考虑负载量均衡的自动拣货系统 AGV 任务分配优化.

To enhance the efficiency of RMFS and reduce operating costs, this paper proposed a double-layer planning model for AGV task allocation considering load balancing, following an analysis of key factors causing congestion in RMFS. The upper layer sought to minimize the total cost, while the lower layer addressed the minimization of the system load standard deviation and AGV idle rate through a multi-objective function. It introduced a modified adaptive genetic algorithm(SAGA) to overcome the challenges of low efficiency and susceptibility to local optima in solving task allocation problems using traditional GA. It integrated the sigmoid function was to transform fitness values and participates in the adaptive adjustment of cross-mutation operators. Additionally, it incorporated catastrophic strategies to prevent premature convergence of the algorithm. Finally, it simulated the proposed algorithm on different scale cases, and the results demonstrate that, in comparison to the genetic algorithm, adaptive genetic algorithm, adaptive disaster genetic algorithm, and ant colony algorithm, the improved algorithm exhibits substantial optimization in the experimental results of various scale cases. This suggests that the enhanced algorithm effectively avoids premature convergence, enhances solution quality and convergence stability, actively balances road network load, and reduces total operating costs. [ABSTRACT FROM AUTHOR]