A hybrid particle swarm optimisation algorithm and fuzzy logic for process planning and production scheduling integration in holonic manufacturing systems

doi:10.1080/09511920903207472

	A hybrid particle swarm optimisation algorithm and fuzzy logic for process planning and production scheduling integration in holonic manufacturing systems
	Zhao, Fuqing 1; Hong, Yi 1; Yu, Dongmei 1; Yang, Yahong 2; Zhang, Qiuyu 1
刊名	INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
	2010
卷号	23 期号:1 页码:20-39
关键词	holonic manufacturing systems (HMS) process planning production scheduling particle swarm optimisation differential evolution (DE)
ISSN号	0951-192X
DOI	10.1080/09511920903207472
英文摘要	Modern manufacturing systems have to cope with dynamic changes and uncertainties such as machine breakdown, hot orders and other kinds of disturbances. Holonic manufacturing systems (HMS) provide a flexible and decentralised manufacturing environment to accommodate changes dynamically. HMS is based on the notion of holon, an autonomous, co-operative and intelligent entity which is able to collaborate with other holons to complete the tasks. HMS requires a robust coordination and collaboration mechanism to allocate available resources to achieve the production goals. In this paper, a basic integrated process planning and scheduling system, which is applicable to the holonic manufacturing systems is presented. A basic architecture of holonic manufacturing system is proposed from the viewpoint of the process planning and the scheduling systems. Here, the process planning is defined as a process to select suitable machining sequences of machining features and suitable operation sequences of machining equipments, taking into consideration the short-term and long-term capacities of machining equipments. A fuzzy inference system (FIS), in choosing alternative machines for integrated process planning and scheduling of a job shop in HMS, is presented. Instead of choosing alternative machines randomly, machines are being selected based on the machine's capacity. The mean time for failure (MTF) values are input in a fuzzy inference mechanism, which outputs the machine reliability. The machine is then being penalised based on the fuzzy output. The most reliable machine will have the higher priority to be chosen. In order to overcome the problem of un-utilisation machines, sometimes faced by unreliable machine, the hybrid particle swarm optimisation (PSO) with differential evolution (DE) has been applied to balance the load for all the machines. Simulation studies show that the proposed system can be used as an effective way of choosing machines in integrated process planning and scheduling.
资助项目	863 High Technology Plan Foundation of China[2002AA415270] ; National Natural Science Foundation of China[2001BA201A32] ; Natural Science foundation of GANSU province[3ZS062-B25-033]
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science
语种	英语
出版者	TAYLOR & FRANCIS LTD
WOS记录号	WOS:000273610900002
状态	已发表
内容类型	期刊论文
源URL	[http://119.78.100.223/handle/2XXMBERH/35311]
专题	计算机与通信学院国际合作处（港澳台办）
通讯作者	Zhao, Fuqing
作者单位	1.Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou 730050, Gansu, Peoples R China 2.Lanzhou Univ Techchnol, Coll Civil Engn, Lanzhou 730050, Gansu, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Fuqing,Hong, Yi,Yu, Dongmei,et al. A hybrid particle swarm optimisation algorithm and fuzzy logic for process planning and production scheduling integration in holonic manufacturing systems[J]. INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING,2010,23(1):20-39.
APA	Zhao, Fuqing,Hong, Yi,Yu, Dongmei,Yang, Yahong,&Zhang, Qiuyu.(2010).A hybrid particle swarm optimisation algorithm and fuzzy logic for process planning and production scheduling integration in holonic manufacturing systems.INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING,23(1),20-39.
MLA	Zhao, Fuqing,et al."A hybrid particle swarm optimisation algorithm and fuzzy logic for process planning and production scheduling integration in holonic manufacturing systems".INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 23.1(2010):20-39.