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Showing 2 results for Managerial Disposability
Dr Monireh Jahani Sayyad Noveiri, Prof. Sohrab Kordrostami , Ms Somayye Karimi Omshi,
Volume 12, Issue 2 (11-2021)
Abstract
Due to the changes of performance measures, a vital aspect for decision makers is finding optimal scale sizes of entities. Moreover, there are undesirable measures in many investigations. In the existing data envelopment analysis (DEA) approaches, optimal scale sizes (OSSs), average-cost efficiency (ACE) and average-revenue efficiency (ARE) of decision making units (DMUs) with desirable measures under strong disposability have been estimated while undesirable factors are presented in many real world examinations. Accordingly, in this research, OSSs and ARE of DMUs with undesirable outputs are addressed under managerial disposability. ARE is defined as the composite of scale and output allocative efficiencies under managerial disposability. To illustrate in detail, a two-stage DEA-based approach is rendered to estimate ARE and OSSs in the presence of undesirable outputs. A numerical example and an illustrative case are given to explain the proposed approach in this study.
Yasaman Zibaei Vishghaei, Sohrab Kordrostami, Alireza Amirteimoori, Soheil Shokri,
Volume 15, Issue 1 (7-2024)
Abstract
The traditional inverse data envelopment analysis (IDEA) models assess specific performance metrics in relation to changes in others, without taking into consideration the existence of random and undesirable outputs. This study presents a novel inverse DEA model with random and undesirable outputs, enabling the estimation of some random performance measures for changes of other random measures. The proposed chance-constrained inverse DEA model integrates both managerial and natural disposability constraints. By using the introduced approach, the estimation of natural disposable random inputs is presented for changes in random desirable outputs. Also, undesirable outputs are assessed for the perturbation of managerial disposable random inputs while the stochastic efficiency is maintained. The models are solved as linear problems, with a numerical example provided to illustrate their application. The findings indicate that this approach is effective for evaluating efficiency and performance metrics in scenarios involving random and undesirable outputs.
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