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Multiple criteria decision-making techniques and their applications – a review of the literature from 2000 to 2014

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  • Abbas Mardani
  • Ahmad Jusoh
  • Khalil MD Nor
  • Zainab Khalifah
  • Norhayati Zakwan
  • Alireza Valipour

Abstract

Multiple criteria decision-making (MCDM) is considered as a complex decision-making (DM) tool involving both quantitative and qualitative factors. In recent years, several MCDM techniques and approaches have been suggested to choosing the optimal probable options. The purpose of this article is to systematically review the applications and methodologies of the MCDM techniques and approaches. This study reviewed a total of 393 articles published from 2000 to 2014 in more than 120 peer reviewed journals (extracted from Web of Science). According to experts’ opinion, these articles were grouped into 15 fields. Furthermore, these articles were categorised based on authors, publication date, name of journals, methods, tools, and type of research (MCDM utilising research, MCDM developing research, and MCDM proposing research). The results of this study indicated that in 2013 scholars have published articles more than in other years. In addition, the analytic hierarchy process (AHP) method in the individual tools and hybrid MCDM in the integrated methods were ranked as the first and second methods in use. Additionally, the European Journal of Operational Research as the first journal with 70 publications was the significant journal in this study. Finally, energy, environment and sustainability were ranked as the first areas that have applied MCDM techniques and approaches.

Suggested Citation

  • Abbas Mardani & Ahmad Jusoh & Khalil MD Nor & Zainab Khalifah & Norhayati Zakwan & Alireza Valipour, 2015. "Multiple criteria decision-making techniques and their applications – a review of the literature from 2000 to 2014," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 28(1), pages 516-571, January.
    • Handle: RePEc:taf:reroxx:v:28:y:2015:i:1:p:516-571
    DOI: 10.1080/1331677X.2015.1075139
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    Cited by:

    1. Tsai, Pei-Hsuan & Wang, Ying-Wei & Chang, Wen-Chang, 2023. "Hybrid MADM-based study of key risk factors in house-for-pension reverse mortgage lending in Taiwan's banking industry," Socio-Economic Planning Sciences, Elsevier, vol. 86(C).
    2. Tsai, Pei-Hsuan & Tang, Jia-Wei, 2023. "Consumers' switching intention towards E-commerce platforms’ store-to-store pickup services: The application of the extended PPM model," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    3. Jose Martino Neto & Valerio Antonio Pamplona Salomon & Miguel Angel Ortiz-Barrios & Antonella Petrillo, 2023. "Compatibility and correlation of multi-attribute decision making: a case of industrial relocation," Annals of Operations Research, Springer, vol. 326(2), pages 831-852, July.
    4. Tsai, Pei-Hsuan & Chen, Chih-Jou & Hsiao, Wei-Hung & Lin, Chin-Tsai, 2023. "Factors influencing the consumers’ behavioural intention to use online food delivery service: Empirical evidence from Taiwan," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    5. Tsai, Pei-Hsuan & Kao, Ya-Ling & Kuo, Szu-Yu, 2023. "Exploring the critical factors influencing the outlying island talent recruitment and selection evaluation model: Empirical evidence from Penghu, Taiwan," Evaluation and Program Planning, Elsevier, vol. 99(C).
    6. Rivero-Iglesias, Jose M. & Puente, Javier & Fernandez, Isabel & León, Omar, 2023. "Integrated model for the assessment of power generation alternatives through analytic hierarchy process and a fuzzy inference system. Case study of Spain," Renewable Energy, Elsevier, vol. 211(C), pages 563-581.
    7. Tsai, Pei-Hsuan & Kao, Ya-Ling & Tang, Jia-Wei, 2023. "Key factors influencing mass willingness to use CESPs under green concept: ECOCO smart recyclers as a case study," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    8. Vairetti, Carla & Aránguiz, Ignacio & Maldonado, Sebastián & Karmy, Juan Pablo & Leal, Alonso, 2024. "Analytics-driven complaint prioritisation via deep learning and multicriteria decision-making," European Journal of Operational Research, Elsevier, vol. 312(3), pages 1108-1118.

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