Fuzzy MCDM procedure for evaluating flexible manufacturing system alternatives

Considering the high required capital outlay and moderate risk of a flexible manufacturing system (FMS) investment, economic justification techniques are insufficient by themselves since they cannot cope with the benefits such as flexibility and enhanced quality offered by advanced manufacturing technologies. A robust decision making procedure for selection of flexible manufacturing systems requires the consideration of both economic and strategic investment measures. In this paper, a fuzzy multi-criteria decision making (MCDM) framework based on the concepts of ideal and negative-ideal solutions is presented for the selection of an FMS from a set of mutually exclusive alternatives. The proposed method provides the means for incorporating the economic figure of merit as well as the strategic performance variables. Initially, the selection criteria and their importance weights are determined. Linguistic variables are used to indicate the importance weight of each criterion. Then, the decision matrix containing the criteria values for the FMS alternatives is normalized to obtain unit-free elements. Afterwards, the weighted normalized decision matrix is obtained by taking the importance weight of each criterion into consideration. The ideal solution and the negative-ideal solution are determined by ranking the weighted normalized values for each criterion. Next, the distance between each FMS alternative, and the ideal and negative-ideal solutions are computed. Finally, the ranking order of the FMS alternatives is obtained based on their relative proximity to the ideal solution.

[1]  S. G. Deshmukh,et al.  A decision support system for selection and justification of advanced manufacturing technologies , 1997 .

[2]  Andrea Rangone,et al.  A reference framework for the application of MADM fuzzy techniques to selecting AMTS , 1998 .

[3]  K. Kim,et al.  Ranking fuzzy numbers with index of optimism , 1990 .

[4]  Chung-Hsing Yeh,et al.  Multi-criteria analysis for dredger dispatching under uncertainty , 1999, J. Oper. Res. Soc..

[5]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[6]  Nagen Nagarur Some performance measures of flexible manufacturing systems , 1992 .

[7]  Ching-Lai Hwang,et al.  Multiple attribute decision making : an introduction , 1995 .

[8]  E. Ertugrul Karsak A TWO-PHASE ROBOT SELECTION PROCEDURE , 1998 .

[9]  Markku Kuula,et al.  Selecting a flexible manufacturing system using multiple criteria analysis , 1991 .

[10]  Craig A. Nelson,et al.  A scoring model for flexible manufacturing systems project selection , 1986 .

[11]  Toshiyuki Sueyoshi,et al.  A unified framework for the selection of a Flexible Manufacturing System , 1995 .

[12]  Mao-Jiun J. Wang,et al.  Ranking fuzzy numbers with integral value , 1992 .

[13]  Gyutai Kim,et al.  Identifying investment opportunities for advanced manufacturing systems with comparative-integrated performance measurement , 1997 .

[14]  V. P. Agrawal,et al.  Computer-aided evaluation and selection of optimum grippers , 1992 .

[15]  G. Bortolan,et al.  A review of some methods for ranking fuzzy subsets , 1985 .

[16]  Jack R. Mkrkdtth,et al.  Justification techniques for advanced manufacturing technologies , 1986 .

[17]  Yash P. Gupta,et al.  Flexibility of manufacturing systems: Concepts and measurements , 1989 .

[18]  Giovanni Perrone,et al.  Strategic FMS design under uncertainty: A fuzzy set theory based model , 1996 .

[19]  Roger N. Wabalickis Justification of FMS with the analytic hierarchy process , 1988 .

[20]  Mao-Jiun J. Wang,et al.  A fuzzy multi-criteria decision-making approach for robot selection , 1993 .

[21]  G. John Miltenburg,et al.  Evaluating Flexible Manufacturing Systems , 1987 .

[22]  V. P. Agrawal,et al.  Computer aided robot selection: the ‘multiple attribute decision making’ approach , 1991 .

[23]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .