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Generalizing inverse data envelopment analysis through directional distance function
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AI
DOI：10.1016/j.eswa.2025.129996.png)
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Abstract
En 中文
Traditional inverse data envelopment analysis (DEA) models focus on either input-oriented or output-oriented scenarios. However, in many real-world problems, it is desirable to adjust both inputs and outputs. In this paper, we introduce directional distance function inverse DEA models that consider input and output perturbations simultaneously. Our method determines the ideal input–output combination for DMUs by considering both the intended outputs and the available additional inputs, in contrast to traditional inverse DEA models. To find the optimal combination of inputs and outputs for the DMU being evaluated, we use a mixed-oriented perturbation that preserves the efficiency scores of all DMUs. To illustrate the concept and logic of the proposed models, we provide a numerical example. Furthermore, we demonstrate the strength, expediency, and suitability of our models through a real-world application in the context of a supermarket chain.
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