基于遗传算法的制造网络服务质量优化

服务质量的合成是面向服务架构系统面临的重要挑战,评价面向服务架构制造网络最优化的典型标准是加工时间成本和系统响应时间.文中提出了一种应用遗传算法解决制造网络服务质量优化问题的新方法.首先用着色Petri网和排队论分别对制造网络的物流和信息流进行建模,然后应用遗传算法对该模型进行调度并获取服务质量的近似最优解.在该算法中,染色体采用分段基因编码,它们分别是制造网络物流和信息流调度方案的规则与加权系数的组合;遗传操作包括选择、交叉、变异3种类型.在每一代种群中,通过仿真得到与每个染色体相对应的各项性能指标值,以模糊综合评判方法求取制造网络服务质量适应度的函数值.实验结果表明,该方法能有效优化制造网络的服务质量.

QoS Optimization of Manufacturing Network Based on Genetic Algorithm

The composition of quality of service(QoS) is a challenge to the system with service-oriented architecture(SOA).The classical optimal criteria for the SOA of a manufacturing network(MN) are the time cost of production and the response time of the system.In this paper,a new method is proposed to optimize the QoS of a MN based on the genetic algorithm(GA).In the proposed method,first,the material and information flows in a MN are modeled respectively with the colored Petri net(CPN) and the queuing theory.Then,the models are scheduled based on the GA,and a nearly optimal solution to the QoS is thus obtained.In the algorithm,each chromosome is made up of two gene-coding sections that respectively correspond to the priority rules and the weight coefficients and denotes the schedule solution to the material and information flows in the MN,and the choice,the crossing and the variation are all involved in the genetic operation.Moreover,the performance index values of every chromosome in each generation are gained by simulation.The QoS fitness of the MN is then computed by means of fuzzy comprehensive evaluation.Simulation results show that the proposed method can effectively optimize the QoS of a manufacturing network.