scholarly journals A novel extension of TOPSIS with interval type-2 trapezoidal neutrosophic numbers using $(\alpha,\beta,\gamma)$-Cuts

Author(s):  
Muhammad Touqeer ◽  
Rimsha Umer ◽  
Ali Ahmadian ◽  
Soheil salahshour

Multi-criteria decision-making (MCDM) is concerned with structuring and solving decision problems involving multiple criteria for decision-makers in vague and inadequate environment. The “Technique for Order Preference by Similarity to Ideal Solution” (TOPSIS) is one of the mainly used tactic to deal with MCDM setbacks. In this article, we put forward an extension of TOPSIS with interval type-2 trapezoidal neutrosophic numbers (IT2TrNNs) using the concept of (α, β, γ)-cut. First, we present a novel approach to compute the distance between two IT2TrNNs using ordered weighted averaging (OWA) operator and (α, β, γ)-cut. Subsequently, we broaden the TOPSIS method in the context of IT2TrNNs and implemented it on a MCDM problem. Lastly, a constructive demonstration and several contrasts with the other prevailing techniques are employed to articulate the practicability of the proposed technique. The presented strategy yields a flexible solution for MCDM problems by considering the attitudes and perspectives of the decision-makers.

Author(s):  
Rimsha Umer ◽  
Muhammad Touqeer ◽  
Abdullah Hisam Omar ◽  
Ali Ahmadian ◽  
Soheil Salahshour ◽  
...  

AbstractThe Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is considered among the most frequently used techniques to deal with multi-criteria group decision-making (MCGDM) conflicts. In this article, we have presented an extended TOPSIS technique in the framework of interval type-2 trapezoidal Pythagorean fuzzy numbers (IT2TrPFN). We first projected a novel approach to evaluate the distance between them using ordered weighted averaging operator and $$(\alpha ,\beta )$$ ( α , β ) -cut. Subsequently, we widen the concept of TOPSIS method formed on the distance method with IT2TrPFNs and applied it on MCGDM dilemma by considering the attitudes and perspectives of the decision-makers. Lastly, an application of solar tracking system and numerous contrasts with the other existing techniques are presented to express the practicality and feasibility of our projected approach.


Author(s):  
Ronald R. Yager

We discuss the use of the ordered weighted averaging (OWA) operator in multi-criteria decision problems as a means of aggregating the individual criteria satisfactions. We emphasize the need for ordering the arguments, the criteria satisfactions, when using the OWA operator. We consider the situation where the criteria satisfactions have some uncertainty, are finite probability distributions and note the requirement of having to order probability distributions. We introduce the idea of using pairwise stochastic dominance to provide the necessary ordering relationship over the probability distributions. We note that while this approach is appropriate, it is often not possible, since the presence of a stochastic dominance relationship between all pairs of probability distributions is not always the case, the relationship is not complete. To circumvent this problem we introduce an approach called the probabilistic exceedance method (PEM), which allows us to provide a surrogate for the OWA aggregation of probability distributions that doesn't require a linear ordering over the probability distributions. We look at this in both the cases in which the criteria have equal and unequal importances.


