Abstract
An Ant Colony Optimization Approach for the Machine-Part Cell Formation Problem
Highlights
Cellular manufacturing (CM) as an application of group technology is concerned with the formation and operation of manufacturing cells in which a set of part families are processed using machine cells
One of the most important problems encountered in designing CM system is cell formation (CF), which deals with identifying machine cells and part families.[1]
The trade-off between intensification and diversification is the key point to achieve good results in different runs of a search algorithm.[43]. This trade-off is obtained by using a well-defined heuristic information and an effective local search algorithm to reinforce the intensification of the algorithm as well as defining upper and lower limits for the pheromone values and using a strategy in the local pheromone update to increase the diversification of the algorithm
Summary
Cellular manufacturing (CM) as an application of group technology is concerned with the formation and operation of manufacturing cells in which a set of part families are processed using machine cells. Many solution methods have been developed to solve the machine-part cell formation (MPCF) problem in which a given machine-part incidence matrix is modified to obtain machine cells and part families with the objective of minimizing inter-cellular movements and maximizing machines’ utilization. Oliveira et al have presented[17] a bipartite graph modeling with a graph clustering algorithm for determining machine cells and part families in the CM systems. Combined[30] grouping GA with the local search heuristic proposed by Goncalves and Resende[24] to determine part families and machine cells. Hung et al have proposed[37] a novel procedure based on a fuzzy relational data clustering algorithm for solving the manufacturing cells design problem. An ant colony optimization (ACO) algorithm is presented to solve the MPCF problem.
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