Study On Container Port’s Yard Capacity Based On Two-stage Stochastic Programming

As the space for operation, transfer, storage and custody in container terminal, container yard is an important part of the container port land arrangement. When terminal equipment and facilities are determined, yard area will be development bottlenecks. With the rapid growth in container throughput at the same time, how to coordinate the yard utilization rate and turnover rate in order to determine the appropriate capacity and yard area have always been the focus of attention of domestic and foreign scholars.Before, research on yard capacity at home and abroad are deterministic problem, or a trend curve from large number of data. Actually, the factors impacting the container port’s yard capacity changes very different. On the basis of the analysis of loading and unloading process, considering types of containers, handling technology, unit space cost and of yard’s income, a two-stage recourse stochastic programming was established, in which yard’s maximum profits and maximum number of containers on the yard were considered as the target and random variable respectively. To solve the required yard capacity and determine the number of flat containers, maximum profit was considered as the target at the first stage, minimum penalty function was considered as the target at the second stage. Compared to deterministic programming, stochastic programming fit the actual situation more. It’s able to effectively put aside the interference of the uncertainties in the results, save construction costs, and draw the affection of yard profit and cost on yard area and the affection of the daily stockpiles’randomized variation on the optimal capacity of the container yard.The results of an example show that the two stage stochastic programming’s result is less than the standard’s. Through the analysis of the example, we draw conclusions that stochastic programming can effectively reduce the impact by uncertainties; save resource costs; game with yard’s income, cost and handling technology to obtain optimal number of flat containers. It’s concluded that in the case of a certain throughput, optimal capacity is largest when the daily stockpiles follow a normal distribution; in the case of a certain capacity, throughput capacity of the container yard is largest when the daily stockpiles follow negative exponential distribution.