Matching of technological forecasting technique to a technology using fuzzy multi-attribute decision-making methods: Case study from the aerospace industry

Given the accelerating pace of technological advances and environmental changes, technology-based companies are required to predict and understand future events in their environments. However, there is a wide range of forecasting methods creating confusion on which method to use. This paper demonstrates the selection of an appropriate technique for technology forecasting in the Iran Aviation Industries Organization (IAIO). To this end, a review of the literature was first reviewed to extract the proper criteria for selecting a forecasting method. Next, the SWARA and fuzzy MUTLIMOORA methods were used to evaluate and prioritize a total of twelve forecasting methods proposed for the case study. The results suggested that the Delphi method for technology forecasting in the IAIO. Scenario writing and the relevance tree are the next proper alternatives that can be used.     

Student selection and assignment methodology based on fuzzy MULTIMOORA and multichoice goal programming

Student selection is a multicriteria decision‐making problem that includes both tangible and intangible factors. In these problems if educational institutions have budget or other different constraints, two problems will exist: which students are the best and how students are assigned to the predefined programs? In this study, an integrated approach of fuzzy MULTIMOORA and multichoice conic goal programming is proposed to consider criteria in choosing the best students and define the optimum assignments among the predefined programs to maximize both the total preference value and total ranking value. The rankings of the students are determined by using fuzzy MULTIMOORA. The rankings of candidates are set as the parameters of the first objective function. The placement preferences of the students according to the predefined programs are considered in the second objective function. The candidates are assigned to their placement preferences both by using multichoice conic goal programming among partner universities according to the objectives and by considering the budget and quota.     

Interval 2-tuple Pythagorean fuzzy linguistic MULTIMOORA method with CIA and their application to MCGDM

The reliable evaluation of financial performance of universities plays an important role in sustainable development of universities, and it could be regarded as a multicriteria group decision making problem. Based on this, in this paper, with respect to the attribute evaluation values are expressed by interval 2-tuple Pythagorean fuzzy linguistic variables, we develop an extended MULTIMOORA method. First, we propose the interval 2-tuple Pythagorean fuzzy linguistic set based on Pythagorean fuzzy set and interval-valued 2-tuple linguistic variables, and further introduce the score and accuracy functions and Hamming distance of it. Then, considering that there are some deficiencies in the traditional MULTIMOORA method, such as exist the case of circular reasoning, and so forth. We improve it by proposing a comprehensive integration approach, which can not only consider both the utility values and sort results, but also reflect the preference attitude of decision makings simultaneously. Based on these research findings, an interval 2-tuple Pythagorean fuzzy linguistic MULTIMOORA method is presented and the calculation process of this method is described in detail. Finally, we give the numerical example concerning the evaluation of financial management performance in universities to illustrate practicability and reliability of the proposed approach and compare the proposed method with different methods to perform its flexibility.     

基于概率语言组合赋权与MULTIMOORA的多准则决策方法

针对传统的全乘比例多目标优化法(MULTIMOORA)存在的难以表征评价信息的模糊性和不确定性以及评价准则的权重需要从外部获取等问题,提出了一种改进的全乘比例多目标优化法,即基于概率语言组合赋权与MULTIMOORA的多准则决策方法.该方法采用概率语言(PLTS)处理决策者的评价信息,并引入改进的G1和CRITIC法构建组合优化赋权模型以计算评价准则的组合权重.实例对比实验结果表明,改进后的MULTIMOORA决策方法不仅赋权合理,而且具有更高的决策效率和鲁棒性,因此该方法对多准则决策问题具有良好的实际应用价值.     

