In order to improve the efficiency of operation in container terminal and reduce the influence of uncertain interference factors on the operation of delivery trucks, a method of processing the interference factors by rolling-window strategy was proposed, a mixed integer model with the goal of minimizing the operation delay penalty cost and yard crane movement cost was proposed, and Genetic Algorithm (GA) was used to solve the model. Firstly, rolling-window strategy was used to obtain the scheduling scheme of the delivery trucks in the case of no interference factors. Secondly, when the interference factor occurred, the rolling-window rescheduling mechanism was triggered to reschedule the operation order of delivery trucks. Finally, the optimal scheduling scheme in each window was calculated, and the optimal operation plan in the total planning time was proposed. By comparing and analyzing the results of case solving in different scenarios, the experimental results show that the minimum operation cost under the rolling-window strategy is 9% lower than that under the traditional operation mode in the case without interference, and in the case with interference, the rolling-window strategy makes the cost reduced by 15% compared to the traditional operation mode, which verifies the effectiveness of the algorithm and the superiority of the rolling-window strategy for the delivery truck operation.

The robust optimal control of single Conveyor-Serviced Production Station (CSPS) with uncertain service rate was researched. Under the cases where only the interval of service rate was given and the look-ahead range was controllable, the optimal robust control problem could be described as a mini-max problem by using Semi-Markov Decision Process (SMDP) with uncertain parameters. Global optimization method was adopted to derive the optimal robust control policy when states were dependent. Firstly, the worst performance value was obtained under fixed policy by genetic algorithm. Secondly, according to the obtained worst performance value, the optimal robust control policy was achieved with simulated annealing algorithm. The simulation results show that there is little difference between optimal performance cost of the system whose service rate is fixed as the mean of interval and optimal robust performance cost of the CSPS system with uncertain service rate. Moreover, the difference is getting smaller when the uncertain interval narrows and it means that the global optimization algorithm works effectively.

In order to solve the problem of not using the product heterogeneity well in recommendation algorithm, a modified mass diffusion algorithm was presented by considering the effect of the object popularity information on the user preference prediction. By introducing a tunable parameter of product popularity and simulating the mass diffusion process on the user-product bipartite network, the effect of the product popularity was quantitatively characterized. The experimental results on three empirical data sets which named MovieLens, Netflix and Last.FM show that, compared with the traditional mass diffusion method, the proposed algorithm can enhance the average ranking score by 25.6%, 10.96% and 1.2% respectively, and increase the diversity of the recommendation lists by 59.30%, 53.07% and 8.59% respectively. The proposed non-equilibrium mass diffusion algorithm can get more practical results.

For the purpose of balancing and decreasing the routing energy consumption of Wireless Sensor Network (WSN), this paper put forward a network coding based routing policy with friendly energy consumption feature. Three restricts on network lifetime, data flow and multicasting flow in WSN were analyzed in detail. Simulation results show this policy can balance energy consumption better than other policies, and can notably extend the lifecycle of WSN.