改进的生物地理学优化算法在混合流水车间调度中的应用  

Improved biogeography-based optimization algorithm used in solving hybrid flow shop scheduling problem

作　　者：李知聪[1] 顾幸生[1] 

机构地区：[1]华东理工大学化工过程先进控制与优化技术教育部重点实验室,上海200237

出　　处：《化工学报》2016年第3期751-757,共7页CIESC Journal

基　　金：国家自然科学基金项目(61174040;61573144);上海市科委基础研究重点项目(12JC1403400)~~

摘　　要：调度问题是将有限的资源分配给各项不同任务的决策过程,其目的是优化一个或多个目标,它广泛存在于当今大多数的制造和生产系统中。混合流水车间调度问题是一般流水车间调度问题的推广,更接近实际的生产过程。采用一种新型的算法——生物地理学优化算法求解混合流水车间调度问题,通过引入改进策略,增强了算法的全局搜索能力和局部搜索能力,并提高了算法的收敛速度。通过10个标准调度算例的仿真研究,并与遗传算法进行对比,验证了改进后的生物地理学优化算法在求解混合流水车间调度问题方面的优越性。Scheduling problems is a form of decision-making that allocates limited resources to tasks and its goal is to optimize one or more objectives. It exists widely in most of the modem manufacturing and production industries. As a expansion of classic flow shop scheduling problem, hybrid flow shop scheduling problem is closer to the practical production process. This paper presents an improved biogeography optimization algorithm（IBBO） to solve hybrid flow shop scheduling problem. By introducing improved strategy, enhance the ability of global and local search and improve the convergence speed. Simulation experiments based on ten standard scheduling instances and comparison with genetic algorithm verify the excellence of the improved biogeography-based optimization algorithm in solving hybrid flow shop scheduling problem.

关 键 词：生产调度 混合流水车间 生物地理学优化算法 向量编码 深度搜索 

分 类 号：TP278[自动化与计算机技术—检测技术与自动化装置]