Volume 57, Number 4, July-August 2023
Recent developments of operations research and data sciences
|Page(s)||1843 - 1876|
|Published online||14 July 2023|
Maintenance groups evaluation under uncertainties: a novel stochastic free disposal hull in the presence of lognormally distributed data
Department of Statistics, College of Science, Arak Branch, Islamic Azad University, Arak, Iran
2 Department of Mathematics, College of Science, Arak Branch, Islamic Azad University, Arak, Iran
Accepted: 4 October 2022
Maintenance groups play an essential role in the successful operation of large companies and factories. Additionally, data envelopment analysis (DEA) is known as a valuable tool for monitoring the performance of maintenance groups. Especially, in contrast to the conventional DEA models that impose the convexity assumption into the technology, the free disposal hull (FDH) model provides a method for assessing the efficiency without the assumption of convexity and can be considered a valuable tool for determining one of the observed groups as the benchmark for each maintenance group. Meanwhile, because of the stochastic structure of data with lognormal distribution in the maintenance groups, this paper extends the FDH model in stochastic data with the lognormal distribution. Moreover, the method’s capabilities are confirmed based on some theorems, and a simulation study that illustrated the properties of the developed procedure is also performed. The developed methodology is applied to assess the performance of 21 maintenance groups of AZCO under uncertainty conditions.
Mathematics Subject Classification: 90C08 / 90C15 / 90B25
Key words: Free disposal hull / lognormal distribution / stochastic output-oriented FDH / stochastic efficiency / systems reliability
© The authors. Published by EDP Sciences, ROADEF, SMAI 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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