Decision-making approaches based on color spectrum and D-TOPSIS method under rough environment

Sarwar, Musavarah

doi:10.1007/s40314-020-01284-7

Cited by 43 publications

(16 citation statements)

References 35 publications

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“…e effectiveness of a sanitizer in COVID-19 was discussed by Akram et al [30]. For more knowledge and applications, the readers are suggested to study [31][32][33][34][35][36][37][38][39][40][41][42][43][44].…”

Section: Introductionmentioning

confidence: 99%

Hamacher Interactive Hybrid Weighted Averaging Operators under Fermatean Fuzzy Numbers

Shahzadi

Muhiuddin

Butt

et al. 2021

Journal of Mathematics

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A Fermatean fuzzy set is a more powerful tool to deal with uncertainties in the given information as compared to intuitionistic fuzzy set and Pythagorean fuzzy set and has energetic applications in decision-making. Aggregation operators are very helpful for assessing the given alternatives in the decision-making process, and their purpose is to integrate all the given individual evaluation values into a unified form. In this research article, some new aggregation operators are proposed under the Fermatean fuzzy set environment. Some deficiencies of the existing operators are discussed, and then, new operational law, by considering the interaction between the membership degree and nonmembership degree, is discussed to reduce the drawbacks of existing theories. Based on Hamacher’s norm operations, new averaging operators, namely, Fermatean fuzzy Hamacher interactive weighted averaging, Fermatean fuzzy Hamacher interactive ordered weighted averaging, and Fermatean fuzzy Hamacher interactive hybrid weighted averaging operators, are introduced. Some interesting properties related to these operators are also presented. To get the optimal alternative, a multiattribute group decision-making method has been given under proposed operators. Furthermore, we have explicated the comparison analysis between the proposed and existing theories for the exactness and validity of the proposed work.

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Section: Introductionmentioning

confidence: 99%

Hamacher Interactive Hybrid Weighted Averaging Operators under Fermatean Fuzzy Numbers

Shahzadi

Muhiuddin

Butt

et al. 2021

Journal of Mathematics

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“…A rough TOPSIS method by Song et al [32] was proposed for this purpose. Sarwar [33] provided a rough D-TOPSIS approach to improve the decision-making process and implemented this approach in agricultural farming. Moreover, Sarwar et al [34] presented a rough ELECTRE II technique to enhance the working conditions and to reduce the loss of energy in the automatic manufacturing process.…”

Section: Introductionmentioning

confidence: 99%

Selection of Suppliers in Industrial Manufacturing: A Fuzzy Rough PROMETHEE Approach

Sarwar¹,

Zafar

Majeed

et al. 2022

Mathematical Problems in Engineering

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In supply chain management (SCM), the selection of suppliers plays a vital role in an efficient production process. Over the last few years, to form a trade-off between the quantitative and qualitative criteria, the selection of suppliers in SCM is considered very conclusive. The decisions generally demand different criteria to balance between every possible inconsistent parameters involving subjectivity and uncertainty in the process. The evaluation information mostly depends on the experience and knowledge of experts that are unsure and indistinct. This study introduces a novel decision-making method by integrating rough approximations with fuzzy numbers and preference ranking organization method for enrichment evaluation (PROMETHEE) to deal with subjective and objective vagueness in the assessment of decision makers. To minimize the dependency on experts’ judgements, entropy weights are computed from the original data set. The preference index is then computed using entropy weights and deviations among alternatives. The alternatives are ranked using the intersection of both positive flow and negative flow. To show the significance and importance of fuzzy rough PROMETHEE method, a case study of supplier selection in industrial manufacturing is discussed in detail. The developed method is effectively used to rank the supplier alternatives under given criteria. The results are then compared with different rough numbers and fuzzy numbers based on MCDM methods. The fuzzy rough PROMETHEE method can be efficiently used for the selection of the best suppliers to reduce losses and maximize the production process.

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“…Jiang et al [29] proposed a rough sets-enabled risk decision-making approach, which not only consider the decision risk but also offer instructions to select the appropriate semantic explanation. Sarwar [30] proposed a rough D-TOPSIS method to manipulate the subjectiveness and vagueness of MADM, which unites the competency to analyze changeable and ambiguous information without additional assumptions. Fei et al [31] proposed a rough D-TOPSIS method to solve the problem of the human resources selection, and an effectual method was applied by authors to describe the changeable data and information called D-numbers.…”

Section: Introductionmentioning

confidence: 99%

A Rule-Based Decision Support Method Combining Variable Precision Rough Set and Stochastic Multi-Objective Acceptability Analysis for Multi-Attribute Decision-Making

Zhu

Liu

Zhang

et al. 2022

Mathematical Problems in Engineering

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In an open environment, the demands of users are diverse and dynamic because users can participate in product design from beginning to end. Owing to this, the disorderly and unplanned participation of users will greatly increase the complexity of multi-attribute decision-making (MADM) in the product design process. In order to ensure the smooth development of the open design process, the decision support model and method need to repeatedly provide decision makers (DMs) with necessary decision support information in a relatively short period of time, which can realize the evaluation of the scheme and improve the utilization efficiency of community resources. However, the process of eliciting preference information is complex, exhausting, inefficient, and time-consuming in existing methods, which will result in a poor decision-making. With the purpose of optimizing the eliciting process in MADM, a rule-based decision support method is proposed in this paper, where the process of eliciting preference information and decision-making are synchronized and guided by pre-extracted decision rules. The rules are deduced from comparison relations on attributes and their outcomes through the combination of variable precision rough set approach (VPRS) and stochastic multi-objective acceptability analysis (SMAA). With the concept of attribute reduction and approximation accuracy in rough set theory, the extracted rules could eliminate redundant attributes and assign the relative priority of preference information. Based on the extracted rules, the multi-attribute decision-making process could be carried out step by step in an orderly manner. In each step, DMs only need to provide partial preference information by non-quantitative statements according to extracted rules. Once the decision result is reliable enough, the eliciting and decision-making process can be terminated promptly. In order to validate the proposed approach, experiments of decision rule extraction are implemented, and the results show that the proposed approach is effective both in the weak rule extraction and the strong rule extraction.

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Decision-making approaches based on color spectrum and D-TOPSIS method under rough environment

Cited by 43 publications

References 35 publications

Hamacher Interactive Hybrid Weighted Averaging Operators under Fermatean Fuzzy Numbers

Hamacher Interactive Hybrid Weighted Averaging Operators under Fermatean Fuzzy Numbers

Selection of Suppliers in Industrial Manufacturing: A Fuzzy Rough PROMETHEE Approach

A Rule-Based Decision Support Method Combining Variable Precision Rough Set and Stochastic Multi-Objective Acceptability Analysis for Multi-Attribute Decision-Making

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