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Hierarchical Evaluation of Criteria and Alternatives Within BWM: A Monte Carlo Approach

In: Advances in Best-Worst Method

Author

Listed:
  • Majid Mohammadi

    (Vrije Universiteit Amsterdam)

  • Jafar Rezaei

    (Delft University of Technology)

Abstract

In the Best-Worst Method (BWM), the criteria weights are typically characterized by intervals, each value in the interval representing an optimal weight for the associated criterion according to the preferences of the decision-maker. While the intervals can potentially provide the DM with more information, it makes it challenging to process the weights of a group of DMs, e.g., for computing the aggregated weights or evaluating a set of alternatives with respect to the interval weights. The problem compounds when the performance matrix of the alternatives is also acquired by the BWM. This paper presents a Monte Carlo approach to address these shortcomings. First, a Monte Carlo approach is developed to compute the aggregated interval weights for group decision-making problems, as well as the extent to which a criterion is more important than another based on the preferences of the group. Second, another Monte Carlo method is developed to evaluate and compare the alternatives based on the interval weights. The experiments validate the applicability of the proposed approach for the BWM.

Suggested Citation

  • Majid Mohammadi & Jafar Rezaei, 2022. "Hierarchical Evaluation of Criteria and Alternatives Within BWM: A Monte Carlo Approach," Lecture Notes in Operations Research, in: Jafar Rezaei & Matteo Brunelli & Majid Mohammadi (ed.), Advances in Best-Worst Method, pages 16-28, Springer.
    • Handle: RePEc:spr:lnopch:978-3-030-89795-6_2
    DOI: 10.1007/978-3-030-89795-6_2
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