Scheduling connected and automated vehicles (CAVs) at reservation-based intersections faces a fundamental trade-off: existing methods sacrifice either optimality for computational efficiency or real-time applicability for optimal solutions. We bridge this gap by proposing a dynamic programming (DP) algorithm with dominance pruning () that simultaneously achieves optimal batch formation, polynomial-time complexity, and superior solution quality. introduces a novel dominance rule that eliminates suboptimal child nodes by comparing analytically derived lower bounds, enabling batch-based processing of CAVs while guaranteeing optimality. Unlike existing approaches that assume predetermined batches or sacrifice optimality, integrates optimal batching decisions directly into the scheduling algorithm. The proposed method achieves a substantial improvement over existing exact algorithms with worst-case and best-case time complexity. Extensive experiments demonstrate that reduces computation time by 91% and 99% compared to a state-of-the-art DP algorithm and Gurobi solver, respectively, while consistently delivering superior performance in makespan, average delay, and maximum delay metrics across diverse traffic scenarios. Furthermore, outperforms heuristic algorithms in achieving better solution quality and comparative computational efficiency. Sensitivity analysis reveals that 's dominance rule remains effective across varying mean headways, CAV counts, and batch proportions, with performance benefits extending beyond simple batch processing to complex traffic patterns.
This study addresses the aerial firefighting resource allocation problem, formulated as the strategic prepositioning of a heterogeneous aerial firefighting fleet across capacity-constrained airbases. A novel bi-objective model is proposed that simultaneously optimizes efficiency (aggregate risk-weighted accessibility) and equity (minimized regional service disparities). The model employs an enhanced spatial accessibility method with a distance-decay function to evaluate coverage effectiveness and distribution fairness. A multi-objective genetic algorithm is implemented to generate Pareto-approximate solutions, revealing quantifiable trade-offs. Applied to a national-scale case study in Greece using 25 years of wildfire data, results demonstrate that the proposed framework provides significantly wider coverage than current practices, thereby extending protection to underserved regions while reducing redundant overlap. Sensitivity analyses confirm robustness across various real-world conditions, such as different budget levels and aircraft availability. This transferable tool enables policymakers to make data-driven decisions by quantifying the balance between response effectiveness and social fairness in wildfire-prone regions.
This paper introduces the bilevel tree-of-hubs location and pricing problem, a multiple-allocation hub location problem in which both the design and pricing of a tree-shaped hub backbone network are determined. Two types of decision-makers are considered: a leader, who defines the network structure and sets usage prices, and a follower, who selects the optimal way to use the network for shipping commodities. The leader aims to maximize profit, while the follower seeks to minimize transportation costs. A bilevel optimization formulation is proposed and reformulated into a single-level equivalent model. To solve it efficiently, we develop a Co-Evolutionary Algorithm (Co-EA) and test it on three well-known datasets adapted to our problem. The novelty of the proposed Co-EA lies in the interaction between its co-evolving populations: rather than representing leader and follower solutions separately, each population is associated with a subset of the leader's decision variables. For each leader decision, the follower's optimal response is computed, ensuring bilevel feasibility. Extensive computational experiments demonstrate the effectiveness of the proposed Co-EA compared with general-purpose solvers applied to the reformulated model. The best Co-EA solutions are also used as warm-starts for the exact solver, confirming the quality of the heuristic results. Finally, managerial insights are derived regarding how the number of hubs affects the backbone network configuration. The paper concludes with directions for future research on bilevel hub location and pricing models.
In this paper, we tackle the team orienteering problem (TOP) with service times, mandatory nodes and incompatibilities arising from two real-world healthcare applications. We propose two heuristic algorithms: a variable neighbourhood descent algorithm and a matheuristic based on a cut separation approach. For the latter, we also provide a multi-threaded version exploiting its intrinsic capability to be parallelised. Both algorithms include a specific heuristic routine to provide a starting feasible solution, since finding a feasible solution has been proven to be NP-complete. The results of our heuristic algorithms are compared with an exact cutting plane approach and have complementary strengths and weaknesses. They are also evaluated on existing TOP benchmarks against state-of-the-art TOP algorithms, demonstrating their competitiveness on general grounds.
Abstract In recent years, natural disasters have become more frequent and destructive, causing major infrastructure damage and significant loss of life. The unpredictable number of casualties and their rapidly changing conditions after a disaster pose significant challenges to the post‐disaster relief efforts. Considering the uncertain number and dynamic condition of the casualties, we investigate an integrated relief planning problem of emergency facility locations, casualty transportation routes, and medical supplies distribution. A robust optimization approach is employed, and the concept of dynamic casualty death probability resulting from the treatment delays is introduced. Then, a tri‐objective optimization model is formulated to minimize the casualty death probability, the latest arrival time of medical supplies, and the total operational costs. To solve the model, a multi‐objective particle swarm optimization algorithm is proposed, which is enhanced with Q‐learning to guide the search direction by dynamically adjusting the inertia weights. Moreover, a local search mechanism is incorporated to improve the population diversity, resulting in a Q‐learning‐based multi‐objective particle swarm optimization algorithm with local search. The effectiveness of the proposed algorithm is validated through a set of numerical experiments. A case study of the Yushu Earthquake further demonstrates the model's practical applicability.
