A robust optimization model for a cross-border logistics problem with fleet composition in an uncertain environment

Abstract

Since the implementation of the open-door policy in China, many Hong Kong-based manufacturers' production lines have moved to China to take advantage of the lower production cost, lower wages, and lower rental costs, and thus, the finished products must be transported from China to Hong Kong. It has been discovered that logistics management often encounters uncertainty and noisy data. In this paper, a robust optimization model is proposed to solve a cross-border logistics problem in an environment of uncertainty. By adjusting penalty parameters, decision-makers can determine an optimal long-term transportation strategy, including the optimal delivery routes and the optimal vehicle fleet composition to minimize total expenditure under different economic growth scenarios. We demonstrate the robustness and effectiveness of our model using the example of a Hong Kong-based manufacturing company. The analysis of the trade-off between model robustness and solution robustness is also presented.