A fuzzy multiple objective programming approach for the selection of a flexible manufacturing system

Karsak E., Kuzgunkaya O.

INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, vol.79, no.2, pp.101-111, 2002 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 79 Issue: 2
  • Publication Date: 2002
  • Doi Number: 10.1016/s0925-5273(00)00157-2
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.101-111
  • Keywords: multiple-objective decision making, fuzzy sets, investment decision analysis, flexible manufacturing systems, ROBOT SELECTION, JUSTIFICATION, FRAMEWORK, MODELS
  • Galatasaray University Affiliated: Yes


Global competition in manufacturing environment has forced the firms to consider increasing the quality and responsiveness to customization. while decreasing costs. The evolution of flexible manufacturing systems offers great potential for increasing flexibility and changing the basis of competition by ensuring both cost effective and customized manufacturing at the same time. This paper presents a fuzzy multiple objective programming approach to facilitate decision making in the selection of a flexible manufacturing system (FMS). Fuzzy set theory is introduced in the model to incorporate the vague nature of future investments and the uncertainty of the production environment. Linguistic variables and triangular fuzzy numbers are used to quantify the vagueness inherent in decision parameters, e.g., increase in market response, improvement in quality, reduction in setup cost, and so forth. The model proposed in this paper determines the most appropriate FMS alternative through maximization of objectives such as reduction in labor cost, reduction in setup cost, reduction in work-in-process (WIP), increase in market response and improvement in quality, and minimization of capital and maintenance cost and floor space used. These objectives are assigned priorities indicating their importance levels using linguistic variables. A numerical example is presented to illustrate the application of the model developed in this paper. (C) 2002 Elsevier Science B.V. All rights reserved.