arXiv AI By Md. Mahfuzur Rahman, Md Abrar Jahin, Md. Saiful Islam, M. F. Mridha

Optimizing Container Loading and Unloading through Dual-Cycling and Dockyard Rehandle Reduction Using a Hybrid Genetic Algorithm

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The paper proposes the Quay Crane Dual Cycle - Dockyard Rehandle Genetic Algorithm (QCDC-DR-GA), a hybrid GA that jointly optimizes the unloading sequence of Quay Crane Dual-Cycling and dockyard rehandle minimization. By employing specialized crossover and mutation strategies, the algorithm maximizes the number of Dual Cycles while reducing dockyard rehandles. Experiments on various ship sizes show a 15‑20% reduction in total operation time for large ships compared to existing methods, with statistical significance confirmed at the 5% level.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning
Jul 9

Optimization-Embedded Active Multi-Fidelity Surrogate Learning for Multi-Condition Airfoil Shape Optimization

arXiv:2603. 17057v2 Announce Type: replace-cross Abstract: Active multi-fidelity surrogate modeling is developed for multi-condition airfoil shape optimization to reduce high-fidelity CFD cost while retaining RANS-consistent aerodynamic metrics.

By Isaac Robledo, Alberto Vilari\~no, Arnau Mir\'o, Oriol Lehmkuhl, Carlos Sanmiguel Vila, Rodrigo Castellanos