ISSN:
1572-8145
Keywords:
Machine cell formation
;
comprehensive grouping
;
Hopfield neural networks
;
generalized grouping efficiency
;
algorithm
;
ortho-synapse network structure
Source:
Springer Online Journal Archives 1860-2000
Topics:
Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
Notes:
Abstract The machine/part grouping problems can be classified into binary and comprehensive grouping problems depending on whether or not the processing times and the machine capacities are considered. The binary grouping problem arises if the part demands are unknown when the CMS is being developed. If the part demand can be forecast accurately, both the processing times and machine capacities have to be included in the analysis. This gives rise to comprehensive grouping. Both the binary and comprehensive grouping have been proven to be NP-complete which cannot be solved in polynomial time. Considering the large number of parts and machines involved in the industrial design problem, efficient solution methods are highly desirable. In this paper, a new neural network approach (OSHNg) is proposed to solve the comprehensive grouping problems. The proposed approach has been tested on 28 test problems. The results show that the OSHNg method is very efficient and its solution quality is comparable to that of a simulated annealing approach.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1023/A:1008923114466
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