یک روش ترکیبی با استفاده از طراحی آزمایش و تجزیه و تحلیل رابطه ای خاکستری برای تصمیم گیری چند معیاره و شکل گیری مشکلات

To improve the market competitiveness of enterprises in the manufacturing industry, some of them involve picking the most appropriate strategies among multiple available alternatives. However, no single alternative works is suitable for all evaluation criteria which must be considered during the selection processes. This paper proposes a new hybrid MCDM method using design of experiment (DoE) and grey relational analysis (GRA), called DoE-GRA method, to solve this kind of problem. Three illustrative MCDM examples consisting of selection of (a) a rapid prototyping process, (b) a flexible manufacturing system, and (c) an automated inspection system, are analyzed to demonstrate the application capability of the DoE-GRA method. The results of comparisons show that the DoE-GRA method is featured by less sensitive to weight changing, simple and fast calculation, robust and practical. It is suitable for solving MCDM problems.

There are many multiple criteria decision making (MCDM) problems in manufacturing industry. Different from single criterion decision making problems, in multiple criteria decision making problems, a decision maker (DM) has to choose the most appropriate alternative that satisfies the evaluation criteria among a set of candidate solutions. For that the evaluation criteria are frequently in conflict with each other, how to make a scientific decision becomes a difficult problem [1].

It is very important for DMs to make a choice among varies alternatives in the manufacturing industry. It is also a MCDM problem. Some methods, such as AHP, TOPSIS, GRA, PROMETHEE and MOOR, are usually used for solving this kind of problems. This paper proposes a new MCDM method, i.e., the DoE-GRA method. Design of experiment and grey relational analysis are used together to identify critical criteria and their interactions of a MCDM problem by fitting a polynomial to the experimental data in a multiple linear regression analysis.