Abstract:

For the resource allocation problem of automatic guided vehicle system, which was composed by both task assigning and route scheduling, a model based on the automatic in-put and out-put system of warehouse was built, and the algorithm with the framework of Particle Swarm Optimization (PSO) and the process of conflict-free routing was proposed to overcome the shortages of just assigning the tasks in sequence. Firstly, the iteration processes were used to search for the optimal scheme of assigning task. Then, the conflict-free routing was employed to obtain the result of resource allocation. Some constraints were added into the solution evaluation mechanism, such as time window, workload balance and conflict-free routes to ensure that the final scheme was feasible. Through the simulation of an automatic in-put system, the traditional scheduling algorithm and the new algorithm were compared. The proposed algorithm can save 10% of the total travelling distance and its balance of task assigning is better. It means that the proposed solution can improve the efficiency of whole system.

Key words: automatic guided vehicle system, resource allocating, task assigning, conflict-free routing scheduling, particle swarm optimization(PSO)

摘要：

针对自动导引车系统中由任务分派及路径规划共同构成的资源分配问题，基于自动化出入库系统建立模型，提出了一种以粒子群优化(PSO)迭代为框架，并加入无冲突路径规划的优化算法，弥补了以往只按顺序分配任务造成的不足。首先通过粒子群的迭代原理寻找最优任务分派方案；然后通过无冲突的路径规划得到资源分配的结果，同时在解的评价机制中加入了时间窗、工作量均衡及路径无冲突等约束条件，保证方案的可行性。通过模拟自动入库系统，与传统的自动导引车系统调度算法进行了对比，实验结果表明，所提算法在总行驶里程上平均节约了10%左右，且任务分配的均衡性更好，系统的整体效率得到了有效的提升。

关键词: 自动导引车系统, 资源分配, 任务分派, 无冲突路径规划, 粒子群算法