An improved particle swarm optimization-based approach for production scheduling problems

	An improved particle swarm optimization-based approach for production scheduling problems
	Zhao, Fuqing 1; Mang, Qiuyu 1; Yang, Yahong 2
	2006
关键词	job-shop scheduling problem particle swarm optimization simulated annealing
页码	2279-+
英文摘要	Job-shop scheduling problem(JSSP) is very common in a discrete manufacturing environment. It deals with multi-operation models, which are different from the flow shop models. It is usually very hard to find its optimal solution. In this paper, a new hybrid approach in dealing with this job-shop scheduling problem based on particle swarm optimization(PSO) and simulated. anneating (SA) technique is presented. PSO employs a collaborative population-based search, which is inspired by the social behavior of bird flocking. It combines local search(by self experience) and global search(by neighboring experience), possessing high search efficiency. SA employs certain probability to avoid becoming trapped in a local optimum and the search process can be controlled by the cooling schedule. The hybrid algorithm combines the high speed of PSO with the powerful ability to avoid being trapped in local minimum of SA. We compare the hybrid algorithm to both the standard PSO and SA models, computer simulations have shown that the proposed hybrid approach is of high speed and efficiency.
会议录	IEEE ICMA 2006: PROCEEDING OF THE 2006 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION, VOLS 1-3, PROCEEDINGS
会议录出版者	IEEE
会议录出版地	345 E 47TH ST, NEW YORK, NY 10017 USA
语种	英语
资助项目	Natural Science foundation of GANSU province[3ZS041-A25-020][3ZS042-B25-005]
WOS研究方向	Automation & Control Systems ; Robotics
WOS记录号	WOS:000241434602107
内容类型	会议论文
源URL	[http://119.78.100.223/handle/2XXMBERH/38256]
专题	国际合作处（港澳台办）土木工程学院
通讯作者	Zhao, Fuqing
作者单位	1.Lanzhou Univ Technol, Sch Comp & Commun Engn, Lanzhou 730050, Gansu, Peoples R China 2.Lanzhou Univ Technol, Coll Civil Engn, Lanzhou 730050, Gansu, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Fuqing,Mang, Qiuyu,Yang, Yahong. An improved particle swarm optimization-based approach for production scheduling problems[C]. 见:.