Please use this identifier to cite or link to this item: https://doi.org/10.1109/4235.687888
Title: Using genetic algorithms in process planning for job shop machining
Authors: Zhang, F.
Zhang, Y.F. 
Nee, A.Y.C. 
Keywords: Computer-aided process planning
Genetic algorithm
Job shop
Operation selection
Operation sequencing
Optimization
Issue Date: 1997
Source: Zhang, F.,Zhang, Y.F.,Nee, A.Y.C. (1997). Using genetic algorithms in process planning for job shop machining. IEEE Transactions on Evolutionary Computation 1 (4) : 278-289. ScholarBank@NUS Repository. https://doi.org/10.1109/4235.687888
Abstract: This paper presents a novel computer-aided process planning (CAPP) model for machined parts to be made in a job shop manufacturing environment. The approach deals with process planning problems in a concurrent manner in generating the entire solution space by considering the multiple decisionmaking activities, i.e., operation selection, machine selection, setup selection, cutting tool selection, and operations sequencing, simultaneously. Genetic algorithms (GA's) were selected due to their flexible representation scheme. The developed GA is able to achieve a near-optimal process plan through specially designed crossover and mutation operators. Flexible criteria are provided for plan evaluation. This technique was implemented and its performance is illustrated in a case study. A space search method is used for comparison. © 1997 IEEE.
Source Title: IEEE Transactions on Evolutionary Computation
URI: http://scholarbank.nus.edu.sg/handle/10635/51736
ISSN: 1089778X
DOI: 10.1109/4235.687888
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