To improve the throughput performance of the autonomous guide vehicle (AGV) unmanned storage system, a two-stage mathematical model was established. The model also considers the equipment configuration of the AGV unmanned storage system and the AGV-picking stations dual resource coordination scheduling problem. In the equipment-task scheduling phase, the model aims at the shortest order completion time, while the equipment configuration and layout model aims at the minimum equipment configuration and operating cost. To solve the two-stage model, a two-layer genetic algorithm was designed. The inner layer algorithm was used to optimize the task scheduling order of AGVs and picking stations. The results of the inner layer algorithm are fed back to the outer model to optimize configuration of the equipment and the picking station's layout. The inner and outer loops are combined to obtain the optimal equipment configuration scheme. Through the simulation study of an enterprise AGV unmanned storage case, the optimal equipment configuration combination and picking stations layout scheme are obtained. Compared with the equipment configuration scheme based on the principle the task scheduling in operation is another key link that affects the picking efficiency of an unmanned warehouse of random task scheduling and the principle of shortest job time first; The model can improve the efficiency of warehouse retrieval and minimize the number of equipment configurations. Finally, the improved genetic algorithm is used to solve the model, and the performance is compared with that of LINGO to verify the effectiveness of the improved algorithm.
According to the problem of the optimization of overall performance of idle spectrum assignment for cognitive radio, it proposed a spectrum assignment method for cognitive radio based on binary weed algorithm. Considering maximizing total network efficiency and fair effectiveness as a criterion, the algorithm is designed and implementation steps are presented. Simulation and analysis compares the solution of the spectrum assignment with Genetic Algorithm and particle swarm algorithm. The results show that the spectrum assignment algorithm based on binary invasive weed algorithm has a higher performance. It can achieve maximizing sum reward and proportional fair better and receiving a better optimal solution.
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