Pareto Based Vessel Schedule Disruption Recovery Model

Container liner shipping is a special maritime transport, it has both certainty and uncertainty. The certainty refers the ship sailing schedules which are announced by liner companies before the ship sailing determined, it is not changed easily during the voyage. While, the uncertain refers that when the ship is sailing, it always encounter some disruption factors. These factors may come from the internal experiment, also may come from the external experiment. These factors may make the ship deviate from the announced sailing schedule if the liner companies do not take any measures. They can also make the ship cannot reach the ports of call on the time, lead to the cargo delay in delivery, increase the direct and indirect losses of cargo owners, if the things often occur, the reputation of the liner companies will be damaged. But if you spare no effort to recovery schedules, the cost may be very high, the liner companies cannot accept.Because of the serious impact of ship delay to the liner companies, and the ships delay events always occur when they are sailing. How to deal with these disruption events is a very important thing to the liner companies. But now, it is always handled by the experience of the operators of the liner companies. While the resolution may be not the best only rely on the intuition of the liner companies. So, research in this area is necessary.This article takes advantage of the idea and theory of the disruption recovery. It both considers sailing schedule deviation and recovery cost. First, we create a model which requires the recovery cost least and a time-based recovery model which make the sailing schedule deviation least. These two models based on schedule reliability analysis-. schedule restoration strategies and schedule recovery cost analysis, then， we use Pareto to integrate these two models, and we can solve by using lingo and human-computer interaction. Through empirical analysis, we can verify the reliability and practical value of the model. And we also make evaluations to the result of the optimization the model, finally we also make our views in the future research directions in the field.