpt":0.2420704770554689,"score_spread":0.2264633159026456,"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."}}