In order to improve the efficiency of container port services and reduce the tardiness costs of ship services, a new mathematical model was established with the objective of minimizing the sojourn time of the ships and the total tardiness for the tug-berth joint scheduling problem under the established conditions of port hardwares (berths, tugboats, quay cranes), and a hybrid algorithm was designed for solving it. Firstly, the serial hybrid strategy of Quantum Genetic Algorithm (QGA) and Tabu Search (TS) algorithm was analyzed and determined. Secondly, according to the characteristics of the joint scheduling problem, the update strategy of dynamic quantum revolving gate was adopted when solving key problems in the executing process of the hybrid algorithm (chromosome structure design and measurement, genetic manipulation, population regeneration, etc.). Finally, the feasibility and effectiveness of the algorithm were verified by the production examples. The experimental results show that compared with results of manual scheduling, the sojourn time of the ships and total tardiness of the hybrid algorithm are reduced by 24% and 42.7% respectively; compared with the results of genetic algorithm, they are reduced by 10.9% and 22.5% respectively. The proposed model and algorithm can not only provide optimized operation schemes for berthing, unberthing as well as loading and unloading operations of port ships, but also increase the port competitiveness.

Content offloading technology based on mobile edge computing can effectively reduce the traffic pressure on the backbone network and improve the end user's experience. A content offloading scheme of greedy strategy was designed for the heterogeneous contact rate between end users and small base stations. Firstly, the content optimal offloading problem was transformed into the content maximum delivery rate problem. Secondly, the maximum delivery rate problem was proved to satisfy the submodularity. On this basis, the greedy algorithm was used to deploy the content. The algorithm was able to guarantee its optimality with the probability (1-1/e). Finally, the impacts of content popularity index and cache size on different offloading schemes were analyzed in detail. The experimental results show that the proposed scheme improves the content delivery rate and reduces the content transmission delay at the same time.