A simulation optimization method for deep-sea vessel berth planning and feeder arrival scheduling at a container port. (December 2020) - Jia, Shuai; Li, Chung-Lun; Xu, Zhou
Highlights: A new problem that allocates berths to deep-sea vessels and schedules feeder arrivals is studied. A stochastic optimization model is developed to enhance vessel service while keeping congestion under control. A novel three-phase simulation optimization method is proposed for solving the problem. Computational performance is evaluated on instances generated from the operational data of a container port in Shanghai. Abstract: Vessels served by a container port can usually be classified into two types: deep-sea vessels and feeders. While the arrival times and service times of deep-sea vessels are known to the port operator when berth plans are being devised, the service times of feeders are usually uncertain due to lack of data interchange between the port operator and the feeder operators. The uncertainty of feeder service times can incur long waiting lines and severe port congestion if the service plans for deep-sea vessels and feeders are poorly devised. This paper studies the problem of how to allocate berths to deep-sea vessels and schedule arrivals of feeders for congestion mitigation at a container port where the number of feeders to be served is significantly larger than the number of deep-sea vessels, and where the service times of feeders are uncertain. We develop a stochastic optimization model that determines the berth plans of deep-sea vessels and arrival schedules of feeders, so as to minimize the departure delays of deep-sea vessels and schedule displacements of feeders. The model controls port congestion through restricting the expected queue length of feeders. We develop a three-phase simulation optimization method to solve this problem. Our method comprises a global phase, a local phase, and a clean-up phase, where the simulation budget is wisely allocated to the solutions explored in different phases so that a locally optimal solution can be identified with a reasonable amount of computation effort. We evaluate the performance of the simulation optimization method using test instances generated based on the operational data of a container port in Shanghai.