Rough Best-Worst Method for Supplier Selection in Biofuel Companies based on Green criteria

Kazemitash, Navid; Fazlollahtabar, Hamed; Abbaspour, Mohammadmahdi

doi:10.31181/oresta20402001k

Cited by 27 publications

(24 citation statements)

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“…According to the fuzzy concept (e.g., [ 12 – 18 ]), the uncertain data are represented by fuzzy sets and their membership functions that must be known in advance as a part of the problem itself. According to the rough concept [ 7 , 19 – 21 ], the uncertain data are represented by rough sets and/or rough functions that use the lower and upper approximations for the rough objective and constraints that must be known in advance as a part of the problem itself. Finally, according to the interval concept, the uncertain coefficients are represented as closed intervals that are supposed to be known in advance as a part of the problem itself.…”

Section: Introductionmentioning

confidence: 99%

Development of an Appropriate Uncertainty Model with an Application to Solid Waste Management Planning

Elazeem

Khalifa

Pamučar

et al. 2022

Computational Intelligence and Neuroscience

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The purpose of this study is to achieve a novel and efficient method for treating the interval coefficient linear programming (ICLP) problems. The problem is used for modeling an uncertain environment that represents most real-life problems. Moreover, the optimal solution of the model represents a decision under uncertainty that has a risk of selecting the correct optimal solution that satisfies the optimality and the feasibility conditions. Therefore, a proposed algorithm is suggested for treating the ICLP problems depending on novel measures such as the optimality ratio, feasibility ratio, and the normalized risk factor. Depending upon these measures and the concept of possible scenarios, a novel and effective analysis of the problem is done. Unlike other algorithms, the proposed algorithm involves an important role for the decision-maker (DM) in defining a satisfied optimal solution by using a utility function and other required parameters. Numerical examples are used for comparing and illustrating the robustness of the proposed algorithm. Finally, applying the algorithm to treat a Solid Waste Management Planning is introduced.

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Section: Introductionmentioning

confidence: 99%

Development of an Appropriate Uncertainty Model with an Application to Solid Waste Management Planning

Elazeem

Khalifa

Pamučar

et al. 2022

Computational Intelligence and Neuroscience

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“…Extensive MCDM methods have been proposed for supplier selection, like the analytic hierarchy process (AHP), analytic network process (ANP), data envelopment analysis (DEA), fuzzy set theory, genetic algorithm (GA), mathematical programming, technique order preference by similarity to ideal solution (TOPSIS), and so forth and their hybrids [16][17][18][19].…”

Section: Methods For Green Supplier Selectionmentioning

confidence: 99%

“…Calculate the upper and lower bounds of objective functions Z 1 ∼ Z 4 based on equations ( 12), (17), and (18), the results are shown in Table 8.…”

Section: Solution Of Fuzzy Gp Modelmentioning

confidence: 99%

Low‐Carbon Supplier Selection Using Fuzzy AHP and Goal Programming Approach

Huang

2022

Discrete Dynamics in Nature and Society

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It has become the leading world trend that all countries join hands to respond to climate change and promote green and low-carbon development. In this background, more and more enterprises seek to control carbon emissions from the source and reduce indirect carbon emissions, consequently paying increased importance to the carbon footprint of their suppliers. Most of the earlier studies concerning supplier selection mainly focus on traditional factors, such as quality, service, and lead time, but minimal importance to the carbon emission of supplies. This study fills this gap by incorporating carbon emission criteria into supplier selection and presenting a method of combining fuzzy analytic hierarchy process (AHP) and fuzzy goal programming (GP) to address the problem of supplier selection and order quota allocation. Firstly, we use fuzzy AHP to evaluate the relevance and importance of supplier selection criteria according to the experts’ opinions. Second, each objective is given weights according to the fuzzy AHP results, and then the fuzzy GP method is used for supplier selection and order quota allocation. Finally, we implement an example study with a data set from a realistic situation, and the results confirm the effectiveness of the proposed method in an uncertain environment.

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“…Rough set is found to be extremely suitable for classifying the air pollutant index in Malaysia and Singapore (Wibowo et al, 2018). Furthermore, the theory has been used to various uncertainty handling, and multicriteria decision-making applications (Kazemitash et al, 2021;Naouali et al, 2020;Pal et al, 2018;Saha et al, 2010;Suresh et al, 2012;Tang et al, 2020;Wang & Zhang, 2014;Ye et al, 2021). In our present study, we have used rough set-based "if-then" rules and inverse-decision rules for air-quality prediction and interpreting/explaining the decision-making process, respectively.…”

Section: Introductionmentioning

confidence: 99%

Z-number-based AQI in rough set theoretic framework for interpretation of air quality for different thresholds of PM2.5 and PM10

Dutta

Pal

2022

Environ Monit Assess

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Kolkata has a reputation for being one of the world’s most polluted cities, particularly in the post-monsoon months of October, November, and December. Diwali, a Hindu festival, coincides with these months where a large number of firecrackers are set off followed by high emissions of air pollutants. As a result, the air quality index (AQI) deteriorates to “very poor” (301 ≤ AQI ≤ 400) and “poor” (201 ≤ AQI ≤ 300) categories. This situation stays for several days to a month. The present study aims to identify the thresholds for PM 2.5 and PM 10 that cause the AQI of Kolkata to deteriorate to “very poor” and “poor.” For this purpose, we have used a rough set theory-based condition-decision support system to predict the aforementioned categories of AQI. We have developed a Z-number-based novel quantification measure of semantic information of AQI to assess the reliability of the outcomes, as generated from the condition-decision-based decision rules, during post-monsoon season. The result reveals the best possible forecast of AQI with linguistic summarization of the reliability or confidence for different threshold ranges of PM 10 and PM 2.5 . Inverse-decision rules based on rough set theory are utilized to justify and validate the forecasts. The explainability of the condition-decision support system is demonstrated/visualized using a flow graph that maps rough-rule-based different decision paths between input and output with strength, certainty, and coverage. The investigation resulted in an advanced intelligent environmental decision support system (IEDSS) for air-quality prediction.

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Rough Best-Worst Method for Supplier Selection in Biofuel Companies based on Green criteria

Cited by 27 publications

References 0 publications

Development of an Appropriate Uncertainty Model with an Application to Solid Waste Management Planning

Development of an Appropriate Uncertainty Model with an Application to Solid Waste Management Planning

Low‐Carbon Supplier Selection Using Fuzzy AHP and Goal Programming Approach

Z-number-based AQI in rough set theoretic framework for interpretation of air quality for different thresholds of PM2.5 and PM10

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