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Quality function deployment is a well-known customer-oriented design procedure for translating the voice of customers into a final production. This is a way that higher customer satisfaction is achieved while the other goals of company may also be met. This method, at the first stage, attempts to determine the best fulfillment levels of design requirements which are emanated by customer needs. In real-world applications, product design processes are performed in an uncertain and imprecise environment, more than one objective should be considered to identify the target levels of design requirements, and the values of design requirements are often discrete. Regarding these issues, a fuzzy mixed-integer linear goal programming model with a flexible goal hierarchy is proposed to achieve the optimized compromise solution from a given number of design requirement alternatives .To determine relative importance of customer needs, as an important input data, we apply the well-known fuzzy AHP method. Inspired by a numerical problem, the efficiency of our proposed approach is demonstrated by several experiments. Notably, the approach can easily and efficiently be matched with QFD problems.

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Abstract This author introduces the concept of Stepwise Strategy Approach (SSA) for dealing with a number of problems arises in the current age of technology. This new idea is combined with the knowledge of Grey Theory for adding flexibility to decision making process. Grey theory is useful for grasping the ambiguity exists in the utilized information and the fuzziness appears in the human judgments and preferences. This article is a very useful source of information for Fuzzy Grey and decision making using more than one decision makers in fuzzy environment. A case study on system selection comprised of 12 attributes and 4 alternatives is constructed and solved by the proposed method and the results are analyzed. For the validation of the results obtained by the Grey theory, the fuzzy VIKOR and Fuzzy TOPSIS were employed for computational purposes. The results of these three approaches on the proposed case study are closely related. Due to the fact that this author proposes the “Stepwise Strategy” approach for implementing a new technology in industries, where already the management of an older compatible type of technology is in existence, along with the grey theory concept and data whitenization approach, its contribution to the literature of operations research is highly recognizable.

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Cost efficiency models evaluate the ability of decision-making units (DMUs) to produce current
outputs at minimal cost. In real applications, the observed values of the input-output data and
their corresponding input prices are imprecise and vague. This paper employs a fuzzy data
envelopment analysis (Fuzzy DEA) method to study cost efficiency of DMUs. In previous studies
on the cost efficiency, no attention has been paid to the issue of ranking problem in fuzzy
environment. In addition, adequate accuracy is ignored in regards to appropriate range of fuzzy
cost efficiency scores. In this study, the proposed method is applied to assess fuzzy cost efficiency
in accordance with the  -level based approach. In this method, data information is considered
as triangular fuzzy numbers. The main idea is to convert the fuzzy DEA model into a family of
parametric crisp models to estimate the lower and upper bounds of the a-cut of the membership
functions of the cost efficiency measures. Moreover, the problem of ranking DMUs is investigated
based on the fuzzy cost efficiency, using a new method. Finally, the proposed method is illustrated
applying a numerical example, and then comparisons between the proposed method and previous
approaches are carried out.
 

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In cost efficiency models, the capability of producing observed outputs of a target decision making unit (DMU) is evaluated by its minimum cost. Traditional cost efficiency models are considered for situations where data set is known for each DMU, while, some of them are imprecise in practice. Several studies have carried out to evaluate cost efficiency using fuzzy data envelopment analysis (DEA) methods for dealing with the imprecise data that have drawbacks. The issue of presenting improve strategy is ignored for inefficient units, as well as the applied models are not easily implemented. This paper proposes a new extension to evaluate fuzzy cost efficiency using fuzzy extended multiplication and division operations. This method offers a fully fuzzy model with triangular fuzzy input-output data along with triangular fuzzy input prices. In the proposed extension, a new definition of fuzzy cost efficiency is suggested based on the extended operations. Finally, a numerical example is provided to show the applicability of the proposed models.