A New Robust Network Slack Based Measure Model
Alka Arya, Shubham Singh

TL;DR
This paper introduces a robust network slack-based measure model that effectively handles uncertain, negative, missing, and undesirable data in decision-making units, improving performance evaluation accuracy.
Contribution
It proposes a novel robust network SBM model incorporating uncertainty sets and worst-case constraints, addressing limitations of fuzzy and stochastic approaches.
Findings
Applied to Indian banks, demonstrating improved performance assessment.
Compared with traditional crisp network SBM, showing enhanced robustness.
Validated effectiveness in handling ambiguous and uncertain data.
Abstract
In real-life challenges, unforeseen and unknown occurrences commonly influence the data values, which may affect the performance of the problems. The performance of decision-making units (DMUs) is determined using the slack-based measure (SBM) model, which considers only crisp data values without uncertainty and is a black-box model. Many authors have used fuzzy SBM and stochastic SBM to deal with ambiguity and uncertainty, and many have used these approaches to deal with ambiguous and uncertain data. However, some ambiguous and uncertain data can not be taken as fuzzy logic and stochastic data due to the huge set of rules which must be given to construct a conceptual method. So, this paper tackles the uncertain data using robust optimization in which the input-output data are limited to remain within an uncertainty set, with extra constraints depending on the worst-case output for the…
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Taxonomy
TopicsRisk and Portfolio Optimization · Efficiency Analysis Using DEA · Supply Chain and Inventory Management
