Network, Shared Flow and Multi-level DEA Models: A Critical Review

Castelli, Lorenzo; Pesenti, Raffaele

doi:10.1007/978-1-4899-8068-7_15

Cited by 11 publications

(7 citation statements)

References 188 publications

(195 reference statements)

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“…The network system in practice includes the series, parallel and mixed (general) structures such as electric power companies, hospitals and broadcasting companies [17]. The popularity and abundance of network DEA models encouraged Castelli et al [18], Cook et al [19], and Agrell and Hatami-Marbini [20], Kao [21] and Castelli and Pesenti [22] to provide the overarching surveys of the advanced applications and models. Kao [21] reviewed studies on network DEA and proposed two distinct classifications according to the structure of the network systems and the developed models.…”

Section: Network Deamentioning

confidence: 99%

“…Focusing on the structures, the second classification is composed of seven categories: basic two-stage, general two-stage, series, parallel, mixed, hierarchical, and dynamic models. Castelli and Pesenti [22] as an extension of Castelli et al [18] provided a classification of the existing network DEA models and their applications to different industries as presented in Figure 1.…”

Section: Network Deamentioning

confidence: 99%

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Measuring performance with common weights: network DEA

Hatami-Marbini

Saati

2019

Neural Comput & Applic

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In conventional data envelopment analysis (DEA), a production system has been seen as a black box for measuring the efficiency without any attention to what is happening inside the system. However, in practice, performance improvement often requires observing the internal structure of the producing system in order to find the sources of inefficiencies. In addition, weight flexibility as a key property of the multiplier DEA models allows a system to totally disregard an assessment factor, either input or output, from the evaluation process by assigning a value of zero or epsilon to its weight. This paper contributes to the existing literature by proposing a common-weights DEA model when the production system includes a number of interrelated processes. To this end, we propose an aggregate DEA model to calculate the most favourable common weights for determining the efficiency of all production systems and their processes at the same time. Our proposed aggregate model is not only linear for equitably evaluating the producing units on the same scale, but also enables us to deal with the mixed network structures. Furthermore, the network system is decomposed into a series system to build a relational network DEA model that emphasises separate relatedness. This greatly reduces the computational complexities for enormous volumes of data in many real applications and treat difficulties in network DEA models including the zero-value and fluctuating weights and multiple solutions. Managerially speaking, this paper aims to provide top management teams of a production system with an integrated framework to shape a better strategic decision process coupled with superior firm performance, which is to determine the sources of inefficiencies and ultimately take corrective actions over the long run. The proposed framework helps top managers make proper decisions in complex situations with a view of improving a firm's efficiency in all production divisions, which can be identified as a core competency leading to competitive advantages. In a context of performance management, our study is equipped with a simple numerical example and a case study of the non-life insurance companies to demonstrate the applicability of the proposed common-weights network model.

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Section: Network Deamentioning

confidence: 99%

Section: Network Deamentioning

confidence: 99%

Measuring performance with common weights: network DEA

Hatami-Marbini

Saati

2019

Neural Comput & Applic

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“…We show how to build a NDEA model for a production process with serie structure as those in Figure 1. Following ( (Castelli, Pesenti, & Ukovich, 2010), (Castelli & Pesenti, 2014), (Castelli, Pesenti, & Ukovicha, 2001)) we build a multiplicative input oriented relational (with two relational variables, in the Figure 1) NDEA model (Kao, 2009(a))with shared variables ( and in Figure 1) and one exogenous variables ( ) (see Note 5). NDEA relational model in (Kao, 2009(a)) use the same weights for all variables in the NDEA model.…”

Section: An Ndea Relational Model For a Two Stages Production Processmentioning

confidence: 99%

An NDEA Model as Policy Tool to Support Managerial Decisions

Pinto¹

2020

IJBA

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Data Envelopment Analysis (DEA) is a non-parametric frontier approach used both to model production processes and/or production organisations of goods and services (public and private) as inputs/output systems and to measure their relative efficiency. However, in addition to being an instrument for measuring economic performances, the DEA is also used in its multiplicative version as a policy tool to support managerial decisions for the pursuit of competing objectives. Based on the data, the DEA offers an answer to the pursuit of competing objectives by placing it as a trade-off and calculating the optimal weights associated with each of them. Here, we will address two questions: 1) how to overcome the DEA modelling of decision-making units as "black boxes" that use inputs to be translated into outputs to taking into account the operations/stages involved in this transformation process, and 2) how to use the Network Data Envelopment Analysis (NDEA) approach as a policy tool. In particular, we will propose a way to use a relational NDEA model as a policy tool by exploiting the possibility of making assumptions about the model variables. In our opinion, compared to the standard DEA, the advantage of using the NDEA as a policy tool is that the policy objectives (in this case organisational) can also be disaggregated at the sub-process level. In particular, we will propose to translate the system of organisational objectives into an NDEA model as a mix of "discretionary/non-discretionary" assumptions about the variables of the model itself. To clarify our proposal, we will then develop an application in the public health services sector.

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“…Phase 2(1992-2011):sustainable development Phase 3(2012-):Construction of ecological civilization and the relative efficiency is evaluated. The difference between the frontier side of ecological efficiency and DMU dominates the performance of ecological civilization policy (Castelli & Pesenti, 2014). Environmental capacity refers to the maximum contaminant capacity which does not significantly harm the ecological system (Luo, 2012).…”

Section: Relationship Between Economic Development and Environmental Prmentioning

confidence: 99%

Performance Evaluation Research of Ecological Civilization Policy Based on Data Envelopment Analysis and Artificial Neural Networks Model

2018

PPAR

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Ecological civilization is a new form of human civilization that solves the contradiction between development and environment. Because the ecological civilization policy system has is complex and nonlinear, this paper combines Artificial Neural Network technology with the Data Envelopment Analysis(DEA) method and proposes the hybrid DEA-Artificial Neural Network (DEANN) model to estimate the impact of ecological civilization policy on economic development. The model uses regional eco-efficiency evaluated by DEA as one of the inputs for the neural network; the neural network integrates the total input and output results to provide a quantitative estimate of the ecological civilization policy in different provincial regions from 2003-2016. By training and examining the evaluation results with the DEANN model, this paper tries to predict the impact of 13th Five-Year Total Emission Reduction Policy on economic development. The empirical research results and suggestions can improve the ecological civilization policy performance by negative feedback mechanisms.

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Network, Shared Flow and Multi-level DEA Models: A Critical Review

Cited by 11 publications

References 188 publications

Measuring performance with common weights: network DEA

Measuring performance with common weights: network DEA

An NDEA Model as Policy Tool to Support Managerial Decisions

Performance Evaluation Research of Ecological Civilization Policy Based on Data Envelopment Analysis and Artificial Neural Networks Model

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