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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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New hybrid EC-PROMETHEE method with multiple iterations of random weight ranges: Step-by-step application in Python.

Marcio Pereira Basilio1,2, Valdecy Pereira2, Fatih Yiğit3

  • 1Controladoria-Geral do Estado do Rio de Janeiro (CGE), Avenida Erasmo Braga, 118, Centro, Rio de Janeiro 20020-000, Brazil.

Methodsx
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Summary

This study introduces EC-PROMETHEE, a hybrid decision-making method that reduces subjectivity in assigning criteria weights. It uses a range of weights to generate a consistent final ranking, improving multi-criteria decision analysis.

Keywords:
CriticDecision makerEC-PROMETHEEEntropyMcdaOperations researchPromethee

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Area of Science:

  • Operations Research
  • Decision Science

Background:

  • Multi-criteria decision-making (MCDM) involves assigning weights to criteria to reflect importance.
  • Existing methods for weight determination include objective, subjective, and hybrid approaches, each with limitations in expert discretion.

Purpose of the Study:

  • To develop and present a novel hybrid MCDM method, EC-PROMETHEE, integrating Entropy, CRITIC, and PROMETHEE.
  • To reduce subjectivity and enhance consistency in the criteria weight definition process.

Main Methods:

  • Developed EC-PROMETHEE, a hybrid method combining Entropy and CRITIC for weight range generation.
  • Emulated weight ranges 'n' times to create a set of weights for ranking.
  • Applied the method to select rotary-wing aircraft for military police service using a Python tool.

Main Results:

  • The EC-PROMETHEE method successfully reduces discretion in determining criteria weights.
  • The innovation lies in utilizing a range of weights per criterion, rather than single values.
  • The model generates 'n' rankings, consolidating them into a single, consistent final ranking.

Conclusions:

  • EC-PROMETHEE offers a more robust approach to MCDM by incorporating weight ranges.
  • The method enhances the consistency and reliability of decision-making outcomes.
  • The Python tool facilitates practical application of this advanced MCDM technique.