Storage Optimization For Export Containers In Container Yard With Delivery Sequence Uncertainties

With the in-depth development of economic globalization in recent30years, the global foreign trade development rapidly, thus promoting the world’s seaborne continues to mature and grow. As the main mode of transport by sea-freight container transportation has become an irresistible trend. Container terminal is a hub node for global container transportation network, the rapid development of container transport have presented a new challenge for whether its throughput or operating efficiency. China’s container terminals have been constantly evolving and constructing, the hardware conditions of container terminals as scale and equipment have been more perfect, but the software lacks systematic, modern job planning and scheduling approach. Building modern container terminals has been an important part in China’s logistics infrastructure.Container terminal yard is the place for exchanging, storage and stockpiling of import and export containers. Rational allocation and utilization of container yard’s equipment resources has become a yard dispatcher’s key issue. Export container stockpiling is an important part of container terminal operations, its operation fluency and ability directly affect the container terminals in operational efficiency and effectiveness. With the substantial increase in container terminal throughput, the effect of export container relocation on container yard operational efficiency are becoming increasingly significant. Since many customers unpredictable reasons, the container delivery order is random, but also the container loading sequence can not be determined, which inevitably led to a certain number of relocations. How to effectively avoid or reduce the occurrence of container relocation has become the key to improve the operational efficiency of container yard.Firstly, considering the randomness of customer delivery sequence, using Markov prediction methods combined with the customer’s reservation information to transfer random delivery sequence into forecast sequence guaranteed by a certain probability. And then based on common practice in the industry and the analysis of the actual operation in container yard, a mathematical model was established with the objective of minimizing the number of container relocation, and a genetic algorithm was developed to solve it. In view of the deviation of Markov prediction series and the real delivery sequence, a heuristic rule based on real-time scheduling was proposed to amend them. After a series of simulation experiments comparing with the existing scheduling rules and the exact solution respectively, verify the effectiveness and practicality ot the stockpiling model and the optimization algorithm.