In order to solve the path conflict problem of automated guided vehicles in horizontal handling operations of automated container terminals, a path optimization method based on tempo-spatial network was proposed. For single transportation demand, firstly, the road network was discretized into grid network, and a tempo-spatial network which is updateable by time was designed. Secondly, the minimum of the completion time was taken as an objective, and a vehicle path optimization model was established based on the set of available road segments under tempo-spatial network. Finally, the shortest path algorithm was used on the tempo-spatial network to obtain the shortest path. For multiple transportation demands, in order to avoid conflicts between paths, the tempo-spatial network of the next transportation demand was updated according to the path planning results of the current transportation demand, and the path planning meeting the collision avoidance and congestion easing conditions were finally obtained through iteration. In the calculation experiment, compared with the basic shortest path solution strategy (solving algorithm P), the proposed method has the number of collisions reduced to 0 and the minimum relative distance always greater than the safety distance; compared with the parking waiting solution strategy (solving algorithm SP), the proposed method has total delay time of task reduced to 24 s, the proportion of delayed tasks and average congestion rate of road network significantly reduced, and the maximum reduction were 2.25% and 0.68% respectively. The experimental results show that the proposed method can effectively solve large-scale conflict-free path planning problems and significantly improve the operation efficiency of automated guided vehicles.

Based on the idea of using reversible transformation and affine transformation to generate S-box, a method to produce dynamic S-box by changing the rows of affine transformation array was proposed. The cryptography properties of single S-box were analyzed. The experimental results show that the cryptography properties of single S-box achieve the security standard of S-box of AES. Moreover, the dynamic differential probability, dynamic linear probability, dynamic non-linear degree, dynamic algebra times and impossible differential numbers of the dynamic S-box produced by the proposed method were analyzed. Theoretical analysis shows that the S-box produced by proposed method possesses good cryptography properties, and experiments also prove that the S-box so produced has good dynamic non-linear degree, dynamic differential and impossible differential properties. Finally, by analyzing the hardware implementation efficiency of the generation of the dynamic S-box, the proposed method has high hardware implementation efficiency.

For the scheduling optimization problem of cross-over twin Automated Stacking Crane (ASC), a multi-objective mathematical formulation model was proposed with considering the conflicts between two ASCs while reaching the same bay. The operation sequences of ASC was optimized, the optimal task sequence, task finish time and ASC's idle time after avoiding the conflict were gained and the practicality of optimized model was proved. Four experiment scenarios were performed to further analyze the efficiency difference between twin ASC and single ASC, and the effects of parameter changes. The results illustrate that the utilization rate of equipment of twin ASC is 107% lower, and the operation efficiency is 35% higher than single ASC. The ASV's total operation time will be decreased while the number of containers decreases and the travel speed of ASC increases. While the ratio of storages and retrieves of containers is 1, the optimized tasks' total operation time and ASC's idle time will be gained. It shows that the container terminals can improve their operation through adjusting the ratio of storages and retrieves during a short period or optimizing ASC travel speed.

Pre-positioning facilities for collecting tents held by communities is helpful to improve the efficacy of tents collection in post-earthquake relief. A problem for pre-positioning facilities that collect the tents held by commnities considering various earthquake scenarios was studied. A bi-stage stochastic programming model was formulated for the facility location problem. The model aimed at minimizing economic costs and time-related costs. Considering the effects of weights and other parameters on the solutions, five experiments were performed and analyzed. A case was used to verify the practicability of the developed model that located the facilities of tents collection from communities in Pudong New District, Shanghai. Finally, a scenario based on Ya'an earthquake was used to analyze the influence of parameters on the solution under such a certain scenario.

The problem of hub construction with considering the uncertainty of hub construction cost and freight flow was discussed in this paper. A mixed integer linear programming model based on life-cycle cost theory was built to minimize the total cost for Less-than-TruckLoad (LTL) logistical hub-and-spoke network, and uncertainty decision making method based on the improved minimax regret value was proposed. Through numerical examples, the effect of investment period, coefficient of discount between hubs and uncertain hub construction cost on network design were analyzed. The experimental results show that operating cost obtained by the improved uncertainty decision making method is reduced by an average of 2.17% compared with other five scenes, which indicates that this method can decrease the total operation cost of hub-and-spoke network.

In order to solve the large-scale integral dynamic scheduling model of continuous berths and quay cranes problem, a method based on rolling-horizon strategy was proposed. A multi-objective optimization model was established under the minimization of total penalty costs of deviation to preferred berthing positions, berthing delays and departure delays. Then the scheduling process was divided into a series of continual scheduling intervals according to the dynamic arrival sequences. Meanwhile, the movement strategy of windows and parameter renew strategy were designed. The input parameters of the model in next window were renewed according to the optimal results of each window. The model for each interval was solved by choosing appropriate rolling window and freezing the quantity of vessels. The holistic optimal solution was obtained by rolling and combining the results of each window. Finally, a case study indicated that the rolling schedule can solve large-scale scheduling problems. The efficiency of the proposed approach relates to the size of rolling window, frozen ship quantity and rolling frequency.