Optimization of empty container allocation for inland freight stations considering stochastic demand

In the post-COVID-19 epidemic era (PCEE), the supply of empty containers will face stronger uncertainty. Estimating the amount of self-owned and leased empty containers that need to be allocated to each inland freight station in a specific area becomes a critical issue for liner companies in PCEE. However, owing to the high degree of unpredictability of the demand and the limited flexibility of empty container relocation, the abovementioned issue has not been fully addressed. This paper provides a model for empty container allocation without knowing the probability distribution function of empty container demand in advance. The abovementioned model can jointly optimize the quantities of self-owned empty containers and leased containers allocated to each inland freight station. To solve the model, a largest-debt-first policy is adopted to simplify the complicated model, and a differential evolutionary (DE) algorithm is developed to solve the simplified model. Compared with some commonly used algorithms, DE has advantages considering the ability to explore the optimal solution. In addition, the utility of the largest-debt-first policy proposed in this paper is compared with that of the traditional method. Experimental results show that in the case of high demand fluctuations, the proposed policy is better in controlling the operational and management costs. Overall, the theory and method proposed in this paper can effectively help the carrier set a reasonable regional empty container stock level and determine the number of self-owned and leased empty containers.

Automated summary

This paper presents a model for empty container allocation without knowing the probability distribution function of demand in advance. It adopts the largest-debt-first policy and a differential evolutionary algorithm to solve the model. Experimental results show that the proposed policy is better in controlling operational and management costs in case of high demand fluctuations.