Cross-ranking of Decision Making Units in Data Envelopment Analysis

Angiz, Majid Zerafat; Mustafa, Adli; Kamali, Mohammad Jalal

doi:10.1016/j.apm.2012.02.038

Cited by 57 publications

(25 citation statements)

References 28 publications

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“…Ostensibly, the same assumptions can also be perceived in the paper by Zerafat Angiz et al, though not within an OWA context. Here, the cumulative scores

θ_{boldp boldr}

are used as coefficients of the preference aggregation model of Cook and Kress.…”

Section: Owa‐based Aggregation Proceduresmentioning

confidence: 61%

“…Preference ranking was first introduced into CE evaluation by Zerafat Angiz et al A modified version of the preference voting DEA model in the paper by Cook and Kress was proposed to find a common set of aggregation weights for the computation of the ultimate efficiency scores. Oukil and Amin proved that the model proposed in the paper by Zerafat Angiz et al may have multiple optima and use OWA aggregation as a substitute. However, it is noteworthy that both aggregation procedures assume implicitly that a zero vote for a specific rank position corresponds to an efficiency score zero.…”

Section: Introductionmentioning

confidence: 99%

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Embedding OWA under preference ranking for DEA cross-efficiency aggregation: Issues and procedures

Oukil

2018

Int J Intell Syst

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Cross‐efficiency (CE) evaluation is an extension of data envelopment analysis (DEA) used for fully ranking decision‐making units (DMUs). The ranking process is normally performed on the matrix of CE scores. An ultimate efficiency score is computed for each DMU through an adequate amalgamation process. The preference ranking approach can be seen as an amalgamation technique based on the rank orders of the CE scores. In this paper, we review this approach by putting more emphasis on the aggregation aspect. We highlight the zero vote issue and we show that the latter has been neglected in the extant aggregation procedures. Consequently, we develop two ordered weighted averaging (OWA)‐based procedures that attempt to meet effectively the requirements of an aggregation mechanism while exploiting the positive properties of the preference‐ranking approach. The merits of the proposed procedures are evaluated on a sample of manufacturing systems by considering, for OWA weights generation, different OWA models with different orness degrees.

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“…Ostensibly, the same assumptions can also be perceived in the paper by Zerafat Angiz et al, though not within an OWA context. Here, the cumulative scores

θ_{boldp boldr}

are used as coefficients of the preference aggregation model of Cook and Kress.…”

Section: Owa‐based Aggregation Proceduresmentioning

confidence: 61%

Section: Introductionmentioning

confidence: 99%

Embedding OWA under preference ranking for DEA cross-efficiency aggregation: Issues and procedures

Oukil

2018

Int J Intell Syst

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“…The usage of fuzzy concepts in handling certain crisp mathematical modeling situations has resulted in the formulation of creative and efficient procedures. This can be seen for example in the work of Zerafat Angiz et al [12] and Emrouznejad et al [13]. Motivated by these results, in this research an alternative interpretation of the CCR model using the possibility set is presented.…”

Section: Introductionmentioning

confidence: 71%

Inexact Discretionary Inputs in Data Envelopment Analysis

Langroudi

2013

Performance Measurement With Fuzzy Data Envelopment Analysis

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In this chapter, the relationship between fuzzy concepts and the efficiency score in Data envelopment analysis (DEA) is dealt with. A new DEA model for handling crisp data using fuzzy concept is proposed. In addition, the relationship between possibility sets and the efficiency score in the traditional crisp CCR model is presented. The relationship provides an alternative perspective of viewing efficiency. With the usage of the appropriate fuzzy and possibility sets to represent certain characteristics of the input data, many DEA models involving input data with various characteristics could be studied. Furthermore, based upon the proposed models, two nondiscretionary models are introduced in which some inputs or outputs, in a fuzzy sense, are inexact discretionary variables. For this purpose, a two-stage algorithm will be presented to treat the DEA model in the presence of an inexact discretionary variable. With this relationship, a new perspective of viewing and exploring other DEA models is now made possible.

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“…The key idea of CE is peer evaluation, allowing each DMU to be assessed with the weights of all the other DMUs instead of his own weights only. However, the existence of alternate optimal solutions in DEA cross‐evaluation motivated a lot of research on developing alternative secondary goal models (see, e.g., Oukil & Amin, , Oral et al, , Ramόn, Ruiz, & Sirvent, , Washio & Yamada, , Zerafat Angiz, Mustafa, & Kamali, ).…”

Section: Methodsmentioning

confidence: 99%

A systematic approach for ranking football players within an integrated DEA‐OWA framework

Oukil

Govindaluri

2017

Manage Decis Econ

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The ranking of players has always been an important input for key decisions in the football industry. This paper introduces a systematic methodology for ranking football players through objective performance factors within a framework that integrates data envelopment analysis and ordered weighted averaging operators. Players from European Premier League Football clubs are selected and ranked under a decision setting that involves the players' desirable and undesirable performance factors alongside the attitude of the decision maker. The robustness of the proposed methodology is evaluated using different levels of optimism of the decision maker. Although ranking players has received considerable attention in other sports, the problem has hardly been addressed in

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Cross-ranking of Decision Making Units in Data Envelopment Analysis

Cited by 57 publications

References 28 publications

Embedding OWA under preference ranking for DEA cross-efficiency aggregation: Issues and procedures

Embedding OWA under preference ranking for DEA cross-efficiency aggregation: Issues and procedures

Inexact Discretionary Inputs in Data Envelopment Analysis

A systematic approach for ranking football players within an integrated DEA‐OWA framework

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