A hybrid genetic algorithm for manufacturing cell design

Abstract

Global competition is demanding innovative ways of achieving manufacturing flexibility and reduced costs. One approach is through cellular manufacturing, an implementation of the concepts of group technology. The design of a cellular manufacturing system requires that a part population be at least minimally described by its use of process technology (padmachine incidence matrix) and partitioned into part families and that the associated plant equipment be partitioned into machine cells. At the highest level, the objective is to form a set of completely autonomous units such that inter-cell movement of parts is minimized. This paper presents a stochastic global optimization technique utilizing genetic algorithms (GAS) and local improvement procedures (LIPs) to solve the cell design problem. The combination of LIPs with GAS is shown to improve the performance of the GA in terms of solution quality and computational efficiency. Several different methods of incorporating these procedures into the GA are investigated. The concepts used in these hybrid techniques can easily be extended to other variations of the cell design problem as well as to other LIPs.