Uncertain Data Envelopment Analysis: Box Uncertainty
Emma Stubington, Matthias Ehrgott, Omid Nohadani

TL;DR
This paper introduces a robust DEA method that incorporates box uncertainty to evaluate decision making units when data are imprecise, with applications in radiotherapy planning.
Contribution
It develops a new uncertain DEA model using box uncertainty and proposes an iterative solution approach for larger problems.
Findings
Exact solutions for small problems
Efficient iterative method for larger problems
Application to radiotherapy plan evaluation
Abstract
Data Envelopment Analysis (DEA) is a nonparametric, data driven technique used to perform relative performance analysis among a group of comparable decision making units (DMUs). Efficiency is assessed by comparing input and output data for each DMU via linear programming. Traditionally in DEA, the data are considered to be exact. However, in many real-world applications, it is likely that the values for the input and output data used in the analysis are imprecise. To account for this, we develop the uncertain DEA problem for the case of box uncertainty. We introduce the notion of DEA distance to determine the minimum amount of uncertainty required for a DMU to be deemed efficient. For small problems, the minimum amount of uncertainty can be found exactly, for larger problems this becomes computationally intensive. Therefore, we propose an iterative method, where the amount of…
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Taxonomy
TopicsEfficiency Analysis Using DEA · Health Systems, Economic Evaluations, Quality of Life · Advanced Statistical Process Monitoring
