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Ant colony recognition systems for part clustering problems. (English) Zbl 1153.90398
Summary: Cellular manufacturing requires an effective part clustering method to start up the manufacturing cell design. This paper presents a new part clustering algorithm that uses the concept of the recognition system of artificial ants. The proposed algorithm mimics the random meetings of real ants to build up the ability of object recognition and then to form many initial part clusters with high similarities. These initial part clusters are further merged into larger and larger clusters in an agglomerative way until the designated number of part families is reached. The characteristics of artificial ants, such as randomization and collective behaviour, allow the algorithm to re-cluster wrongly grouped parts into the proper clusters. As a result this can eliminate the chaining effects resulting from the interference of abnormal parts during the clustering process. This algorithm has been developed into a software system called the ant colony recognition system (ACRS). A number of problems selected from the literature have been solved by ACRS, and the evaluation results indicate that ACRS is able to solve the cell formation problems effectively.

MSC:
90B30 Production models
90C59 Approximation methods and heuristics in mathematical programming
Software:
AntClust
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