柔性调度多目标优化的混合粒子群算法研究

Hybrid Particle Swarm Algorithm in the Application of Multi-objective Flexible Scheduling

研究多目标柔性调度问题,提出了一种基于多目标粒子群优化算法和局域搜索技术相结合的新算法。建立以最大完成时间、机器总负载和最大机器负载为目标函数的多目标数学调度模型。将粒子群算法运用到机器分配子问题;局域搜索技术运用到工序排列子问题,对粒子群算法得到的结果进行再调度。粒子群优化算法的全局搜索能力与局域搜索技术相结合,加快了算法的收敛速度。最后通过与其他算法进行测试比较,验证了该算法的可行性及有效性。

Multi-objective flexible scheduling problems are studied on the basis of analysis of the characteristics of the problem.A new hybrid algorithm is proposed based on multi-objective particle swarm optimization（PSO） and local search technology.Makespan,total workload and max workload are considered as objective functions.The mathematical model of multi-objective scheduling is built.Particle swarm optimization is used for allocating machines.Local search strategies are applied to the sequence sub-problem and reschedule the result obtained from particle swarm algorithm.Convergences of our algorithm are accelerated with the combination of the global search ability of PSO and local search technology.Finally,compared with other optimization algorithms,simulation results verify the feasibility and effectiveness of the proposed algorithm.