An analytical-iterative clustering algorithm for cell formation in cellular manufacturing systems with ordinal-level and ratio-level data

Abstract

In this paper, the problem of clustering machines into cells and components into part-families with the consideration of ratio-level and ordinal-level data is dealt with. The ratio-level data is characterized by the use of workload information obtained both from per-unitprocess times and production quantity of components, and from machine capacity. In the case of ordinal-level data, we consider the sequence of operations for every component. These data sets are used in place of conventional binary data for arriving at clusters of cells and part-families. We propose a new approach to cell formation by viewing machines, and subsequently components, as 'points' in multi-dimensional space, with their coordinates defined by the corresponding elements in a Machine-Component Incidence Matrix (MCIM). An iterative algorithm that improves upon the seed solution is developed. The seed solution is obtained by formulating the given clustering problem as a Traveling Salesman Problem (TSP). The solutions yielded by the proposed clustering algorithm are found to be good and comparable to those reported in the literature.

Concurrently part-machine groups formation with important production data

“…Therefore, Nash [28] principal of bargain should be used. This approach is used by Nair and Narendran [16] and George et al [14]. From this discussion, we conclude the integration of ordinal level data and ratio level data by a matrix, Sequence Part Load Matrix, SPLM.…”

“…When production volume is large, total number of intercell movements will be further large. Part-machine grouping without considering operation sequences also tends to distort the real extent of material handling efforts within and outside the cells [6,[14][15][16][17]. Nair and Narendran proposed CASE (1998) -SCM based model [15] using sequence data only and Accord (1999) -SCM based model [16] using ordinal and ratio level data.…”

“…They emphasized use of operation sequence, operation time, production volume and machine capacity for grouping. George et al [14] proposed an interactive cluster approach, a SCM based model considering ordinal and ratio level data. However, they [14,16] considered unity production volume in their models.…”

“…George et al [14] proposed an interactive cluster approach, a SCM based model considering ordinal and ratio level data. However, they [14,16] considered unity production volume in their models. In the design of cellular manufacturing systems, many production factors should be involved when the cells are created, e.g.…”

Application of principal component analysis in machine-part cell formation

“…In addition, the various clustering and machine-part grouping methods like array-based clustering algorithms, such as, rank order clustering (ROC), direct clustering analysis (DCA), and bond energy analysis (BEA) for manufacturing cell formation have been reported by researchers (Chu & Tsai, 1990;Murugan, 2007;Murugan & Selladurai, 2007). George et al, 2003 developed an analytical-iterative clustering algorithm for cell formation in cellular manufacturing systems with ordinal-level and ratio-level data. Prabhaharan et al (2005) and Kao and Fu (2006) proposed antcolony based clustering algorithm for manufacturing cell design.…”