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.