An Alternative Ranking Order Method Accounting for Two-Step Normalization (AROMAN)-A Case Study of the Electric Vehicle Selection Problem

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25510%2F23%3A39920310" target="_blank" >RIV/00216275:25510/23:39920310 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097712" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097712</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2023.3265818" target="_blank" >10.1109/ACCESS.2023.3265818</a>

Jazyk výsledku

angličtina

Název v původním jazyce

An Alternative Ranking Order Method Accounting for Two-Step Normalization (AROMAN)-A Case Study of the Electric Vehicle Selection Problem

Popis výsledku v původním jazyce

Decision-making is a ubiquitous and paramount issue in the modern business world. Inappropriate decisions may lead to severe consequences for companies. Considering that the evaluation of alternatives is generally affected by several criteria, decision-making should be considered a very challenging task. From the 1945s to the present day, various multi-criteria decision-making (MCDM) methods have evolved, supporting people in the decision-making process. The main aim of this paper is to propose an original MCDM method and to demonstrate its applicability in an empirical case study that relates to the Electric Vehicle (EV) selection problem. To solve the electric vehicle selection problem for the last-mile delivery, we developed and applied a new MCDM method - the AROMAN (Alternative Ranking Order Method Accounting for Two-Step Normalization) method. The main contribution of the AROMAN method is coupling the linear and vector normalization techniques to obtain precise data structures used in further calculation. In addition, the original final ranking equation is developed. To demonstrate the robustness of the proposed method, a comparative analysis with other state-of-the-art MCDM methods is conducted. The results indicate a high level of confidence in the AROMAN method in the decision-making field. In addition, the sensitivity analysis is performed, and the results indicate a high level of stability. Nevertheless, based on the confident results, the managerial implications have also been indicated.

Název v anglickém jazyce

An Alternative Ranking Order Method Accounting for Two-Step Normalization (AROMAN)-A Case Study of the Electric Vehicle Selection Problem

Popis výsledku anglicky

Decision-making is a ubiquitous and paramount issue in the modern business world. Inappropriate decisions may lead to severe consequences for companies. Considering that the evaluation of alternatives is generally affected by several criteria, decision-making should be considered a very challenging task. From the 1945s to the present day, various multi-criteria decision-making (MCDM) methods have evolved, supporting people in the decision-making process. The main aim of this paper is to propose an original MCDM method and to demonstrate its applicability in an empirical case study that relates to the Electric Vehicle (EV) selection problem. To solve the electric vehicle selection problem for the last-mile delivery, we developed and applied a new MCDM method - the AROMAN (Alternative Ranking Order Method Accounting for Two-Step Normalization) method. The main contribution of the AROMAN method is coupling the linear and vector normalization techniques to obtain precise data structures used in further calculation. In addition, the original final ranking equation is developed. To demonstrate the robustness of the proposed method, a comparative analysis with other state-of-the-art MCDM methods is conducted. The results indicate a high level of confidence in the AROMAN method in the decision-making field. In addition, the sensitivity analysis is performed, and the results indicate a high level of stability. Nevertheless, based on the confident results, the managerial implications have also been indicated.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50204 - Business and management

Projekt

<a href="/cs/project/CK01000032" target="_blank" >CK01000032: Smart city logistika v kontextu e-commerce a plánů udržitelné městské mobility</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

IEEE ACCESS

ISSN

2169-3536

e-ISSN

2169-3536

Svazek periodika

11

Číslo periodika v rámci svazku

JAN 2023

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

39496-39507

Kód UT WoS článku

000979472600001

EID výsledku v databázi Scopus

2-s2.0-85153399216