A hybrid genetic algorithm and DEA approach for multi-criteria fixed cost allocation

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

This paper proposes a hybrid genetic algorithm and data envelopment analysis framework for solving the fixed cost allocation (FCA) problem. The proposed framework allows managers to incorporate different FCA sub-objectives for efficient and inefficient decision-making units (DMUs) and solves the FCA problem so that the total entropy of resource allocation for efficient DMUs is maximized, and correlation between resource allocation and efficiency scores of inefficient DMUs is minimized. The FCA sub-objectives and solutions are kept consistent with the overall management objective of rewarding efficient DMUs by allocating to them fewer fixed cost resources. We illustrate the application of our approach using an example from the literature. The results of our study indicate that the solution values obtained in our study are superior to those obtained in other studies under various criteria. Additionally, the relative gap between the solution obtained using our procedure, and the upper bound on the optimal value is approximately 1%, which indicates that our solution is very close to the optimal solution.

Original languageEnglish (US)
Pages (from-to)7315-7324
Number of pages10
JournalSoft Computing
Volume22
Issue number22
DOIs
StatePublished - Nov 1 2018

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Cost Allocation
Hybrid Genetic Algorithm
Multi-criteria
Genetic algorithms
Decision Making
Decision making
Unit
Resource Allocation
Costs
Resource allocation
Data Envelopment Analysis
Data envelopment analysis
Optimal Solution
Entropy
Upper bound
Managers
Resources
Framework

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Geometry and Topology

Cite this

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A hybrid genetic algorithm and DEA approach for multi-criteria fixed cost allocation. / Pendharkar, Parag C.

In: Soft Computing, Vol. 22, No. 22, 01.11.2018, p. 7315-7324.

Research output: Contribution to journalArticle

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