A Discriminative Multi-Objective Programming Method for Solving Network DEA

Kao, Han-Ying; Chan, Chieh-Yu

doi:10.1007/978-3-642-35452-6_34

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…Moreover, for our context, when relying on this strategy we end with interesting relationships between the indexes and their decomposition (Table 1). Alternative methods include multi-objective programming (see, for example, Zimmermann (1978), Charnes et al (1989), Kao and Chan (2013), Tohidi and Razavyan (2013), Despotis et al (2016)), the law of one price (Kuosmanen et al (2006), and Fang and Li (2015)), or industry-based approaches (Li and Ng (1995), Ylvinger (2000), Zelenyuk (2003, 2007), Walheer (2016a, b), Zelenyuk (2006Zelenyuk ( , 2016, and Färe and Karagiannis (2017)). These approaches make extra assumptions on either the technologies or the input prices, or are more complex (e.g.…”

Section: Observation Of the Group-specific And Global Input Pricesmentioning

confidence: 99%

Global Malmquist and cost Malmquist indexes for group comparison

Walheer

2022

J Prod Anal

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The Malmquist index (MI) has demonstrated its usefulness in comparing the performances of Decision Making Units (DMUs) performances. The global MI (GMI) has been suggested as a means to overcome three drawbacks of the MI: noncircularity, disparate measurements, and infeasibility. Recently, it has appeared that the MI can also be used to compare groups of DMUs. While this new function of the index has also increased its usefulness, it presents the same drawbacks as the MI. In this paper, we define the global counterpart of the MI for group contexts. We also consider the case where DMUs have an economic optimization behavior by proposing a global cost MI (GCMI). The GCMI requires the observation of the input prices. As it may represent a strong assumption, we propose solutions. These two novel indexes equip the practitioners with a new toolkit. We illustrate the usefulness of our new indexes with the Chinese energy sector.

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Section: Observation Of the Group-specific And Global Input Pricesmentioning

confidence: 99%

Global Malmquist and cost Malmquist indexes for group comparison

Walheer

2022

J Prod Anal

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“…Most recently, Kao and Chan (2013) have introduced a multi-objective programming method that computes both the overall efficiencies of the DMUs and the divisional efficiencies of the DMSUs of network DEA models.…”

Section: Simultaneous Evaluation Of Dmu and Dmsu Efficienciesmentioning

confidence: 99%

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

Castelli

Pesenti

2014

International Series in Operations Research &Amp; Management Science

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In the last two decades, complex and detailed DEA models that consider the internal structure of DMUs have been proposed by several authors. This chapter describes the mathematical formulations, along with their main variants, extensions and applications, of three large and popular model families: network (with special emphasis on multi-stage), shared flow (also known as multi-component or multi-activity), and multi-level models. Each family is a different generalization of the same elementary internal structure. This review extends and updates the classification presented in Castelli et al. (Ann Oper Res 173(1):207–235, 2010)

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Efficiency decomposition in network data envelopment analysis with slacks-based measures

Kao

2014

Omega

118

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A Discriminative Multi-Objective Programming Method for Solving Network DEA

Cited by 5 publications

References 9 publications

Global Malmquist and cost Malmquist indexes for group comparison

Global Malmquist and cost Malmquist indexes for group comparison

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

Efficiency decomposition in network data envelopment analysis with slacks-based measures

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