Determination of the Efficient Frontier of Weak and Strong Production Possibility Set using Genetic Algorithms

Authors

  • Sharif Malakouti Department of Basic Sciences, Shahr-e Rey (Yadegar-e Emam) Branch, Islamic Azad University, Tehran, Iran
  • Reza Kargar Department of Basic Sciences, Qom branch, Islamic Azad University, Qom, Iran
  • Zohre Taeb Department of Basic Sciences, Shahr-e Rey (Yadegar-e Emam) Branch, Islamic Azad University, Tehran, Iran
  • Hadi Bagherzade Department of Basic Sciences, Shahr-e Rey (Yadegar-e Emam) Branch, Islamic Azad University, Tehran, Iran
  • Leila Karamali Department of Basic Sciences, Shahr-e Rey (Yadegar-e Emam) Branch, Islamic Azad University, Tehran, Iran

Keywords:

Farkas'lemma, the genetic algorithm(GA), affine independence, efficiency frontier and defining hyperplanes, Artificial Neural

Abstract

This article uses genetic algorithms and geometric properties of production plants and a constructive way to determine strong and weak hyperplanes in the Efficient Border collection. Normal vectors in a constructive hyperplane are obtained by mapping the space of hyperplanes based on the real set. The production possibility set (PPS) of an input-based system is determined by utilizing the axiom. By using the genetic algorithm (GA) and the geometric properties of the PPS, this paper presents a solution to choose the strong and weak definitions of the hyperplanes of the so-called "efficient frontier". The generated hyperplanes are crucial for determining the returns to scale and modifying the DMU ranking methods. The hyperplane equation can enable a simple and more accurate analysis of the sensitivity of DEA methods. A numerical example is used to show how the algorithm is used in this paper and how the results are compared.

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Published

2025-11-09

Issue

Vol. 19

Section

Vol. 19, No. 7, (2025)

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