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An analytical framework for the best–worst method

Author

Listed:
  • Wu, Qun
  • Liu, Xinwang
  • Zhou, Ligang
  • Qin, Jindong
  • Rezaei, Jafar

Abstract

Since the development of the best–worst method (BWM) in 2015, it has become a popular research focus in multi-criteria decision-making. The original optimization problem of the BWM is a nonlinear min–max model that can lead to multiple optimal solutions, while the linear model of the BWM produces a unique solution. The two models need to be solved by optimization software packages. In addition, although the linear model of the BWM can obtain a unique solution, it produces different feasible regions than the nonlinear model of the BWM, and it changes the objective function. This study aims to solve the nonlinear model of the BWM mathematically to obtain the analytical forms of the optimal solutions. First, we transform the original nonlinear model of BWM into an equivalent optimization model driven by the optimally modified comparison vectors. The equivalent BWM provides a solid basis for computing the analytical solutions. Second, for not-fully consistent pairwise comparison systems, we strictly prove that there is only one unique optimal solution with three criteria, and there might be multiple optimal solutions with more than three criteria. We further develop the analytical forms of these unique and multiple optimal solutions and the optimal interval weights. Third, we develop a secondary objective function to select a unique solution for the BWM. The secondary objective function retains all the characteristics of the original nonlinear model of the BWM, and we find the unique solution analytically. Finally, some numerical examples are examined, and a comparative analysis is performed to demonstrate the effectiveness of our analytical solution approach.

Suggested Citation

  • Wu, Qun & Liu, Xinwang & Zhou, Ligang & Qin, Jindong & Rezaei, Jafar, 2024. "An analytical framework for the best–worst method," Omega, Elsevier, vol. 123(C).
    • Handle: RePEc:eee:jomega:v:123:y:2024:i:c:s030504832300138x
    DOI: 10.1016/j.omega.2023.102974
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