A DEA Approach for Optimal Value Efficiency (OVE) Conditions Using Supporting Hyperplanes

Subject Areas : Electrical Engineering

Seyyed Ali Kazemipour ¹ , Hossein Abbasiyan ²

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Received: 2025-07-22 Accepted : 2025-09-24 Published : 2025-10-01

Keywords: Optimal Value Efficiency, Interval Scale Variables, Most Preferred Solution (MPS), Variable Returns to Scale (VRS), Control-Oriented Evaluation,

Abstract :

This paper presents a control-oriented approach to measuring Optimal Value Efficiency (OVE) in Data Envelopment Analysis (DEA) under negative data conditions, particularly when the data originate from interval scale (IS) variables such as differences in sales or other financial indicators. These IS variables are first decomposed into two ratio scale (RS) components to preserve DEA consistency. The OVE framework, originally introduced by Halme et al. (1998), incorporates the decision maker’s preferences into the evaluation process. The method first identifies technically efficient Decision Making Units (DMUs) and supports the decision maker in selecting the Most Preferred Solution (MPS) among them. To approximate the unknown value function's indifference contour at the MPS, a supporting hyperplane is constructed on the production possibility set (PPS). The value function is assumed to be strictly increasing in outputs, decreasing in inputs, and pseudo-concave. We implement this within a radial DEA model under Variable Returns to Scale (VRS), where both input minimization and output maximization are considered. The proposed approach effectively supports efficiency evaluation even with negative data, offering a robust tool for real-world decision environments involving dynamic systems and control strategies

 

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