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Simultaneous interpretive structural modelling and weighting (SISMW)

Author

Listed:
  • Mahdi Nasrollahi
  • Javaneh Ramezani
  • Mahmoud Sadraei
  • Mohammad Reza Fathi

Abstract

Multi-criteria decision-making (MCDM) methods have been implemented in many fields. In the meantime, several methods have been proposed to obtain the weight of the criteria that each method determines the importance of each criterion in a different way. In this paper, a new approach, called simultaneous interpretive structural modelling and weighting (SISMW), is proposed to solve a multi-criterion decision-making (MCDM) problem. Using SISMW, the weight of the criteria and the relationship between them will be determined simultaneously. In this approach, like the ISM method, pair comparison between criteria is made by the decision-maker to determine the relationships among the different criteria. With the help of these data, the weight of the criteria, as well as the causal (cause and effect) relationships between them, will be determined in 12 steps. The main advantage of this method is that only one stage of data collection is required to obtain weights and modelling, and so the research process will be faster. This will increase the reliability of the collected data because, in a one-step survey, the impact of time is minimised. This process can be useful for conceptualising and developing theories to help decision-makers understand the problem better.

Suggested Citation

  • Mahdi Nasrollahi & Javaneh Ramezani & Mahmoud Sadraei & Mohammad Reza Fathi, 2022. "Simultaneous interpretive structural modelling and weighting (SISMW)," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 32(1), pages 111-126.
  • Handle: RePEc:wut:journl:v:32:y:2022:i:1:p:111-126:id:2604
    DOI: 10.37190/ord220106
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    References listed on IDEAS

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    1. Sushil, 2018. "How to check correctness of total interpretive structural models?," Annals of Operations Research, Springer, vol. 270(1), pages 473-487, November.
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