Ant colony optimization for group technology applications

The problem of grouping of parts into part-families and that of machines into machine-cells has attracted the attention of many researchers particularly for medium variety of parts with medium production volume requirement, which traditionally required their production in batches for achieving economics of production. This kind of grouping, consequently offering benefits of mass production, was aimed to have independent cells processing ideally almost different sets of part-types. For this purpose, a number of approaches are available from various kinds of heuristics to mathematical programming formulations. Evolutionary methods such as neural network, genetic algorithm, and simulated annealing have also been tried and have been found to provide better grouping solutions with much less computational complexity. In the present paper, ant colony optimization approach with number of newer strategies, incorporating more generalised framework of ants' behaviour, has been applied to the parts and machines grouping problems taken from the literature. The results obtained from their application were found to be encouraging and thus establish the usefulness of the proposed approaches. Average performance of Tabu search with multiple ants was found to be the best and thus the parameter values for this approach were also determined using design of experiments methodology.