Reduction of Empty Container Repositioning Costs by Container Sharing

Abstract Within the area of regional port clusters, this paper establishes a multi-period mixed integer programming model to optimize the empty container repositioning between public hinterlands and ports, comprehensively considering the quantitative and periodic inventory control strategy. By using Markov decision process combined with dynamic programming method, this paper dynamically optimizes the empty container inventory threshold (D;U) under quantitative strategy and S under periodical strategy at each port within the regional port clusters. On this basis, this paper optimizes the empty container repositioning scheme between public hinterlands and ports. Meanwhile, Liaoning coastal regional port cluster and its northeast hinterland are selected as the objects to solve this model and the results show that the total cost of shipping company can be saved by 14.16% and 11.92% respec- tively by the quantitative and periodical inventory control strategy. Selecting the quantity of public hinterland terminals, the empty container demand of public hinterland terminals and ports, the inventory threshold of empty containers and other factors, this paper carries on the sensitivity analysis. This paper validates inventory control strategy can weaken the shipping company in the influence of the external environment changes. And the quantitativeinventory control strategy can reduce the total cost value to a greater extent and more effective in cost control than periodical strategy.

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A New Separable Piecewise Linear Learning Algorithm for the Stochastic Empty Container Repositioning Problem

Mathematical Problems in Engineering ◽

10.1155/2020/4762064 ◽

2020 ◽

Vol 2020 ◽

pp. 1-16

Author(s):

Shaorui Zhou ◽

Xiaopo Zhuo ◽

Zhiming Chen ◽

Yi Tao

Keyword(s):

Cost Function ◽

Programming Model ◽

Learning Algorithm ◽

Piecewise Linear ◽

Two Stage ◽

Empty Container ◽

Expected Cost ◽

Stochastic Programs ◽

Empty Container Repositioning ◽

Empty Containers

A common challenge faced by liner operators in practice is to effectively allocate empty containers now in a way that minimizes the expectation of costs and reduces inefficiencies in the future with uncertainty. To incorporate uncertainties in the operational model, we formulate a two-stage stochastic programming model for the stochastic empty container repositioning (ECR) problem. This paper proposes a separable piecewise linear learning algorithm (SPELL) to approximate the expected cost function. The core of SPELL involves learning steps that provide information for updating the expected cost function adaptively through a sequence of piecewise linear separable approximations. Moreover, SPELL can utilize the network structure of the ECR problem and does not require any information about the distribution of the uncertain parameters. For the two-stage stochastic programs, we prove the convergence of SPELL. Computational results show that SPELL performs well in terms of operating costs. When the scale of the problem is very large and the dimensionality of the problem is increased, SPELL continues to provide consistent performance very efficiently and exhibits excellent convergence performance.

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