Stochastic Multiplicative DEA Model for Measuring the Efficiency and Ranking of DMUs under VRS Technology

Dibachi, H.; Behzadi, Mohammad Hassan; Izadikhah, Mohammad

doi:10.17485/ijst/2014/v7i11.19

Cited by 13 publications

(3 citation statements)

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“…[54] [9] and [55] constructed a stochastic DEA model for utilizing resource allocation. while [56,57,58] utilized the stochastic DEA model to assess supply chains. [59] suggested a stochastic fuzzy DEA model to investigate different stochastic fuzzy DEA models having inputs and outputs as fuzzy random values.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Efficiency Assessment of Egyptian Pharmaceutical Companies through the Usage of Two-Stage Uncertainty Data Envelopment Analysis Algorithm

El-Demerdash,

Ming,

Abelrahman

2024

Preprint

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Evaluating the performance of pharmaceutical manufacturing companies is crucial for their success and competitiveness in the rapidly evolving Egyptian market. However, traditional data envelopment analysis (DEA) approaches often overlook the inherent uncertainty in the data, which can significantly impact efficiency assessments. To address this issue, this paper proposes a novel integrated DEA model to enhance the efficiency assessment of Egyptian pharmaceutical manufacturing companies. This two-stage framework effectively handles imprecise and ambiguous data, accommodating various forms of uncertainty, including fuzzy, stochastic, and neutrosophic data, alongside deterministic data. The results demonstrate that the proposed model outperforms traditional DEA models in capturing data uncertainty and providing more accurate efficiency evaluations.

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Section: Related Workmentioning

confidence: 99%

Enhancing Efficiency Assessment of Egyptian Pharmaceutical Companies through the Usage of Two-Stage Uncertainty Data Envelopment Analysis Algorithm

El-Demerdash,

Ming,

Abelrahman

2024

Preprint

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“…Cook and Kress [2] showed some models like WSR that apply an imposed set of weights may fail to obtain a correct evaluation. Cook and Kress [2] applied a data envelopment analysis (DEA) based methodology of Charnes et al [3] for measuring the preference scores without imposing any fixed weights from the outset (see [4,5,6]. Their proposed method obtained the most favorable weights for each alternative but their method very often leads to more than one alternative as efficient.…”

Section: Izadikhah and E Karapinarmentioning

confidence: 99%

Enhanced Multi-Objective Voting Data Envelopment Analysis Models with Common Set of Weights

Izadikhah

Karapınar

2020

MACO

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The important issue of the aggregation preference is how to determine the weights associated with different ranking places and DEA models play an important role in this subject. DEA models use assignments of the same aggregate value (equal to unity) to evaluate multiple alternatives as efficient. Furthermore, overly diverse weights can appear, thus, the efficiency of different alternatives obtained by different sets of weights may be unable to be compared and ranked on the same basis. In order to solve two above problems, and rank all the alternatives on the same scale, in this paper, we propose a multiple objective programming (MOP) approach for generating a common set of weights in the DEA framework. Also, we develop a novel model to make a maximum discriminating among candidates' rankings. Additionally, we present two scenarios to provide suitable strategies for solving the proposed MOP model. MSC(2010): 90B50; 90C05; 90C70.

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“…Since 1978 that CCR model was proposed by Charnes et al (1978), DEA has had a significant role in evaluating the relative efficiency of DMUs (e.g. Farzipoor Saen and Zohrehbandian, 2008;Dibachi et al, 2014Dibachi et al, , 2015Masoumzadeh et al, 2014). Simsek and T€ uys€ uz (2018) developed an integrated network DEA method with fuzzy data to measure and analyze the performance of sub-process and overall system of a cargo company.…”

Section: Introductionmentioning

confidence: 99%

How to use fuzzy screening system and data envelopment analysis for clustering sustainable suppliers? A case study in Iran

Izadikhah

Saen

Ahmadi

et al. 2020

JEIM

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PurposeThe aim of this paper is to classify suppliers into some clusters based on sustainability factors. However, there might be some unqualified suppliers and we should identify and remove those suppliers before clustering.Design/methodology/approachFirst, using fuzzy screening system, the authors identify and remove the unqualified suppliers. Then, the authors run their proposed clustering method. This paper proposes a data envelopment analysis (DEA) algorithm to cluster suppliers.FindingsThis paper presents a two-aspect DEA-based algorithm for clustering suppliers into clusters. The first aspect applied DEA to consider efficient frontiers and the second aspect applied DEA to consider inefficient frontiers. The authors examine their proposed clustering approach by a numerical example. The results confirmed that their method can cluster DMUs into clusters.Originality/valueThe main contributions of this paper are as follows: This paper develops a new clustering algorithm based on DEA models. This paper presents a new DEA model in inefficiency aspect. For the first time, the authors’ proposed algorithm uses fuzzy screening system and DEA to select suppliers. Our proposed method clusters suppliers of MPASR based on sustainability factors.

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Stochastic Multiplicative DEA Model for Measuring the Efficiency and Ranking of DMUs under VRS Technology

Cited by 13 publications

References 0 publications

Enhancing Efficiency Assessment of Egyptian Pharmaceutical Companies through the Usage of Two-Stage Uncertainty Data Envelopment Analysis Algorithm

Enhancing Efficiency Assessment of Egyptian Pharmaceutical Companies through the Usage of Two-Stage Uncertainty Data Envelopment Analysis Algorithm

Enhanced Multi-Objective Voting Data Envelopment Analysis Models with Common Set of Weights

How to use fuzzy screening system and data envelopment analysis for clustering sustainable suppliers? A case study in Iran

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