Due to the intensified globalization of world economies, containerized liner trades have been steadily growing since the early 1990s. However, more than 50 years after containerized shipping was extended to international trades, the industry is still struggling with the problem of how to manage empty containers. On one hand, this industry is highly dynamic and unstable, with a constant ebb and flow in supply and demand for containers. On the other hand, trades are typically imbalanced in terms of the numbers of inbound and outbound containers, especially on the arterial East-West trade routes. To alleviate the effects of uncertainty and trade imbalance, ocean carriers are pressurized to seek effective policies to rationalize empty container movement....[ Read more ]

Due to the intensified globalization of world economies, containerized liner trades have been steadily growing since the early 1990s. However, more than 50 years after containerized shipping was extended to international trades, the industry is still struggling with the problem of how to manage empty containers. On one hand, this industry is highly dynamic and unstable, with a constant ebb and flow in supply and demand for containers. On the other hand, trades are typically imbalanced in terms of the numbers of inbound and outbound containers, especially on the arterial East-West trade routes. To alleviate the effects of uncertainty and trade imbalance, ocean carriers are pressurized to seek effective policies to rationalize empty container movement.

This paper considers the dynamic empty container reposition problem for a network of transhipment services in a random setting. The objective is for ocean carriers to minimize the finite horizon total expected empty container operational cost, comprising the transportation cost, the unloading cost, the loading cost, the holding cost and the leasing cost. The decisions in the model include when and where we need to reposition how many empty containers, and the number of leased containers needed to meet customers’ demand over time. The randomness arises from the demands for and the supplies of empty containers. The problem is formulated using chance constrained programming approach. For this special case, an inequality constraint containing random parameters can be transformed into chance constraint and further reduced to a deterministic programming problem. According to the principle of chance constrained programming approach, a probability level or service level is specified, with which the decisions feasibility can be guaranteed. Numerical tests are conducted to evaluate the model validity and to investigate the effects of different factors on both decisions and cost results.