云制造环境下子任务在分布式机器人中的调度方法

摘要

Cloud manufacturing (CMF) provides a sharing and cooperation platform for efficient utilization of distributed manufacturing resources(industrial robots).However,effective scheduling of tasks or subtasks to these robots is a challenging problem.Based on the analysis on task decomposition and processing procedure,a CMF system model for efficiently exploiting the robotswas proposed,so manufacturing robots of different locations and functions can cooperatively handle a batch of tasks.Specifically,this paper considered the performance of four distributed robots deployment methods,including random deployment,robotbalanced deployment,function-balanced deployment and location-aware deployment.Further,based on the modeling of production cost and delay,three subtask-scheduling strategies were derived with genetic algorithm for three optimization objectives,including load-balance of robots,minimizing overall cost and minimizing overall processing time.Simulation results demonstrate that each strategy can achieve the relevant optimization objective respectively.The results also show that distance between two manufacturing centers can influence the overall cost,and location-aware deployment leads to smaller transportation cost.Location-aware deployment and function-balanced deployment lead to smaller overall processing time for low-workload state and high-workload state respectively.%云制造模式为制造资源(如:机器人)的分布式利用提供了共享合作平台,如何将生产任务高效调度到各机器人是一个复杂的问题.基于对任务结构和过程的分析提出云制造系统模型,使不同位置和功能的机器人能协作处理一批任务.同时探讨多种机器人部署方式,包括随机部署、机器人均衡部署、功能均衡部署和方位意识部署.基于对生产代价和时延的建模,采用遗传算法实现三种调度策略,优化目标分别为机器人负载均衡、最小化总生产时延和最小化总生产开销.仿真结果表明这三种策略能分别实现对应的优化目标,制造中心的物理距离对总生产开销有影响,而方位意识部署能明显降低总生产开销.在系统轻载和重载状态下,方位意识部署和功能均衡部署分别实现较小的总生产时延.