Abstract: In order to find out the shortest time that traverses all blocks to solve the circular orbit RGV（rail guided vehicles） scheduling problem in automated warehouse, this paper analyzes the main influencing factors and then proposes the goal that find out the shortest path and the least clogging scheme. Mathematical model is established and rule-based Genetic Algorithm is designed to solve the problem. This paper uses adaptive crossover and mutation probability to replace traditional fixed parameters to solve the problem that genetic algorithm is easy to fall into the phenomenon of local optima. An improved dynamic exploring process is advanced for the multi-objective optimization. In the end, the genetic operators are analyzed by experimental comparisons and the algorithm is validated by experiments.

Key words: rail guided vehicles, annular shuttle scheduling, genetic algorithm, dynamic exploring process

摘要： 针对自动化仓库中环形轨道RGV（有轨制导车辆）调度问题，以任务最短完成时间为目标，分析其主要影响因素。在此基础上提出路径最短和堵塞次数最少两个优化目标，并建立数学模型，设计基于规则的遗传算法求解。使用自适应的交叉变异概率代替传统遗传算法中的固定参数，改善遗传算法易陷入局部最优解的现象。同时，为解决多目标优化求解问题，提出了改进的自适应权重的求解方案。通过Matlab仿真实验分析比较算法性能，验证了算法的有效性。

关键词: 有轨制导车辆（RGV）, 环形穿梭车调度, 遗传算法, 自适应权重