Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.rcim.2004.12.003
Title: Application of genetic algorithm to computer-aided process planning in distributed manufacturing environments
Authors: Li, L. 
Fuh, J.Y.H. 
Zhang, Y.F. 
Nee, A.Y.C. 
Keywords: Computer-aided process planning
Distributed manufacturing system
Genetic algorithm
Multiple factories
Issue Date: Dec-2005
Source: Li, L.,Fuh, J.Y.H.,Zhang, Y.F.,Nee, A.Y.C. (2005-12). Application of genetic algorithm to computer-aided process planning in distributed manufacturing environments. Robotics and Computer-Integrated Manufacturing 21 (6) : 568-578. ScholarBank@NUS Repository. https://doi.org/10.1016/j.rcim.2004.12.003
Abstract: In a distributed manufacturing environment, factories possessing various machines and tools at different geographical locations are often combined to achieve the highest production efficiency. When jobs requiring several operations are received, feasible process plans are produced by those factories available. These process plans may vary due to different resource constraints. Therefore, obtaining an optimal or near-optimal process plan becomes important. This paper presents a genetic algorithm (GA), which, according to prescribed criteria such as minimizing processing time, could swiftly search for the optimal process plan for a single manufacturing system as well as distributed manufacturing systems. By applying the GA, the computer-aided process planning (CAPP) system can generate optimal or near-optimal process plans based on the criterion chosen. Case studies are included to demonstrate the feasibility and robustness of the approach. The main contribution of this work lies with the application of GA to CAPP in both a single and distributed manufacturing system. It is shown from the case study that the approach is comparative or better than the conventional single-factory CAPP. © 2005 Elsevier Ltd. All rights reserved.
Source Title: Robotics and Computer-Integrated Manufacturing
URI: http://scholarbank.nus.edu.sg/handle/10635/59555
ISSN: 07365845
DOI: 10.1016/j.rcim.2004.12.003
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