Genetic Algorithm-enhanced Rank aggregation model to measure the performance of Pulp and Paper Industries

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10251925" target="_blank" >RIV/61989100:27240/22:10251925 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0360835222005551?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0360835222005551?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.cie.2022.108548" target="_blank" >10.1016/j.cie.2022.108548</a>

Result language
angličtina
Original language name
Genetic Algorithm-enhanced Rank aggregation model to measure the performance of Pulp and Paper Industries
Original language description
Performance measurement is a complex but important task required in all sectors. The problem however arises when usage of different methods for performance assessment provides different results. Under such circum-stances when there is a difference of opinions, rank aggregation methods can be used to provide the best solution to decision-makers (DMs). Such approaches, also known as data fusion approaches, combine ranked lists from various methods to generate a consensus. In this study, a novel rank aggregation method is proposed for addressing the problem of conflicting MCDM ranking results. The suggested method uses genetic algorithm (GA) to minimize the Euclidean distance between the ideal ranking and the ranking computed by multiple MCDM methods. This model is embedded into a hybrid multi-criteria decision-making (HMCDM) approach, which is divided into three distinct phases. The first phase identifies the most efficient alternatives; the second analyses the rankings obtained through various MCDM methods; and finally, a compromise ranking result is generated. The proposed approach is employed to measure the performance of Indian Pulp and Papermaking Industries (IPPI).
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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