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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.

 

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Supplier selection is one of the main discussions in the Supply Chain. The issue of assigning purchase orders to suppliers that act differently in terms of quality, cast, services, etc. criteria is one of the significant concerns of purchase managers in the supply chain. To adopt an optimal decision in this regard is related to a multi-objective problem that the objectives are contradicting each other and have different importance and priority depending on the location. In practice, the existence of kind of ambiguity in explaining the information related to the problem constraints and complicated. In this regard, the emergence of Fuzzy set theory as a tool to describe such conditions besides presenting question model realistically can help to solve such problems well. Despite the importance of the model with the mentioned structure, unfortunately, few original works have been done in this field. As a result, in this paper, in addition to presenting a new multi-objective Fuzzy model being modelled based on assigning purchase order to suppliers in a supply chain a solution method is introduced based on using Fuzzy linear programming. To clarify solution process modelling and description, a case study is included related to selecting flour supplier for providing industrial bread of Khoshkar factory. The proposed model includes four objective functions:

1. Aggregate costs of minimizing type,
2. Services of maximizing type (such as packing, being faithful to promise, factory heath, discount, correct transportation, good relationships, honestly, etc.),
3. Flour useful survival of maximizing type (regarding monthly flour buying by the factory),
4. The purchased flour quality of maximizing type (concerning product type).

 Especially in the solution process, a method is determined based on setting weight for each of the objectives concerning the major factory stockholders.
 

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The main objective of the research is to rank the risk factors of foreign investment and present a model of their impact on the risk of foreign investment in upstream oil industries. It is descriptive in nature and method, and in terms of relationships, inferential and correlational. The statistical population of the research includes managers and experts in the oil industry, and the sample size was estimated at 90 people by random sampling method. The data collected with questionnaires were analyzed using SPSS and Matlab software. The results showed that according to the experts of the statistical population, political risk is in the first rank of mportance in creating foreign investment risk. Also, in the fuzzy regression method, the correlation between foreign investment risk factors and foreign investment risk is completely significant, and political risk has the greatest impact on foreign investment risk, and economic, social and non-commercial risks are in the next ranks. By examining the overall fit of the proposed model, it was determined that the appropriate power of fit of the proposed model has been able to determine the relationship between the independent and dependent variables of the research well.
 

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Effective management, as one of the central pillars of organizational success, hinges on the ability to make informed decisions by appropriately combining and coordinating various elements to achieve desired objectives. In decision-making processes, especially in complex scenarios, decision-makers often rely on a multitude of factors to arrive at the most suitable conclusion. This is particularly true in multi-criteria decision-making (MCDM), where decisions are based on evaluating several criteria rather than a single measure of optimality. The growing body of research over recent decades has delved deeply into MCDM methodologies, yet one fundamental aspect remains: the varying significance of the criteria involved. It is critical to accurately determine the weight or importance of each criterion to ensure optimal decision outcomes.
In this paper, we introduce a novel weighting technique designed to address the challenges of assigning weights in MCDM problems, called the Dispersion-based Weighting Method (DWM). This method builds upon the principles of statistical dispersion and offers an efficient alternative to traditional entropy-based weighting methods. The process involves constructing a criterion matrix, followed by the calculation of the mean, standard deviation, and coefficient of variation for each criterion. The weights are then computed based on these statistical measures, providing a robust and straightforward approach for determining the relative importance of each criterion.
To validate the proposed DWM technique, several numerical examples are presented, demonstrating its practical application and effectiveness. Additionally, we compare the results obtained using DWM with those derived from the well-established Shannon entropy method, which is widely used in MCDM applications. The comparative analysis reveals a strong correlation between the two techniques, while highlighting the advantages of the DWM approach, including:

• Reduced computational complexity
• No requirement for data normalization
• Applicability to both positive and negative data sets

The findings suggest that DWM offers a more accessible, efficient, and versatile alternative to traditional methods, particularly in situations where computational efficiency and handling of diverse data types are crucial.