A robust cross-efficiency data envelopment analysis model with undesirable outputs

Tavana, Madjid; Toloo, Mehdi; Aghayi, Nazila; Arabmaldar, Aliasghar

doi:10.1016/j.eswa.2020.114117

Cited by 29 publications

(9 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Robust WDEA (RWDEA): Presenting RWDEA approach using robust optimization for dynamic performance assessment of DMUs under deep uncertainty and scenario-based data. In other words, uncertainty based RWDEA and scenario-based RWDEA models can be proposed for dealing with continuous and discrete uncertainty of panel data, respectively (for details, see Peykani, Mohammadi, Farzipoor Saen, et al, 2020;Peykani, Mohammadi, Jabbarzadeh, et al, 2020;Sadjadi & Omrani, 2008;Tavana et al, 2021;Zahedi-Seresht et al, 2017 Y. Chen et al, 2009;Cook & Zhu, 2014;Kao, 2014Kao, , 2017Kao & Hwang, 2008;Koronakos et al, 2019;Koronakos, 2019).…”

Section: Conclusion and Future Research Directionsmentioning

confidence: 99%

Window data envelopment analysis approach: A review and bibliometric analysis

et al. 2021

View full text Add to dashboard Cite

Window Data Envelopment Analysis (WDEA) is a popular, effective, and applicable methods for dynamic performance assessment of peer decision making units (DMUs). WDEA is a non-parametric panel method that operates based on the principle of moving averages and establishes efficiency measures by treating each DMU in different periods as a separate DMU. By applying the WDEA approach, a decisionmaker (DM) can measure the efficiency of different DMUs in different periods through a sequence of overlapping windows. Also, WDEA can increase the discrimination power by increasing the number of DMUs when a limited number of DMUs is available. Given the advantages of the WDEA approach and its applications in realworld problems, this paper surveys and analyses 387 WDEA papers published from 1985 to 2020. The paper also recommends some suggestions, guidelines, and opportunities for future research. Notably, the findings show the applicability and efficacy of WDEA in the literature.

show abstract

Section: Conclusion and Future Research Directionsmentioning

confidence: 99%

Window data envelopment analysis approach: A review and bibliometric analysis

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Constraint (26) imposes that bus b can serve the local route (i, j) only if it is dispatched to station i or the route (i − 1, i) is accessible. Constraint (27) is the initial condition. Constraint (13) certifies that the maximum capacity of each vehicle on a local route is not exceeded.…”

Section: Extensionsmentioning

confidence: 99%

Bus Bridging for Rail Disruptions: A Distributionally Robust Fuzzy Optimization Approach

Yang

et al. 2023

IEEE Trans. Fuzzy Syst.

View full text Add to dashboard Cite

Dealing with uncertain rail disruptions effectively raises a significant challenge for computational intelligence research. This paper studies the bus bridging problem under demand uncertainty, where the passenger demand is represented as parametric interval-valued fuzzy variables and their associated uncertainty distribution sets. A distributionally robust fuzzy optimization model is proposed to minimize the maximum travel time and to search for the optimal scheme for vehicle allocation, route selection, and frequency determination. To solve the proposed robust model, we discuss the computational issues concerning credibilistic constraints, turning the robust counterpart model into computationally tractable equivalent formulations. The proposed approach is verified, and the resulting method is validated with a report on uncertain parameters in a real-world disrupted event of Shanghai Rail Line 1. Experiment results show that the distributionally robust fuzzy optimization approach can provide a better uncertainty-immunized solution.

show abstract

“…Together with the widespread use of the RO approach in diverse fields, several studies have been carried out to tackle the problems of uncertainty in DEA (see e.g. Shokouhi et al 2010 , 2014 ; Sadjadi et al 2011 ; Omrani 2013 ; Lu 2015 ; Arabmaldar et al 2017 ; Tavana et al 2021 ; Arabmaldar et al 2021 ; Hatami-Marbini and Arabmaldar 2021 ;). This strand of DEA models is generally based on the three RO approaches proposed by Mulvey et al ( 1995 ), Ben-Tal and Nemirovski ( 2000 ) and Bertsimas and Sim ( 2004 ).…”

Section: Uncertain Data In the Malmquist Indexmentioning

confidence: 99%

Robust productivity growth and efficiency measurement with undesirable outputs: evidence from the oil industry

2022

Self Cite

View full text Add to dashboard Cite

This paper aims to contribute to the contemporary and imperative research on the performance and productivity growth of the oil industry. Among cutting edge methods, frontier analysis is a successful approach that has been widely used to assess the efficiency and productivity of entities with multiple resources and multiple outputs. This study first develops a unique framework based upon data envelopment analysis (DEA) to measure efficiency and productivity in the way that it tackles the uncertainty in data and undesirable outputs and, in turn, provides useful information to decision-makers. An adaptive robust optimisation is utilised to combat uncertain data whose distributions are unknown and consider the nexus between the level of conservatism and decision-makers’ risk preference. The key advantage of the proposed robust DEA approach is that the results remain relatively unchanged when uncertain conditions exist in the problem. An empirical study on the oil refinery is presented in situations of data uncertainty along with considering CO 2 emissions as the undesirable output to conduct environmental efficiency and productivity analysis of the 25 countries over the period 2000–2018. The empirical results obtained from the proposed approach give some imperative implications. First, results show that the price of robustness does not affect identically for varying technologies when assessing productivity in a global oil market, and the USA oil industry is observed as the highest productivity growth in all cases confirming its efforts for the rapid rise in oil extraction and production at low costs. There may be practical lessons for other nations to learn from the USA oil industry to improve productivity. Findings also support a considerable regress during the 2008 Global Financial Crisis in the oil industry compared to the rest of the periods in question, and due to monetary and fiscal stimulus, there is a sharp productivity growth from 2009 to 2011. The other implication that can be drawn is that the GDP growth rate and technology innovation can more effectively improve the productivity of the oil industry across the globe. Supplementary Information The online version contains supplementary material available at 10.1007/s00291-022-00683-y.

show abstract

A robust cross-efficiency data envelopment analysis model with undesirable outputs

Cited by 29 publications

References 57 publications

Window data envelopment analysis approach: A review and bibliometric analysis

Window data envelopment analysis approach: A review and bibliometric analysis

Bus Bridging for Rail Disruptions: A Distributionally Robust Fuzzy Optimization Approach

Robust productivity growth and efficiency measurement with undesirable outputs: evidence from the oil industry

Contact Info

Product

Resources

About