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Most of data in a multi attribute decision making (MADM) problem are unstable and changeable, and thus sensitivity analysis can effectively contribute to making proper decisions. Here, we offer a new method for sensitivity analysis of multi-attribute decision making problems so that by changing one element of decision making matrix, we can determine changes in the results of a decision making problem. An analysis is made for simple additive weighting method (SAW) technique, a mostly used multi-attribute decision making techniques, and the corresponding formulas are obtained.

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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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The false location of airports is one of the most important issues and challenges that we face on some airports, finding scientific solutions to optimize airports, to achieve travelers, including these challenges. The main purpose of this research is to provide a metaheuristic technique for locating the construction of airport and compared with the results of the seca model and the Copras Method. The metaheuristic technique is based on new multi-criteria decision making techniques, aimed at prioritizing research alternatives and its difference with the rest of the methods is to use statistical methods and now it is possible to understand and simply process its process. The statistical population of this research is (experts and management in Iran airport and air Navigation Company). After research, alternatives were selected based on the opinions of experts who included five provinces of the country, as well as 10 standard indicators, including the average income per year, the population of the province and ... who were extracted from the questionnaire as input. Finally, the provinces were prioritized according to different ways, all results based on choosing Isfahan province as the right province and Najaf Abad city as the final alternative

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In third world countries, organizational leaders rarely have figured out to consider happiness and joy of work as a part of the system they are managing. Usually, happiness in organizations is not considered as a management style. Gradually, it became obvious that joy and fun at the workplace will decrease the health care costs, enhances the customers’ loyalty, and increases productivity and profits as a result. Most research on this subject matter relied upon very specific case studies. No research exits dealing with the risks and benefits of Joyful organization. The objectives of this paper are twofold: (1) to utilize hierarchical fuzzy technique for order preference by similarity to ideal solution (TOPSIS) to determine the most suitable type of Joyful Organization (JO), and (2) to list key risks and benefits of Joyful Organization. This researcher explains the importance of selection criteria for evaluation of Joyful organizations. It provides key elements on JO, Quantitative strategic planning matrix (QSPM), and fuzzy hierarchical TOPSIS methodology. Since QSPM is used with SWOT by many practitioners and researchers in various fields of study, it was selected as a tool for validation purposes. A case study is taken under consideration and results are explained for both approaches.  The finding of this research points to the suitability of semi conventional organization strategy which means implementing about 50% of the rules of main cultural organizations. A sensitivity analysis was performed on TOPSIS using the weights generated by the hierarchical fuzzy TOPSIS approach, Shannon entropy weight, and TOPSIS approach. The ranking results obtained are identical for all these three cases.
 

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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.
 

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The main objective of this study is to identify and rank foreign investment risk factors and determine their impact on attracting foreign investment in the upstream oil industries. In terms of nature and method, it is descriptive and, in terms of relationships, it is inferential and correlational. The statistical population of the research includes managers and experts in the upstream oil industries, and the sample size was estimated to be 103 people using random sampling. The collected data was analyzed using SPSS, Expert Choice, and Smart PLS software. The results showed that according to the experts in the statistical population, economic risk is the most important factor in foreign investment. Also, in the structural equation modeling method, the correlation between foreign investment risk and attraction factors was significant, with political risk having the greatest impact on foreign investment risk, followed by economic and financial risks and 87.4% of the changes in foreign investment attraction factors could be predicted by foreign investment risk, and the overall fit of the proposed model showed a GOF value of 0.447, indicating a high fit of the research model.