The Yard Cranes Joint Scheduling Research In Multiple Blocks Under Mixed Storage Mode

Standing as the necessary node in the container transportation network, container terminals hold the ability of shifting the container status between sea and land, such great function of container terminal depends on the seamless coordination of those sub-systems. Container yard plays an important role for its storage function in a container terminal, meanwhile, the most important facility in container yard is yard cranes, the efficiency of yard cranes affect both the efficiency and operational cost of container yard, even the whole container terminal, as a result, it is of both theoretical and practical significance to conduct a research in yard cranes scheduling.This paper makes deep research towards container yard, including the storage mode, the facility craftwork, operational cost and task input information and so on. In order to improve the service level of container yard and reduce the operational cost at the same time, the yard cranes scheduling problem in multiply blocks in a fixed period under mixed storage mode is discussed hereunder. Considering yard cranes resource sharing among multi-blocks, the workload balance of yard cranes and their relationship with the internal and external trucks, a non-liner mathematical model of yard cranes joint scheduling in mixed storage yard is proposed with the objective of minimizing the total cost of yard cranes’ moving, turning cost and the waiting cost of the trucks involved.Considering the characteristic of the model, a two-part chromosome representation is presented based on the handling sequence and workload of yard cranes. Animproved genetic algorithm with the embedding of the available space split strategy and chromosome correction technology is thereafter proposed to solve the problem, in which the available space split strategy is used to deal with the workload balance problem while the chromosome correction technology is used to correct the wrong individuals when the cranes are interfered with others.Numerical experiments are conducted to testify the feasibility and validity of the model and improved genetic algorithm (IGA). First, the comparison between the result of the model and the practical scheduling method is conducted, meanwhile, more empirical comparison ofIGA and real scheduling method and the exact solution from Lingo for the same case are conducted separately, the result shows the model built and IGA function very well, even more experiments under different scales also showed the stability of IGA. At last, some deficiencies of the model and the further research directions are listed. All in all, the research achievement in this paper is very constructive for the yard decision makers when it comes to the yard cranes scheduling.