基于云多目标微粒群算法的多项目调度方法

摘要

For the characteristics of multi-skilled employee constrained multiple projects scheduling problem, a scheduling model is established with the optimization object of minimum multi-project duration and minimum total cost; by applying cloud model into Vector Evaluated Particle Swarm Optimization Based on Pareto(VEPSO-BP), a novel Cloud Multi-Objective Particle Swarm Optimization (CMOPSO) is utilized for solving this problem; In the CMOPSO, the swarm code is comprised of the disposal matrix and the start time of activities, and inertia weight depends on the fitness of the swarm. It tests the performance of CMOPSO with software development projects and compares it with VEPSO-BP. Results show that CMOPSO is able to obtain more superior Pareto non-dominate solutions than VEPSO-BP.%针对多技能员工受限的多项目调度问题的特点,建立了以项目群的总工期及总费用最小为目标的调度模型；将云模型嵌入到基于Pareto的向量评价微粒群算法(VEPSO-BP)中,提出了一种新的云多目标微粒群算法(CMOPSO);该算法结合任务分配矩阵及开工时间设计了微粒编码,能根据微粒适应度自动调整惯性因子；结合软件研发实例测试了CMOPSO的性能,与VEPSO-BP进行了对比；实验结果表明CMOPSO能取得更为丰富且优化效果更好的Pareto非支配解.