Scheduling of Yard Truck Considering Loading and Unloading Simultaneously in an Underground Container Logistics System

Abstract

With the rapid growth of containers and scarce of land, the underground container logistics system (UCLS) presents a logical alternative for container terminals to better protect the environment and relieve traffic pressure. The operating efficiency of container terminals is one of the competitive edges over other terminals, which requires UCLS to be well integrated with the handling process of the storage yard. In UCLS, yard trucks (YTs) serve different handling points dynamically instead of one fixed handling point, and yard cranes (YCs) perform loading and unloading simultaneously. To minimize the total time of handling all containers in UCLS, the mixed integer programming problem is described and solved using an adaptive genetic algorithm (AGA). The convergence speed and accuracy of AGA are demonstrated by comparison with conventional genetic algorithm (GA). Additionally, AGA and CPLEX are compared with different scale cases. Experimental results show that the proposed algorithm is superior to CPLEX in resulted solutions and calculation time. A sensitivity analysis is provided to obtain reasonable numbers of YTs for scheduling between handling points and the storage yard in UCLS.