{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009189323,0.001036512,0.0009994308,0.0006347994,0.0003314299,0.001124872,0.0009099434,0.0009207061,0.002940416],"category_scores_gemma":[0.001745251,0.000531018,0.0007546354,0.0007793457,0.0004933164,0.0005931844,0.0007601493,0.0008399761,0.0002639697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001205371,"about_ca_system_score_gemma":0.001882344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01306327,"about_ca_topic_score_gemma":0.008296944,"domain_scores_codex":[0.9995552,0.0001639771,0.00001354731,0.00008682884,0.00005987126,0.000120616],"domain_scores_gemma":[0.9992746,0.0004070897,0.0001105122,0.00002436736,0.00008960855,0.00009387708],"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":[0.00003935914,0.00002300245,0.0003125021,0.00002367358,0.00001667868,0.00003845677,0.00001250092,0.9941984,0.0004513929,0.0008492568,0.0001791256,0.003855684],"study_design_scores_gemma":[0.000008926054,0.00003452713,0.0001713327,0.000003055437,0.000005641825,0.000006799604,0.00001703464,0.9989166,0.0001545325,0.0004913906,0.000187347,0.000002830202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3329412,0.0007448828,0.6493209,0.0004040918,0.000128329,0.0002540798,0.0003291105,0.0003431862,0.01553428],"genre_scores_gemma":[0.944775,0.0002069555,0.05021941,0.00006545686,0.00001591341,0.0001267779,0.0001696158,0.00004925742,0.004371522],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01306327,"threshold_uncertainty_score":0.02597451,"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."}}