A dynamic schedule methodology for discrete job shop problem based on Ant Colony Optimization

Author

Meilin, Wang ; Xiangwei, Zhang ; Qingyun, Dai ; Jinbin, He

Author_Institution

Fac. of Inf. Eng., Guangdong Univ. of Technol., Guangzhou, China

fYear

2010

fDate

16-18 April 2010

Firstpage

306

Lastpage

309

Abstract

Job shop scheduling is an important problem in implementing Manufacturing Execution System (MES). In this paper, an algorithm based on Ant Colony Optimization (ACO) is proposed to solve a discrete job shop scheduling problem (DJSSP). A dynamic schedule methodology is applied to DJSSP. The main concept is that the real-time production status from the MES IDT (Intelligent Data Terminal) is passed to the pheromone updating rule to guide the transfer of the work pieces. MES IDE is a hardware platform deployed in the shop floor with the aim of real-time and wireless manufacturing. This methodology has been put into real-life practice in several manufacturing enterprises according to its universality. It has achieved excellent efficiency in terms of real-time scheduling and planning, JIT (Just-In-Time) manufacturing etc.

Keywords

cooperative systems; job shop scheduling; just-in-time; optimisation; ant colony optimization; discrete job shop scheduling problem; dynamic schedule methodology; intelligent data terminal; just-in-time manufacturing; manufacturing execution system; realtime scheduling; Algorithm design and analysis; Ant colony optimization; Convergence; Dynamic scheduling; Hardware; Job shop scheduling; Manufacturing; Processor scheduling; Production; Scheduling algorithm; ACO; Discrete; Job Shop Scheduling; MES;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Management and Engineering (ICIME), 2010 The 2nd IEEE International Conference on

Conference_Location

Chengdu

Print_ISBN

978-1-4244-5263-7

Electronic_ISBN

978-1-4244-5265-1

Type

conf

DOI

10.1109/ICIME.2010.5477648

Filename

5477648