Efficient automatic guided vehicle (AGV) scheduling is the key to increase the throughput of automated container terminals. Traditional transport strategies cannot guarantee that AGVs are fully loaded during their traveling between the dock and the container yard, which leads to the insufficient utilization of AGVs. A load-in-load-out AGV route planning mode provides two-way loading between the dock and the container yard and thus improves the efficiency of container terminals. In this paper, a load-in-load-out AGV route planning model is designed with the help of a buffer zone where an AGV can carry at most two containers. A simulated annealing algorithm is used to solve it. Comparisons with the popular generic algorithm and particle swarm algorithm are made. Simulated results show that the proposed algorithm can effectively solve the problem and realize the bi-directional loading of AGVs, which is of great significance to improve the efficiency of the production of automated container terminals.

Container terminals (CTs) play an important role in the modern logistics and transportation industry. The utilization of automated guided vehicles (AGVs) can be effectively facilitated by reducing their empty running. The existing strategies cannot guarantee the full load of AGVs during their transportation because of the complex constraints of container scheduling. This work proposes a double-cycling AGV scheduling model that ensures a full load of AGVs between the quayside and the yard. The objective is to minimize the total waiting time of AGVs and ensure a high loading rate of AGVs by scheduling loading/unloading containers. Furthermore, it takes the randomness of the quay crane’s operational time into consideration. By assigning a time interval to AGVs’ arrival at a quayside, a container scheduling sequence is obtained based on a Hybrid Particle Swarm Optimization (HPSO) algorithm with a penalty function. Via experiments, it shows that the proposed model can obtain the least number of AGVs for container transportation, minimize AGVs’ total waiting time, and ensure the high loading rate of AGVs.

In order to study the Scheduling of quay cranes and tractor in container port, this paper establishes the simulation model of container port's (un)loading operation by use of container port's simulation software FlexTerm. The mixed loading operation and independent (un)loading operation in different quantities of Trucks are simulated. The accomplished time of mission, crane utilization and truck travel distance in two different loading operation modes are compared. According to the simulation result, some suggestions on allocation of truck and (un)loading pattern are provided.

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Abstract. The efficient and economic organization of the railway fuel supply and good diesel locomotive fuel supply management is very significant to ensure the railway transport safety, punctuality, good service, transportation and production costs reduced, and it also improves the economic efficiency. This study comes from the reality of China's rail fuel supply business processes. It established the rail fuel inventory-transportation integrated optimization model whose goal is the total cost of the minimum and uses iterative genetic algorithm to solve it. Then we get the final optimal order quantity, total cost and transport path. The result provides a reference for the optimization of train fuel supply chain.