An integrated rough ELECTRE II approach for risk evaluation and effects analysis in automatic manufacturing process

Sarwar, Musavarah; Akram, Muhammad; Лю, Пэйдэ

doi:10.1007/s10462-021-10003-5

Cited by 57 publications

(17 citation statements)

References 41 publications

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“…Therefore, many scholars have carried out a variety of studies on it. Sarwar et al (2021) proposed a novel risks analysis approach that integrates the rough set theory and ELimination and Choice Translating REality (ELECTRE) II method to cope with vagueness and subjectivity in experts' opinions. Dong and Cooper (2016) proposed an AHP-based approach evaluate sourcing risks for a telecommunication equipment and service firm.…”

Section: Quantitative Approaches In Supply Chain Risk Managementmentioning

confidence: 99%

A novel hesitant-fuzzy-based group decision approach for outsourcing risk

Yazdani

Mohammed

Bai

et al. 2021

Expert Systems with Applications

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Section: Quantitative Approaches In Supply Chain Risk Managementmentioning

confidence: 99%

A novel hesitant-fuzzy-based group decision approach for outsourcing risk

Yazdani

Mohammed

Bai

et al. 2021

Expert Systems with Applications

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“…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. For the selection of suppliers in green supply chain implementation, a rough number MAIRCA method and hybrid rough number DEMATEL-ANP [14] were presented.…”

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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“…Feng et al (Feng et al, 2011) investigated the concept of soft rough set. For more appropriate ideas and assumptions associated to this research paper, the readers referred to (Akram et al, 2021a; Akram et al, 2021b; Akram et al, 2021c; Atik et al, 2021; Bibin et al, 2020; Gowers, 2010; Munakata & Pawlak, 1996; Pawlak, 1985; Pawlak, 1996; Sarwar, 2020; Sarwar et al, 2021; Uluay & Sahin, 2020; Wang et al, 2009; Zadeh 1965; Zafer & Akram, 2018; Zhan et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Group decision‐making analysis based on distance measures under rough environment

Fatima

Sarwar²,

Zafar

et al. 2022

Expert Systems

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A rough set as a superset of a crisp set is a mathematical tool to cope with uncertainty using initial data without additional assumptions and pre-defined parameters.Using the technique of upper and lower approximations, rough models provide a complete description of the problem. This paper aims to introduce the notion of distance function, which is a metric, in rough graphs. We establish formulae of distance function, degree and radius of certain products of rough graphs in terms of initial given rough graphs. We discuss the concepts of weak isomorphism and isomorphism in rough graphs, and describe the properties of isomorphic rough graphs. We demonstrate the significance and importance of the distance function with an application to a decision making problem concerning organ trafficking networks. An application of rough matrices in steam valve systems is discussed, and we show how they can help to identify the potential failures and risk components during the power generation process. The results obtained under the rough model are compared with existing extensions of graphs and fuzzy based approaches.

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An integrated rough ELECTRE II approach for risk evaluation and effects analysis in automatic manufacturing process

Cited by 57 publications

References 41 publications

A novel hesitant-fuzzy-based group decision approach for outsourcing risk

A novel hesitant-fuzzy-based group decision approach for outsourcing risk

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

Group decision‐making analysis based on distance measures under rough environment

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