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Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 3763512, 13 pages
Research Article

An Extended Genetic Algorithm for Distributed Integration of Fuzzy Process Planning and Scheduling

School of Information, Zhejiang University of Finance and Economics, No. 18 Xueyuan Street, Xiasha, Hangzhou 310018, China

Received 23 July 2015; Accepted 14 March 2016

Academic Editor: David Bigaud

Copyright © 2016 Shuai Zhang 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.


The distributed integration of process planning and scheduling (DIPPS) aims to simultaneously arrange the two most important manufacturing stages, process planning and scheduling, in a distributed manufacturing environment. Meanwhile, considering its advantage corresponding to actual situation, the triangle fuzzy number (TFN) is adopted in DIPPS to represent the machine processing and transportation time. In order to solve this problem and obtain the optimal or near-optimal solution, an extended genetic algorithm (EGA) with innovative three-class encoding method, improved crossover, and mutation strategies is proposed. Furthermore, a local enhancement strategy featuring machine replacement and order exchange is also added to strengthen the local search capability on the basic process of genetic algorithm. Through the verification of experiment, EGA achieves satisfactory results all in a very short period of time and demonstrates its powerful performance in dealing with the distributed integration of fuzzy process planning and scheduling (DIFPPS).