A rankability-based fuzzy decision making procedure for oil supplier selection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F23%3AA250387A" target="_blank" >RIV/61988987:17610/23:A250387A - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61988987:17610/23:A2402CD1 RIV/61989100:27240/23:10253778

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S1568494623009742?ref=pdf_do" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1568494623009742?ref=pdf_do</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.asoc.2023.110956" target="_blank" >10.1016/j.asoc.2023.110956</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A rankability-based fuzzy decision making procedure for oil supplier selection

Popis výsledku v původním jazyce

Multiple-criteria decision-making (MCDM) explicitly assesses several conflicting criteria for our daily livesin selecting products, vehicles, techniques, etc. Weighting on criteria is a critical step in MCDM as theinvalid weight of criteria will lead to a wrong decision. The proposed method addresses some drawbacksof the entropy-based weighting method commonly used in MCDM. The proposed new weighting methodconsiders multiple evaluation factors, including the performance of the decision-maker, the edge weightbasis of a digraph, and dominance relationships in the data. By incorporating these factors, the proposedmethod overcomes the limitations of the entropy-based method and reduces the total computation required.We conducted experiments using sustainable transportation data and comprehensively analyzed the results.We also propose a fuzzy MCDM model incorporating the proposed weighting method and Dempster–Shafertheory. Our model aims to handle uncertainty and imprecision in decision-making. Finally, the correctness andeffectiveness of the proposed model were tested on real-life applications. The results of these tests demonstratedthat the proposed method provides a practical and effective approach to decision-making in various domains.Overall, the work introduces a new weighting method based on rankability in MCDM, addresses the limitationsof the entropy-based method, and presents a fuzzy MCDM model for handling uncertainty. The experimentalresults suggest that the proposed approach is promising and offers valuable insights for decision-makers.

Název v anglickém jazyce

A rankability-based fuzzy decision making procedure for oil supplier selection

Popis výsledku anglicky

Multiple-criteria decision-making (MCDM) explicitly assesses several conflicting criteria for our daily livesin selecting products, vehicles, techniques, etc. Weighting on criteria is a critical step in MCDM as theinvalid weight of criteria will lead to a wrong decision. The proposed method addresses some drawbacksof the entropy-based weighting method commonly used in MCDM. The proposed new weighting methodconsiders multiple evaluation factors, including the performance of the decision-maker, the edge weightbasis of a digraph, and dominance relationships in the data. By incorporating these factors, the proposedmethod overcomes the limitations of the entropy-based method and reduces the total computation required.We conducted experiments using sustainable transportation data and comprehensively analyzed the results.We also propose a fuzzy MCDM model incorporating the proposed weighting method and Dempster–Shafertheory. Our model aims to handle uncertainty and imprecision in decision-making. Finally, the correctness andeffectiveness of the proposed model were tested on real-life applications. The results of these tests demonstratedthat the proposed method provides a practical and effective approach to decision-making in various domains.Overall, the work introduces a new weighting method based on rankability in MCDM, addresses the limitationsof the entropy-based method, and presents a fuzzy MCDM model for handling uncertainty. The experimentalresults suggest that the proposed approach is promising and offers valuable insights for decision-makers.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

<a href="/cs/project/EF17_049%2F0008414" target="_blank" >EF17_049/0008414: Centrum pro výzkum a vývoj metod umělé intelligence v automobilovém průmyslu regionu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

APPLIED SOFT COMPUTING

ISSN

1568-4946

e-ISSN

1872-9681

Svazek periodika

—

Číslo periodika v rámci svazku

December

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

16

Strana od-do

—

Kód UT WoS článku

001112876600001

EID výsledku v databázi Scopus

2-s2.0-85175698909