🤖 AI Summary
This study addresses the service network design challenge in intermodal logistics networks arising from uncertain travel times and limited truck fleet availability. The authors propose a two-stage optimization framework that jointly handles tactical decisions—such as transport request acceptance and capacity reservation—and operational decisions, including dynamic vehicle dispatching and route replanning. Innovatively integrating an adaptive machine learning surrogate model with a simulated annealing algorithm, the approach effectively mitigates cascading disruptions caused by uncertainty-induced perturbations. Computational experiments demonstrate that the method achieves up to a 20-fold reduction in solution time while maintaining solution quality within a 5% deviation from the optimal objective value, significantly outperforming existing benchmark approaches.
📝 Abstract
This paper addresses the Service Network Design (SND) problem for a logistics service provider (LSP) operating in a multimodal freight transport network, considering uncertain travel times and limited truck fleet availability. A two-stage optimization approach is proposed, which combines metaheuristics, simulation and machine learning components. This solution framework integrates tactical decisions, such as transport request acceptance and capacity booking for scheduled services, with operational decisions, including dynamic truck allocation, routing, and re-planning in response to disruptions. A simulated annealing (SA) metaheuristic is employed to solve the tactical problem, supported by an adaptive surrogate model trained using a discrete-event simulation model that captures operational complexities and cascading effects of uncertain travel times. The performance of the proposed method is evaluated using benchmark instances. First, the SA is tested on a deterministic version of the problem and compared to state-of-the-art results, demonstrating it can improve the solution quality and significantly reduce the computational time. Then, the proposed SA is applied to the more complex stochastic problem. Compared to a benchmark algorithm that executes a full simulation for each solution evaluation, the learning-based SA generates high quality solutions while significantly reducing computational effort, achieving only a 5% difference in objective function value while cutting computation time by up to 20 times. These results demonstrate the strong performance of the proposed algorithm in solving complex versions of the SND. Moreover, they highlight the effectiveness of integrating diverse modeling and optimization techniques, and the potential of such approaches to efficiently address freight transport planning challenges.