The m-polar fuzzy set ELECTRE-I with revised Simos’ and AHP weight calculation methods for selection of non-traditional machining processes

Jagtap, Madan; Karande, Prasad

doi:10.31181/dmame060129022023j

Cited by 21 publications

(5 citation statements)

References 25 publications

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“…Even recently, the AHP has addressed more complicated problems. For example, [25] discussed the use of improvements to the m-polar fuzzy set (mFS) elimination and choice translating reality-I (ELECTRE-I) approach for calculating criteria weights for the selection of non-traditional machining (NTM) processes.…”

Section: Fahpmentioning

confidence: 99%

Integrating the Coupled Markov Chain and Fuzzy Analytic Hierarchy Process Model for Dynamic Decision Making

Huang,

Chen

2024

Axioms

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This paper introduces a pioneering model that merges coupled Markov chains (CMC) with the fuzzy analytic hierarchy process (FAHP) to enhance multi-criteria decision making (MCDM) amidst the dynamic interplay of criteria. Traditional MCDM frameworks often lack the granularity to manage the intricate and changing relationships among criteria. Our model addresses this gap by integrating fuzzy numbers into AHP, providing a nuanced means to handle the inherent uncertainty of decision criteria. The application of the Kronecker product in CMC enriches our approach, offering a data-driven analysis while mitigating excessive dependence on subjective expert opinion. A comprehensive numerical example underlines the model’s improved decision-making accuracy and efficiency, marking a substantial advancement in MCDM methodologies. This research contributes to the field by presenting a sophisticated yet practical framework for dynamic decision analysis in complex uncertain environments.

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Section: Fahpmentioning

confidence: 99%

Integrating the Coupled Markov Chain and Fuzzy Analytic Hierarchy Process Model for Dynamic Decision Making

Huang,

Chen

2024

Axioms

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“…This can be accomplished through the use of a sensitivity analysis (Prakash & Barua 2016;Sivaprakasam & Angamuthu, 2023). Sensitivity analysis is used to determine the MCDM method's efficacy and the outcome's stability under various settings, including modest alterations in the criteria (Pamuèar & Cirovic, 2015;Pamucar & Biswas, 2023;Jagtap & Karande, 2023). Sensitivity analysis can be performed in a variety of methods, one of which is by exchanging the criteria weights (Nüt et al, 2009;Radovanovic et al, 2023).…”

Section: Reports In Mechanicalmentioning

confidence: 99%

Modelling of green manufacturing barriers using a survey-integrated decision-making approach

Sharma,

Kumar,

Chatterjee

et al. 2023

Rep. Mech. Eng.

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Green manufacturing (GM) barriers encompass challenges and obstacles that impede the seamless adoption and effective implementation of environmentally sustainable practices within the manufacturing industry. This paper is dedicated to identifying, analyzing, ranking, and modeling the primary barriers that hinder the integration of GM practices. The study meticulously identifies 11 barriers through an extensive literature review and expert opinions. These barriers are subsequently validated by gathering insights from 90 survey responses. To evaluate these challenges, the study employs a survey-based integrated decision-making model that synergizes Fuzzy Analytic Hierarchy Process (FAHP), Interpretive Structural Modeling (ISM), and Matrice d'Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) methods. This model not only ranks the identified barriers but also delves into their intricate interrelationships. A sensitivity analysis is conducted to fortify the dependability of the findings. Among these obstacles, two stand out as the most prominent including “lack of research and development facilities” and “insufficient in-house knowledge on environmental issues”. This research represents a pioneering endeavor in its domain, shedding light on the vital realm of green manufacturing and its associated challenges.

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“…Jagtap and Karande [12] selected a nontraditional machining process using an m-polar fuzzy set elimination and choice translating reality-I (ELECTRE-I) approach. The criteria weights for the m-polar fuzzy ELECTRE-I method were evaluated using an analytical hierarchy process (AHP) approach and revised Simos' method.…”

Section: Literature Reviewmentioning

confidence: 99%

Determining Priorities in Infrastructure Management Using Multicriteria Decision Analysis

Bošnjak,

Jajac

2023

Sustainability

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This paper aims to form a concept of infrastructure management based on a multicriteria approach to determining management priorities. As the complexity of infrastructure construction and maintenance management requires looking at this problem from different aspects, the proposed multicriteria approach in this paper is based on the application of a two-phase analytical hierarchy process (AHP) method and technique for order of preference by similarity to ideal solution (TOPSIS) method. Using the two-phase AHP method, the process of determining the relative weights of the criteria is improved with the aim of providing better management of stakeholders as one of the essential preconditions for the success of the entire management process. In this way, it is desired to simulate the decision-making process as realistically as possible, in which the opinions and interests of all stakeholders are respected, but the key decision-maker is responsible for the final decision. Furthermore, with the help of the TOPSIS method, a ranking list of maintenance management priorities is formed, based on which it is possible to distribute limited financial resources intended for annual maintenance more rationally. The stability of the TOPSIS results was confirmed by a sensitivity analysis when changing the relative weights of the criteria. The proposed allocation of financial resources represents the basis for a better design of the maintenance management plan of the analyzed infrastructure elements, thus completing the observed gap in the existing literature. The aim of the above is to improve the planning function and at the same time to improve the implementation, monitoring, and control management functions, which creates a more efficient management system that can preserve the value of the analyzed infrastructure elements and extend their lifetime.

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The m-polar fuzzy set ELECTRE-I with revised Simos’ and AHP weight calculation methods for selection of non-traditional machining processes

Cited by 21 publications

References 25 publications

Integrating the Coupled Markov Chain and Fuzzy Analytic Hierarchy Process Model for Dynamic Decision Making

Integrating the Coupled Markov Chain and Fuzzy Analytic Hierarchy Process Model for Dynamic Decision Making

Modelling of green manufacturing barriers using a survey-integrated decision-making approach

Determining Priorities in Infrastructure Management Using Multicriteria Decision Analysis

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