2020 ◽  
Vol 7 (2) ◽  
pp. 219
Author(s):  
Sri Kusumadewi ◽  
Hepi Wahyuningsih

<p>Depresi, kecemasan dan stress merupakan tiga gangguan yang sering dijumpai di masyarakat. Ketiga gangguan tersebut memiliki gejala yang hampir mirip. <em>Depression, Anxiety and Stress Scales</em> (DASS) merupakan salah satu alat ukur yang dapat digunakan untuk mengukur tingkat keparahan ketiga gangguan tersebut. DASS dengan jumlah item/gejala sebanyak 42 item dikenal dengan nama DASS-42. Alat ukut ini membedakan dengan jelas item/gejala dari setiap gangguan. Setiap gangguan memiliki item yang mempengaruhi sebanyak 14 item. Pada penelitian ini dibangun model Sistem Pendukung Keputusan Kelompok (SPKK) yang memungkinkan para psikolog untuk berkolaborasi memberikan preferensi terkait prioritas gangguan yang akan terjadi apabila diketahui item/gejala tertentu menurut DASS-42. Preferensi diberikan dengan format <em>ordered vectors</em>. Untuk memudahkan proses agregasi/komposisi, selanjutnya dilakukan transformasi preferensi ke relasi preferensi fuzzy. Operator <em>Ordered Weighted Averaging</em> (OWA) digunakan untuk melakukan agregasi peferensi menjadi satu matriks. Proses seleksi alternatif terbaik dilakukan dengan menggunakan <em>Quantifier Guided Dominance Degree</em> (QGDD). Hasil pengujian menunjukkan bahwa ketepatan hasil SPKK terhadap DASS-42 adalah sebesar 71,43% (30 dari 42 item/gejala). Item/gejala yang beririsan secara signifikan antara gangguan kecemasan dan stress sebesar 16,67%. (7 dari 42), antara depresi dan kecemasan sebesar 9,52% (4 dari 42). Secara umum SPKK ini mampu mengakomodasi preferensi para pengambil keputusan dalam memberikan bobot pengaruh. Gangguan kecemasan dan gangguan stress memiliki gejala yang sangat mirip sehingga untuk beberapa item.gejala pada DASS-42 ada perbedaan yang cukup signifikan.</p><p> </p><p><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Depression, anxiety and stress are three disorders that are often found in the community. These three disorders have almost identical symptoms. Depression, Anxiety and Stress Scales (DASS) is an psychological instrument that can be used to measure the severity of these disorders. DASS with a total of 42 items known as DASS-42. This instrument distinguishes clearly the symptoms of each disorder. Each disorder has 14 items affect. The three disorders have a number of symptoms that are similar, even a symptom may affect two or three disorders with different levels of influence. In this study, a Group Decision Support System (GDSS) model was developed so that psychologists can collaborate to give preference regarding priority of disorders that would occur if certain items / symptoms were identified by  DASS-42. Preferences are given in ordered vectors format. The preferences given by each decision maker aggregated to get a single preference. These preferences will be transformed to the fuzzy preference relation format. Ordered Weighted Averaging (OWA) operator used to aggregation process for all decision maker preference. The OWA operator are used to aggregate into one matrix. The best alternative selected by using Quantifier Guided Dominance Degree (QGDD). The test results show that the accuracy of the GDSS results on DASS-42 is 71.43% (30 of 42 items / symptoms). Symptoms that overlap significantly between anxiety and stress disorders are 16.67%. (7 of 42), between depression and anxiety by 9.52% (4 of 42). The GDSS is able to accommodate the preferences of decision makers in giving influence weight. Anxiety and stress disorder have very similar symptoms so that for some symptoms in the DASS-42 there are significant differences.</em></p><p><em><strong><br /></strong></em></p>


Author(s):  
Meimei Xia

The target-oriented multi-criteria decision making is investigated based on the ordered weighted averaging (OWA) operator. The criteria evaluations are measured by using the likelihood of satisfying the targets of criteria. To aggregate the target-oriented criteria evaluations, the target-oriented OWA operator is firstly introduced, in which the target-oriented criteria evaluations are reordered and then aggregated by using the weight vector associated with the position of criteria evaluations. Four types of targets about criteria evaluations and four types of attitudinal characters about criteria weight vector are introduced, based on which, models are given to identify the potential best alternative(s), and estimate the ranges of attitudinal characters about criteria weight vector for each potential best alternative. The proposed models can not only analyze the sensitivity of each potential best alternative, but also can explore the impact of targets about criteria evaluations and attitudinal characters about criteria weight vector on the decision results. Models are further established to find the best and worst ranking orders of each alternative based on targets about criteria evaluations, and give decision analysis by considering specific ranking orders of alternatives. The proposed method considers the targets about criteria evaluations and attitudinal character about criteria weight vector at the same time and can provide decision makers more choices. Several examples are given to illustrate the proposed methods.


2021 ◽  
Vol 11 (16) ◽  
pp. 7195
Author(s):  
Iris Dominguez-Catena ◽  
Daniel Paternain ◽  
Mikel Galar

Ordered Weighted Averaging (OWA) operators have been integrated in Convolutional Neural Networks (CNNs) for image classification through the OWA layer. This layer lets the CNN integrate global information about the image in the early stages, where most CNN architectures only allow for the exploitation of local information. As a side effect of this integration, the OWA layer becomes a practical method for the determination of OWA operator weights, which is usually a difficult task that complicates the integration of these operators in other fields. In this paper, we explore the weights learned for the OWA operators inside the OWA layer, characterizing them through their basic properties of orness and dispersion. We also compare them to some families of OWA operators, namely the Binomial OWA operator, the Stancu OWA operator and the exponential RIM OWA operator, finding examples that are currently impossible to generalize through these parameterizations.


2012 ◽  
Vol 9 (1) ◽  
pp. 357-380 ◽  
Author(s):  
José Merigó ◽  
Anna Gil-Lafuente

A new method for decision making that uses the ordered weighted averaging (OWA) operator in the aggregation of the information is presented. It is used a concept that it is known in the literature as the index of maximum and minimum level (IMAM). This index is based on distance measures and other techniques that are useful for decision making. By using the OWA operator in the IMAM, we form a new aggregation operator that we call the ordered weighted averaging index of maximum and minimum level (OWAIMAM) operator. The main advantage is that it provides a parameterized family of aggregation operators between the minimum and the maximum and a wide range of special cases. Then, the decision maker may take decisions according to his degree of optimism and considering ideals in the decision process. A further extension of this approach is presented by using hybrid averages and Choquet integrals. We also develop an application of the new approach in a multi-person decision-making problem regarding the selection of strategies.


2019 ◽  
Vol 21 (6) ◽  
pp. 1831-1843
Author(s):  
Huidong Wang ◽  
Jinli Yao ◽  
Jun Yan ◽  
Mingguang Dong

Sign in / Sign up

Export Citation Format

Share Document