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.
arXiv:2608. 19214v1 Announce Type: new Abstract: The berth allocation and quay crane assignment problem (BACAP) is a representative port-terminal scheduling problem in maritime transportation and freight logistics, where vessel arrivals, berth positions, service durations, and quay?
arXiv:2606. 00009v1 Announce Type: new Abstract: Bayesian Optimization (BO) is widely and successfully adopted for solving optimization problems having an expensive-to-evaluate, black-box, and non-convex objective function.
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.
arXiv:2606. 12667v1 Announce Type: cross Abstract: Rapidly expanding low Earth orbit satellite constellations are placing increasing demands on terrestrial ground networks, motivating the development of more efficient ground station network designs.
arXiv:2608. 10245v1 Announce Type: cross Abstract: Inventory and distribution planning in Physical Internet networks requires coordinating factory-hub assignments, factory supply, lateral transshipment among collaborative hubs, retailer deliveries, and shortages.
arXiv:2607. 06066v1 Announce Type: new Abstract: The Vehicle Routing Problem (VRP) and its variants represent some of the most practically consequential optimization challenges in modern logistics and urban mobility.
The paper evaluates the Tabular Prior-data Fitted Network (TabPFN) as a surrogate model in surrogate‑assisted evolutionary algorithms (SAEAs) for expensive optimization problems. Through extensive experiments in both offline and online settings across a range of problem types—including single‑objective, multi‑objective, constrained, combinatorial, mixed‑variable, and engineering tasks—the study finds that TabPFN’s effectiveness varies strongly with the problem characteristics. The authors conclude that TabPFN should be used selectively, with customized model management and algorithm design tailored to data availability, landscape complexity, and search‑space properties.
The paper introduces LLM-EBG, an evolutionary framework that uses a large language model as a generative operator to automatically create optimization benchmarks. By generating unconstrained single-objective continuous minimization problems expressed as mathematical formulas, the framework can produce benchmarks that consistently favor a target algorithm over a comparison algorithm in over 80% of trials. Landscape analysis shows that these generated problems exhibit distinct geometric traits, such as sensitivity to variable scaling, reflecting the search behaviors of different optimization methods.
arXiv:2604.04940v3 Announce Type: replace Abstract: Designing effective heuristics for NP-hard combinatorial optimization problems remains challenging and often requires substantial domain expertise....
arXiv:2606. 12279v1 Announce Type: cross Abstract: Recent work in ML applies genetic algorithms at inference time to iteratively improve solutions to optimization problems.
arXiv:2609.14418v1 Announce Type: cross Abstract: Dynamic multi-mode resource-constrained project scheduling requires decisions to be made under precedence constraints, limited resources, multiple ex...
arXiv:2607. 07289v1 Announce Type: cross Abstract: This study presents a multi-fidelity framework for the systematic optimization of genetic algorithm (GA) hyperparameters.