Suitability and redundancy of non-homogeneous weight restrictions for measuring the relative efficiency in DEA

Podinovski, Victor V.

doi:10.1016/s0377-2217(03)00176-0

Cited by 39 publications

(16 citation statements)

References 34 publications

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“…Non-homogeneous weight restrictions can also be related to production trade-offs in the envelopment model, but formula (6) A further difficulty arising in DEA models with non-homogeneous weight restrictions is that the managerial meaning of the resulting efficiency obtained via the multiplier DEA model may be unclear. In particular, the optimal input and output weights in the resulting models do not generally represent the assessed DMU in the best light compared to the other DMUs (Podinovski and Athanassopoulos 1998;Podinovski 1999;2004b).…”

Section: Vrsmentioning

confidence: 99%

DEA Models with Production Trade-offs and Weight Restrictions

Podinovski

2015

International Series in Operations Research &Amp; Management Science

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There is a large literature on the use of weight restrictions in multiplier DEA models. In this chapter we provide an alternative view of this subject from the perspective of dual envelopment DEA models in which weight restrictions can be interpreted as production trade-offs. The notion of production trade-offs allows us to state assumptions that certain simultaneous changes to the inputs and outputs are technologically possible in the production process. The incorporation of production trade-offs in the envelopment DEA model, or the corresponding weight restrictions in the multiplier model, leads to a meaningful expansion of the model of production technology. The efficiency measures in DEA models with production trade-offs retain their traditional meaning as the ultimate and technologically realistic improvement factors. This overcomes one of the known drawbacks of weight restrictions assessed using other methods. In this chapter we discuss the assessment of production trade-offs, provide the corresponding theoretical developments and suggest computational methods suitable for the solution of the resulting DEA models.

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Section: Vrsmentioning

confidence: 99%

DEA Models with Production Trade-offs and Weight Restrictions

Podinovski

2015

International Series in Operations Research &Amp; Management Science

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“…In addition, adding the constraint n j=1 w j = 1 to the DEA-based GRA model is not recommended here. In fact, the sum-to-one constraint is a non-homogeneous constraint (i.e., its right-hand side is a non-zero free constant) which can lead to underestimation of the grey relational grades of alternatives or infeasibility in the DEA-based GRA model (see [18]). …”

Section: Dea-based Gra Modelsmentioning

confidence: 99%

Multiple attribute grey relational analysis using DEA and AHP

Pakkar

2016

Complex Intell. Syst.

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This research proposes an integrated data envelopment analysis (DEA) and analytic hierarchy process (AHP) approach to obtain attribute weights in a grey relational analysis (GRA) method. First, this can be implemented by developing a DEA-based GRA model to obtain attribute weights for the alternative under assessment. Second, weight bounds, using AHP, can be incorporated in the DEA-based GRA model to reflect the priority weights of attributes. Third, the effects of incorporating weight bounds on attribute weights can be analyzed by developing a parametric distance model. Increasing the value of a parameter in a domain of grey relational loss, i.e., a reduction in grey relational grade, we explore the tradeoff relationship between the grey relational grade and the priority weights of attributes for each alternative. This may result in various ranking positions for each alternative in comparison with the other alternatives. An illustrated example of selecting dispatching rules is also presented to highlight the usefulness of the proposed approach.

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“…The scaling factor α is added to avoid the possibility of contradicting constraints leading to infeasibility or underestimating the relative composite scores of DMUs [58]. The optimal solution to model (10) produces a set of weights for indicators that are used to compute the performance of DMUs.…”

Section: Prioritizing Indicator Weights Using Ahpmentioning

confidence: 99%

A Hierarchical Aggregation Approach for Indicators Based on Data Envelopment Analysis and Analytic Hierarchy Process

Pakkar

2016

Systems

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This research proposes a hierarchical aggregation approach using Data Envelopment Analysis (DEA) and Analytic Hierarchy Process (AHP) for indicators. The core logic of the proposed approach is to reflect the hierarchical structures of indicators and their relative priorities in constructing composite indicators (CIs), simultaneously. Under hierarchical structures, the indicators of similar characteristics can be grouped into sub-categories and further into categories. According to this approach, we define a domain of composite losses, i.e., a reduction in CI values, based on two sets of weights. The first set represents the weights of indicators for each Decision Making Unit (DMU) with the minimal composite loss, and the second set represents the weights of indicators bounded by AHP with the maximal composite loss. Using a parametric distance model, we explore various ranking positions for DMUs while the indicator weights obtained from a three-level DEA-based CI model shift towards the corresponding weights bounded by AHP. An illustrative example of road safety performance indicators (SPIs) for a set of European countries highlights the usefulness of the proposed approach.

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Suitability and redundancy of non-homogeneous weight restrictions for measuring the relative efficiency in DEA

Cited by 39 publications

References 34 publications

DEA Models with Production Trade-offs and Weight Restrictions

DEA Models with Production Trade-offs and Weight Restrictions

Multiple attribute grey relational analysis using DEA and AHP

A Hierarchical Aggregation Approach for Indicators Based on Data Envelopment Analysis and Analytic Hierarchy Process

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