Adaptive decision-making for stochastic service network design

TL;DR

This paper presents an adaptive, integrated optimization framework for stochastic service network design in freight logistics, combining metaheuristics, simulation, and machine learning to improve decision-making under uncertainty.

Contribution

It introduces a novel two-stage approach that combines metaheuristics, simulation, and machine learning for stochastic service network design, enhancing solution quality and computational efficiency.

Findings

The proposed SA outperforms state-of-the-art methods on deterministic problems.

The learning-based SA achieves high-quality solutions with 20x faster computation.

The framework effectively handles complex stochastic freight transport planning challenges.

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.