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Journal of Advanced Transportation
Volume 2017, Article ID 1527858, 12 pages
Research Article

A Variable Interval Rescheduling Strategy for Dynamic Flexible Job Shop Scheduling Problem by Improved Genetic Algorithm

1School of Mechanical and Automotive Engineering, Anhui Polytechnic University, Wuhu 241000, China
2Department of Electrical and Computer Engineering, University of Detroit Mercy, Detroit, MI 48221, USA

Correspondence should be addressed to Lei Wang; moc.621@0002ieladgnaw

Received 27 April 2017; Accepted 14 June 2017; Published 19 July 2017

Academic Editor: Rongxin Cui

Copyright © 2017 Lei Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In real-world manufacturing systems, production scheduling systems are often implemented under random or dynamic events like machine failure, unexpected processing times, stochastic arrival of the urgent orders, cancellation of the orders, and so on. These dynamic events will lead the initial scheduling scheme to be nonoptimal and/or infeasible. Hence, appropriate dynamic rescheduling approaches are needed to overcome the dynamic events. In this paper, we propose a dynamic rescheduling method based on variable interval rescheduling strategy (VIRS) to deal with the dynamic flexible job shop scheduling problem considering machine failure, urgent job arrival, and job damage as disruptions. On the other hand, an improved genetic algorithm (GA) is proposed for minimizing makespan. In our improved GA, a mix of random initialization population by combining initialization machine and initialization operation with random initialization is designed for generating high-quality initial population. In addition, the elitist strategy (ES) and improved population diversity strategy (IPDS) are used to avoid falling into the local optimal solution. Experimental results for static and several dynamic events in the FJSP show that our method is feasible and effective.