Production data based similarity coefficient for machine-component grouping decisions in the design of a cellular manufacturing system

Abstract

This paper presents a similarity coefficient based approach to the problem of machine-component grouping. The proposed method incorporates relevant production data such as part type production volume, routing sequence and unit operation time in the early stages of grouping decisions for cellular manufacturing. The algorithm also suggests a methodology for evaluating alternative solutions from different algorithms on a quantitative basis using a modified version of an existingcoefficient. The modifiedquantitative measure is a comprehensiveindicator for the goodness of a grouping solution. The algorithm then identifies bottleneck machines and corresponding cell candidates for their duplication using percentage utilization in each cell as a criterion. Finally, additional constraints can be applied to determine the best grouping solution among alternative solutions generated by the algorithm. Asoftwarepackage has beendeveloped to verify the implementation.

Formation of machine groups and part families in cellular manufacturing systems using a correlation analysis approach

“…Many other definitions of similarity coefficient have been proposed for GT. For example in Gupta & Saifoddini, 1990;Genwook et al, 1998;Kitaoka et al, 1999. Step 2:…”

The evolution of cell formation problem methodologies based on recent studies (1997–2008): Review and directions for future research

“…Essentially hierarchical techniques can be classified into two: (a) Divisive methods where the process starts with all the data (machines or parts) in a single group and a series of partitions is created until each machine (part) is in a singleton cluster and, (b) Agglomerative methods where the process starts with singleton clusters and proceeds to merge them into larger partitions until a partition containing the whole set is obtained. Important work utilising agglomerative methods can be found in Gupta and Seifoddini (1990) and Gupta (1993). Also Vakharia and Wemmerlov (1990) proposed a methodology for the CF problem, this time based on the identification of part families rather than machine cells.…”

Application Similarity Coefficient Method to Cellular Manufacturing

“…Other production volume / operation time considered studies include Selvam and Balasubramanian (1985), Seifoddini (1987Seifoddini ( /1988, Gupta and Seifoddini (1990), Balasubramanian and Panneerselvam (1993), Gupta (1993), Lee et al (1997) and Nair and Narendran (1999). Their characteristics have been discussed in sections 6.1.1 and 6.1.2.…”

“…To select the value L, one has to balance the need to uncover the natural strength of the relationships among the parts and the computational efforts necessary to calculate the sequences of length 1 through L. In general, the higher the value of L, the more discriminating power similarity coefficient will have. Gupta and Seifoddini (1990) proposed a similarity coefficient incorporating operation sequence, production volume and operation time simultaneously. From the definition, each part that is processed by at least one machine from a pair of machines contributes towards their similarity coefficient value.…”

“…Operation sequences, one of the most important manufacturing factors, was incorporated in 1985 (Selvam and Balasubramanian). In the late 1980s and early 1990s, other weighted manufacturing factors such as tooling requirements (Gunasingh and Lashkari, 1989) and operation times (Gupta and Seifoddini, 1990) were taken into consideration. Alternative process routings of parts is another important manufacturing factor in the design of a CF system.…”