In urban areas, fluctuating road speeds due to traffic congestion and accidents significantly impact bus operations and stop connectivity. Current approaches cannot maintain public transport (PT) network stability during adaptation to changing road conditions, undermining both operations and passenger experience. This paper proposes a data sampling-based adjustment strategy to adapt the time-varying road conditions. The innovation lies in utilising limited network modifications to enhance the existing static PT network instead of considering reconstruction from scratch or minor adjustments (such as stop-skipping), aiming to minimise both passenger travel time degradation and the operational duration of each transit line. Our proposed multi-objective optimization model leverages historical traffic data samples and integrates route variation quantification with penalty mechanisms to enable real-time adaptive routing decisions. The case studies utilising Mandl’s network illustrate that our methodology can propose effective strategies for time-varying roads with any coefficient of variation. Experimental findings with high-variance samples indicate that our methodology decreases passenger travel time in roughly 80
The Traveling Salesman Problem (TSP) is a classic combinatorial optimization challenge with broad applications in logistics and transportation. While traditional heuristics remain dominant due to their efficiency and reliability, recent developments in Deep Reinforcement Learning (DRL) have introduced promising new directions for data-driven approaches to solving the TSP. Although DRL-based methods show promise, they often do not yet match classical heuristics in terms of computational efficiency and solution quality. One promising direction to bridging this gap is the integration of classical heuristics with learning-based methods. The Learn-to-Improve (L2I) framework follows this hybrid paradigm, combining heuristics with reinforcement learning to iteratively refine solutions. In this paper, we propose a novel multi-action sampling strategy that further enhances the L2I framework for solving TSP. The core idea is to improve solution quality by averaging rewards over multiple actions, which reduces bias and encourages more effective exploration compared to single-action strategies. During inference, multi-action sampling is applied in the later stages to explore diverse solution paths in parallel, helping to prevent premature convergence. Experimental results demonstrate that the proposed method outperforms existing L2I approaches while maintaining competitive computational efficiency.
The Unified Planning Framework (UPF) provides convenient access to automated planning technology. It allows for problem formulation independent of a planning engine and the utilization of planners available on the system. However, choosing a suitable parameter configuration of the planning engine for a given problem constitutes a significant challenge. Manually finding a high-quality configuration requires domain knowledge and a considerable time investment, contradicting the intended ease-of-use of the UPF. This issue is addressed by Algorithm Configuration (AC) techniques, which aim to automatically find high-quality configurations. Algorithm runtime as well as quality of solutions found by the parameterized algorithm have been shown to be improved by AC methods in wide-ranging problem settings, which includes planning. We integrate three state-of-the-art AC methods into the UPF and perform AC runs with planning engines which are integrated in the UPF. To this end, we perform AC in runtime, solution quality and anytime planning scenarios on problem instance sets from several International Planning Competitions (IPC). We demonstrate that AC methods provide performance improvements for the IPC.
This paper introduces a hybrid approach to address the Capacitated Vehicle Routing Problem with Time Windows by integrating quadratic unconstrained binary optimization (QUBO) hardware with deep learning-assisted heuristics. The proposed three-phase heuristic leverages the strengths of QUBO-solving hardware while mitigating its limitations, aiming at offering better scalability to larger problem instances. In the first phase, a deep learning-enhanced QUBO formulation is employed to partition the vertices into clusters. The second phase uses deep learning-assisted tree searches to generate candidate routes within each cluster. These candidate routes are combined in the third phase into a feasible global solution by solving a quadratic unconstrained binary set partition problem. This framework ensures compliance with capacity and time window constraints while maintaining computational efficiency. Computational results indicate that the hybrid approach is promising to potentially scale well for larger problem cases while respecting hardware limitations, offering a viable approach for leveraging quantum-inspired hardware in combination with advanced heuristics for solving complex combinatorial optimization problems.
This study examines the effectiveness of adversarial attacks in determining upper bounds for robustness distributions for neural networks. While complete neural network verification techniques can provide exact safety margins, their computational cost limits scalability. To address this, we evaluate multiple adversarial attack methods, including FGSM, PGD, AutoAttack and FAB, comparing them to a state-of-the-art verification technique, α , β -CROWN. Using the MNIST dataset, we demonstrate that adversarial attacks yield computationally efficient and tight upper bounds for robustness distributions. We assess the trade-offs between running time, accuracy and the quality of the bounds obtained through our approach. The results highlight complementarities between verification and attack methods: Attacks achieve near-optimal upper bounds at a significantly reduced computational cost. These findings open opportunities for large-scale robustness analysis while acknowledging limitations in safety guarantees inherent to the approximation techniques on which our approach is based.
