Application of PROMETHEE method for green supplier selection: a comparative result based on preference functions

Abdullah, Lazim; Chan, W. H. K.; Afshari, Ali Reza

doi:10.1007/s40092-018-0289-z

Cited by 150 publications

(74 citation statements)

References 29 publications

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“…The results of those calculations are given in Table 2. The preference function of type 1-Usual was used, which was the most acceptable for qualitative criteria based on the assessments from a survey and possible values were low (Mareschal, 2018;Podvezko & Podviezko, 2010;Abdullah et al, 2019). Usual preference function did not have thresholds and each difference among alternatives, according to a given criterion, higher than 0 produced absolute preference.…”

Section: Data Analysis and Resultsmentioning

confidence: 99%

Model for Considering the Propensity of Students to Accept M-learning

Milošević

Rakić

Arsić

et al. 2020

Manage: J. Sustainable Bus. Manage, Solutions Emerging Econ.

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Research question: The aim of the study is to examine the inclination of engineering students in Serbia to adopt M-learning. Motivation: Mobile technologies provide new solutions in the current concept of learning. Students today have a new approach to learning since they live in the era of digital technology. The use of mobile technology in higher education came along with the technological enlightenment of professors and students, eradicating geographical boundaries and enabling cooperative learning. Idea: Mobile learning (M-learning) is unthinkable without the use of mobile devices and it plays an increasingly important role in the development of teaching methods in higher education. The research refers to the analysis and assessment of M-learning in higher education with the aim to identify problems and weaknesses related to the application of M-learning in order to set alternatives and criteria for the proposed model. Data: The survey was conducted through a questionnaire, and 341 correctly filled out questionnaires were collected. The obtained data were processed by the Visual Promethee software package. Tools: The research methodology was based on the Multi-criteria decision analysis (MCDA) method which was used in the form of PROMETHEE II/GAIA technique. In order to rank the student programmes from different study areas, groups of questions were used as criteria, and study programmes were used as alternatives. Findings: The acquired results indicated that M-learning was mostly used by the students of Engineering Management, since they frequently used mobile technology in certain required courses, as well as for obligatory business practice. It was also concluded that the study programme Metallurgical Engineering had the lowest performance concerning the implementation of M-learning among all other observed study programmes. Contribution: The conclusions of this study can add to the awareness-raising of the higher education institutions' interested parties and alert them to improve the quality of M-education by pointing out that there are differences in the adoption of M-learning depending on the type of engineering sciences.

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Section: Data Analysis and Resultsmentioning

confidence: 99%

Model for Considering the Propensity of Students to Accept M-learning

Milošević

Rakić

Arsić

et al. 2020

Manage: J. Sustainable Bus. Manage, Solutions Emerging Econ.

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“…In this research article, we evaluate green suppliers using bipolar fuzzy information in the form of trapezoidal bipolar fuzzy numbers. For this case study, criteria and alternatives were considered based on the work of Abdullah et al [23] and Gurel et al [39]. After initial screening, the following four alternatives denoted by S 1 , S 2 , S 3 , and S 4 were selected for further evaluation.…”

Section: Green Supplier Selectionmentioning

confidence: 99%

“…Moreover, Brans and Mareschal [22] presented two new extensions of the PROMETHEE method-PROMETHEE III, in which ranking is based on intervals, and PROMETHEE IV which is a continuous case of ranking. Abdullah et al [23] used the PROMETHEE method for the selection of green suppliers and provided a comparative study on the basis of preference functions. Behzadian et al [24] provided a comprehensive study of PROMETHEE methodologies and applications.…”

Section: Introductionmentioning

confidence: 99%

“…Abdullah et al [23] used the PROMETHEE method for the selection of green suppliers and provided a comparative study on the basis of preference functions. Behzadian et al [24] provided a comprehensive study of PROMETHEE methodologies and applications. Govindan et al [25] presented an application of a PROMETHEE-based method for the ranking of green suppliers in a food supply chain.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Criteria Group Decision-Making for Selection of Green Suppliers under Bipolar Fuzzy PROMETHEE Process

2020

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The preference ranking organization method for enrichment of evaluations (PROMETHEE) method considers a significant outranking class of multi-criteria decision analysis (MCDA), as it is easy to deal with its simple computations. In the PROMETHEE, different preference functions are used according to the type and nature of attributes or criteria that demonstrate the clearness and reliability of this method. This study provides a new version of the PROMETHEE method using bipolar fuzzy information, named the bipolar fuzzy PROMETHEE method. Bipolar fuzzy sets or numbers constitute an asymmetrical relationship between two judgmental factors of human reasoning. Vague and imprecise knowledge is characterized by bipolar fuzzy linguistic terms which are further represented in the form of trapezoidal bipolar fuzzy numbers. The trapezoidal bipolar fuzzy numbers are used by analysts to assign the preferences of alternatives on the basis of criteria. Further, a ranking function of bipolar fuzzy numbers is considered to access the crisp real preferences of alternatives. The entropy weighting information is employed to calculate the weights of attributes by considering the condition of normality. A numerical example such as the selection of green suppliers by using the bipolar fuzzy PROMETHEE is performed on the basis of the usual criterion preference function in order to explain the procedure of the proposed method. Comparable results are derived by using the combination of linear and level preference functions. The results obtained by using different types of preference functions are the same, representing the authenticity of the proposed bipolar fuzzy PROMETHEE method.

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“…It is a very simple ranking method understanding and application compared to other MCDM methods. It is well adapted to problems with multiple or conflicting criteria of the finite set of alternatives [33]. When comparing the two alternatives considered a and b in terms of criteria, a P function is preferred.…”

Section: Optimization and Decision Makingmentioning

confidence: 99%

Application of a multiple criteria analysis for the selection of appropriate radical based processes in treatment of car wash wastewater

Durna

Genç

2020

Environmental Engineering Research

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In this study, the treatment of car wash wastewater was investigated by radical based hybrid/combined processes. Proposed processes, (Microwave (MW) + persulfate (PS) + Electrocoagulation (EC)), (ozone (O3) + PS + EC) and (MW + PS + O3), were optimized with Taguchi orthogonal array technique for maximum COD removal. The COD removal under optimum conditions was obtained to be 84%, 64.9% and 61.4%, for (MW + PS+ EC), (O3 + PS+ EC) and (MW + PS+ O3) processes, respectively. Operating costs for (MW + PS + EC), (O3 + PS + EC) and (MW + PS + O3) processes have been calculated as 0.2614, 0.1335 and 0.2653 €/L wastewater under optimum operating conditions. Pareto analysis showed that MW time and PS dose are very effective parameters but especially ozone related parameters have no significant effect on COD removal. Processes were evaluated with the PROMETHEE approach in terms of treatment efficiency, operating cost, sludge formation, and preferability criteria to determine the most suitable among the three alternative processes. As a result, the preference order of the processes for the treatment of car wash wastewater with radical based treatment processes was found as (MW + EC + PS) > (MW + O3 + PS) > (O3 + EC + PS).

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Application of PROMETHEE method for green supplier selection: a comparative result based on preference functions

Cited by 150 publications

References 29 publications

Model for Considering the Propensity of Students to Accept M-learning

Model for Considering the Propensity of Students to Accept M-learning

Multi-Criteria Group Decision-Making for Selection of Green Suppliers under Bipolar Fuzzy PROMETHEE Process

Application of a multiple criteria analysis for the selection of appropriate radical based processes in treatment of car wash wastewater

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