基于遗传模拟退火算法的堆垛机路径优化

Path Optimization of Stacker Crane Based on Genetic Simulated Annealing Algorithm

自动化立体仓库中的堆垛机要处理大量货物的进出库,因此需要不断提高堆垛机效率,由此提出一种新的堆垛机路径规划算法。首先,加入堆垛机最大装载件数这一约束条件,建立以出库时间最短为目标的数学调度模型。然后,在全局搜索能力强的遗传算法(GA)基础上,引入局部搜索能力强的模拟退火算法(SA),得到一种新的遗传模拟退火算法(SAGA)。最后,结合实际应用案例,在MATLAB中利用遗传模拟退火算法(SAGA)对其进行仿真。仿真结果表明,相比遗传算法的求解结果,该算法对货物出库时间的优化提升度增加了9.7%,并且该算法有更好的收敛性,具有一定的可行性。

In this paper, we put forward a new stacker crane path planning algorithm. First, we set up a mathematical scheduling model with the shortest outbound shipment time as the objective by adding in the maximum load of the stacker crane as a constraint. Then, we combined the genetic algorithm(GA) which has strong global search ability with the simulated annealing algorithm which has strong local search ability to derive the new genetic simulated annealing algorithm(SAGA). Finally, in connection with an empirical case, we realized the SAGA using MATLAB and found that, compared to the conventional genetic algorithm, SAGA could yield better outbound shipment time and have better convergence, proving its feasibility.