This thesis addresses three issues arising in the ocean transport industry. The first essay studies the cost-speed conundrum which confronts seasonal-product shippers. We consider a newsvendor-type shipper who transports and sells seasonal products to an overseas market where the selling price declines over time. Multiple shipping services are available with different (uncertain) arrival times and freight rates. Our analysis reveals that a portfolio of shipping services has twofold benefits in mitigating both demand and arrival-time uncertainties. We first derive the optimal portfolio by exploiting the special structure when vessels’ arrival sequence is deterministic, and then propose an iterative approximation procedure to solve the general problem with uncertain arrival sequenc...[ Read more ]

This thesis addresses three issues arising in the ocean transport industry. The first essay studies the cost-speed conundrum which confronts seasonal-product shippers. We consider a newsvendor-type shipper who transports and sells seasonal products to an overseas market where the selling price declines over time. Multiple shipping services are available with different (uncertain) arrival times and freight rates. Our analysis reveals that a portfolio of shipping services has twofold benefits in mitigating both demand and arrival-time uncertainties. We first derive the optimal portfolio by exploiting the special structure when vessels’ arrival sequence is deterministic, and then propose an iterative approximation procedure to solve the general problem with uncertain arrival sequence. In each iteration of our procedure, we only need to minimize a cost function approximated by a deterministic arrival schedule and the portfolio generated can converge to the optimal one under mild conditions.

In the second essay, we analyze the joint decision of empty container repositioning and pricing from the perspective of ocean liners. We develop a stylized Markovian decision model consisting of two locations with stochastic and endogenous shipping demand. With the help of L^♮-concavity, we explicitly characterize the structure of optimal control policies, which sheds light on the interplay between the pricing decision and empty container repositioning. As opposed to single-location inventory systems, we find that the allocation of empty containers may oscillate between two different base-stock levels, depending on the direction of demand imbalance.

In the third essay, we extend the classical berth allocation problem (BAP) by incorporating vessels’ uncertain arrival and service times. A distributionally robust optimization framework is developed, which enables terminal operators to optimize the berth schedule based only on partial information about random variables. The stochastic BAP problem under our framework can be exactly reformulated into a mixed integer conic programming. In particular, given the mean and the variance of each random variable, our reformulation reduces to a mixed integer second-order conic program, which can be further approximated by a mixed integer linear program with the same number of binary variables as the deterministic counterpart. Hence, our framework is able to address stochastic BAPs without imposing much additional complexity compared to the deterministic model. In addition, an extensive numerical study based on a real dataset from Hongkong International Terminal is reported.