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Academic Lecture: Disruption Recovery in Liner Shipping
2015-12-28
Time: 13:00-14:15, Dec. 28, 2015
Venue: Room 335, School of Economics & Management
Speaker: Dr. Chen Li, Hong Kong University of Science and Technology
Abstract:
Container vessels in liner shipping are operated on closed-loop routes following a pre-announced schedule. In practice, there are lots of uncertain factors which may delay a vessel from its original schedule, even if some uncertainty has been considered in the tactical network design. In this talk, we investigate two issues about disruption events.
We first consider the aftermath of a disruption that delays a vessel from its given schedule, aiming to propose an operational-level solution to recover the disrupted schedule. We consider different operational actions such as speeding up, port skipping, and port swapping. For the case where only speeding up is allowed, we approach the problem by nonlinear programming and obtain certain structural results of the optimal recovery schedule. When there is a large delay, we study the problem with more options such as port skipping and swapping and develop dynamic programming algorithms on the discretized time space. We also provide a method to estimate a lower bound of the problem, which enables us to evaluate the relative error caused by the discretized time space in dynamic programming.
Then we move on to consider the case where the vessel faces a forecasted incoming disruption. The decision is to make proactive actions for future uncertainties. One important contribution of this work is to explicitly distinguish two types of uncertainties in liner shipping, and propose different strategies to handle them. The problem can be formulated as a multi-stage stochastic control problem that minimizes the total expected fuel cost and delay penalty. For regular uncertainties, we develop the properties of the optimal control policy; then we show how an emerging disruption may change the control policies. We also provide a wide range of numerical studies to verify the analytical results and demonstrate the effectiveness of the model.
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