A multi-criteria decision making approach for strategy formulation using Pythagorean fuzzy logic

The objective of this paper is to develop Pythagorean fuzzy (PF) multi-objective optimization by ratio analysis (PF-MOORA) plus full MULTIplicative form (PF-MULTIMOORA) method for solving multicriteria decision making (MCDM) problems with completely unknown information of criteria weights. In the model formulation process, a new distance measure is defined to quantify the difference between PF sets by combining Hamming distance and Hausdorff metric. This distance measure is, subsequently, implemented in entropy weight model for determining unknown weights of criteria, and also in reference point approach for obtaining preference indices of alternatives. To overcome the deficiencies occurred in existing MULTIMOORA method, like multiple comparisons, circular reasoning, and so on, an aggregation-based approach is recommended in the proposed PF-MULTIMOORA. To demonstrate the feasibility and practicality of the proposed method, an example concerning strategy prioritization of a tiles manufacturing company is presented. In the strategy evaluation process, at first, the judgement values provided by the decision maker are expressed in linguistic terms, and then those are converted into PF numbers through a PF weighting scale. The sensitivity of the proposed model is validated by changing of weights of criteria which impact on the ranks of the strategies. To show robustness of the developed method, the result attained by applying PF-MULTIMOORA is compared with existing techniques, not only in crisp and fuzzy quantitative strategic planning matrix context, but also using four other MCDM methods, namely, modified PF-MOORA, as a particular case of the proposed PF-MULTIMOORA technique, PF weighted sum, PF-TOPSIS and PF-VIKOR.     

Intuitionistic fuzzy MULTIMOORA approach for multi-criteria assessment of the energy storage technologies

This paper proposes MULTIMOORA-IFN2 technique for multi-criteria decision making MCDM). The proposed approach involves information fusion which allows considering information expressed in both crisp and fuzzy variables. What is more, we introduce the aggregation of the different parts of MULTIMOORA which makes the technique more operational, especially in case of large-scale applications. The empirical example considers the case of energy storage technology selection. The sensitivity of the results obtained by applying MUTIMOORA-IFN2 is checked in two ways. The weighting is adjusted to ascertain whether the changes in the importance of the criteria impact the ranks of the energy storage technologies. Further on, the results obtained by applying MULTIMOORA-IFN2 are compared to those obtained by employing TOPSIS and VIKOR methods.     

Improving quality function deployment analysis with the cloud MULTIMOORA method

Quality function deployment (QFD) is a quality guarantee method extensively used in various industries, which can help enterprises shorten the product design period and enhance the manufacturing and managing work. The task of selecting important engineering characteristics (ECs) in QFD is crucial and often involves multiple customer requirements (CRs). In this paper, a modified multi‐objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA) method based on cloud model theory (called C‐MULTIMOORA) is developed to determine the ranking order of ECs in QFD. First, the linguistic assessments provided by decision makers are transformed into normal clouds and aggregated by the cloud weighted averaging operator. Then, the weights of CRs are determined based on a maximizing deviation method with incomplete weight information. Finally, the importance of ECs is obtained using the C‐MULTIMOORA method. An empirical case conducted in an electric vehicle manufacturing organization is provided together with a comparative analysis to validate the advantages of our proposed QFD model.     

An improved MULTIMOORA method with combined weights and its application in assessing the innovative ability of universities

Innovative ability plays a critical role in the sustainable development of universities. Although the assessment of universities' innovative ability is a significant undertaking, it is difficult work. This challenge can be addressed as a typical multiple attribute decision making (MADM) problem, in which multiple attributes should be considered with different levels of importance. This paper aims to propose an integrated MADM method to solve this issue. To do so, we first introduce the least square method with the hesitant fuzzy linguistic term set to determine the subjective attribute weights. Considering that the selected attributes are not always in conflict with each other due to the complexity of objective things, we further present a correlation coefficient‐based method to calculate another kind of attribute weight. The final weights are the combined form of these two types of attribute weights. In addition, we enhance the robust ranking method, MULTIMOORA, with the Borda rule to calculate the utility values of universities and derive their rankings. Finally, after establishing an index system, the assessment of the innovative ability of 26 world‐class construction universities in China is conducted by using the proposed method. The advantages and disadvantages of the assessed universities are analysed.