Abstract
The rapid growth of e-commerce platforms has given rise to convenient online shopping methods, but platform operators usually don’t have sufficient capacity to deliver these online orders. Hence, based on the estimated demand for each type of commodity, logistics service providers generally require providing freight services to fulfill customer demands while maintaining their own profitability. However, each origin-destination demand is impacted by various factors and is inherently uncertain. This poses significant challenges for logistics service providers seeking to develop an economically viable and efficient freight service network. Given that most research on service network design problems rarely utilizes historical data, in this paper, we propose a data-driven robust service network design problem, aiming to answer: is it possible to obtain more economically efficient decisions when we design a service network based on historical data? To achieve this, a support vector clustering method is employed to adaptively capture a compact convex uncertainty set from the historical demand data, allowing explicit control over the conservatism of decisions. Based on the obtained uncertainty set, a novel data-driven robust optimization model is developed. Considering the NP-hard nature of this problem, we propose a customized column-and-constraint generation algorithm that effectively solves the model. Compared to the Benders decomposition algorithm, it significantly improves computational efficiency. Numerical experiments demonstrate that the proposed method outperforms traditional robust optimization approaches in mitigating demand uncertainty, exhibiting robustness while being less conservative. Meanwhile, we provide valuable managerial insights to decision-makers through parameter sensitivity analysis. Results reveal that neither overly aggressive nor excessively conservative strategies are optimal, and adjusting the clustering parameter allows decision-makers to tailor the service network to their risk tolerance for more cost-effective operations under demand uncertainty.
Keywords: Service network design; Uncertainty; Data-driven robust optimization; Column-and-constraint generation; Support vector clustering