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云制造环境下的供应链是新型的供应链,如何选择云制造平台中供应链节点的企业是需要解决的问题之一.针对使节点批次任务总完成时间最小的调度问题,由于蝙蝠算法容易陷入局部最优解,本文使用ROV编码对蝙蝠算法进行了重新编码和解码,并且对其进行了混沌序列初始化和自适应变步长的运算步长改进,提高了原蝙蝠算法的收敛速度和最优解的精度.通过仿真实验,结果表明改进的蝙蝠算法(IBA)较原蝙蝠算法(BA)具有更快的收敛速度、更好的稳定性,有效避免了原蝙蝠算法容易陷入局部最优解的状况,可较好地满足云制造环境下新型供应链动态性、复杂性的要求.
In the cloud manufacturing environment, the supply chain is a new type of supply chain, and how to choose the supply chain nodes in the cloud manufacturing platform is one of the problems to be solved.For the scheduling problem that minimizes the total completion time of batch tasks, the bat algorithm is easy In the local optimal solution, this paper uses the ROV coding to re-encode and decode the bat algorithm, and the chaos sequence initialization and adaptive step size of the algorithm to improve the step-by-step, to improve the convergence speed of the original bat algorithm and the most The simulation results show that the improved bat algorithm (IBA) has faster convergence rate and better stability than the original bat algorithm (BA), and effectively avoids the original bat algorithm being easily trapped in the local optimal solution The situation can better meet the new supply chain dynamics and complexity requirements in the cloud manufacturing environment.