یک روش DEA / AR فازی برای انتخاب سیستم های تولید انعطاف پذیر

Flexible Manufacturing System (FMS) offers opportunities for manufacturers to improve their technology, competitiveness, and profitability through a highly efficient and focused approach to manufacturing effectiveness. Data envelopment analysis (DEA) has been utilized as a multiple criteria tool for evaluation of FMSs. The concept of the assurance region (AR) is restricting the ratio of any two weights to some range to avoid the evaluated alternatives from ignoring or relying too much on any criterion in evaluation. In this paper, we develop a fuzzy DEA/AR method that is able to evaluate the performance of FMS alternatives when the input and output data are represented as crisp and fuzzy data. Based on Zadeh’s extension principle, a pair of two-level mathematical programs is formulated to calculate the lower and upper bounds of the fuzzy efficiency score of the alternatives. We transform this pair of two-level mathematical programs into a pair of conventional one-level DEA/AR method to evaluate the FMS performance. An example illustrates the application of the proposed methodology.

Changing economic conditions have challenged many companies to improve cost, quality, and responsiveness to meet fierce competition. A flexible manufacturing system (FMS) is designed to combine the efficiency of a mass-production line and the flexibility of a job shop to produce a variety of workpieces on a group of machines (Chan, Kazerooni, & Abhary, 1997). FMS brings opportunities for manufacturers to improve their technology, competitiveness, and profitability through a highly efficient and focused approach to manufacturing effectiveness. The primary reason for implementing FMS lies in its versatility. Generally, increased flexibility enables a company to adjust more easily to changes in market place and in customer requirements, while maintaining high quality standards for its products and keeping good performance of manufacturing system (Shang and Sueyoshi, 1995 and Priore et al., 2006). The research work in the design, evaluation, justification and implementation of FMS has long been a concern of researchers. In the past years, a number of studies have included cases studies, empirical research, analytical and simulation modeling, to help understand and address the issues of the FMS justification by organizations. The contexts of the research have covered the spectrum of managerial issues from focus on cost management system to the application of advanced mathematical models to understand the FMS and its characteristics. When flexibility, risk and non-monetary benefits are expected, as with FMSs, analytical procedures are required. Value analysis, scoring models, mathematical programming, and risk analysis can be considered among the analytical procedures.

Manufacturing firms need to increase the quality and responsiveness to customization, while lower costs to compete in today’s global market place. The evolution of FMSs offers a great potential for increasing flexibility and changing the basis of competition by ensuing both cost effective and customized manufacturing at the same time. DEA has been utilized as a multiple criteria tool for evaluation of FMSs. In practice, there are cases where each factor must be maintained at a minimum level for the production mechanism to work. The concept of AR is thus employed to restrict the ratio of any two weights to some range derived from price/cost information or experts’ opinions. An evaluation process usually comprises complicated inputs and outputs, where many factors cannot be precisely measured. This paper develops a method to find the fuzzy efficiency measures of FMS alternatives embedded with AR concept when some observations are fuzzy numbers. The idea is based on Zadeh’s extension principle to transform a fuzzy DEA/AR model into a family of crisp DEA/AR models to calculate the lower and upper bounds of efficiency scores under a specific α level. From different possibility level α, the membership function is approximately accordingly. Since the efficiency measures of alternatives are expressed by fuzzy numbers rather than by crisp values, a method for ranking fuzzy numbers ( Chen & Klein, 1997) is adopted to help identify which FMS alternatives has better performance.