基于改进细菌觅食算法的堆垛机调度

Stacker Scheduling Based on Improved Bacterial Foraging Algorithm

针对自动化立体仓库堆垛机作业调度问题,构建以运行时间及碳排放量最小化为优化目标的数学模型,并提出一种具有万有引力效应的情绪化细菌觅食算法。针对细菌觅食算法存在的趋化步长的不确定性,在趋化过程中引入高斯分布搜索机制更新个体位置,避免算法陷入局部最优;在群集步骤中引入万有引力效应,促进个体间信息的交互能力,提升算法的收敛性;引入情绪化突变权重机制,利用权重策略更新个体速度,并赋予细菌个体情绪感知因子以实现速度的自适应调整,避免算法过早收敛。仿真及算法对比结果表明,所提算法在解决堆垛机调度问题上可以提升算法搜索性能及自适应运行速度,同时减少碳排放,提高设备利用率。

In view of the job scheduling problem of the stacker in the automated warehouse, a mathematical model is built to minimize the running time and carbon emissions, and an emotional bacteria foraging algorithm with gravitational effect is proposed. In view of the uncertainty of chemotaxis step length in bacterial foraging algorithm, Gauss distribution search mechanism is introduced to update the individual position in the process of chemotaxis to avoid the algorithm falling into local optimum. The gravitational effect is introduced in the cluster step to promote the ability of information interaction between individuals and improve the convergence of the algorithm. The weight mechanism of emotional mutation is introduced, the weight strategy is used to update the individual speed, and the individual emotion perception factors of bacteria are given to realize the adaptive adjustment of the speed, so as to avoid premature convergence of the algorithm. Results of simulation and algorithm comparison show that the algorithm proposed in this paper can improve the search performance and adaptive running speed of the algorithm in solving the scheduling problem of the stacker,reduce carbon emissions and improve equipment utilization.