A new robust DEA model and super-efficiency measure

Arabmaldar, Aliasghar; Jablonský, Josef; Saljooghi, Faranak Hosseinzadeh

doi:10.1080/02331934.2017.1295047

Cited by 31 publications

(9 citation statements)

References 28 publications

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“…DD: Describe the underachievement of portfolio from the market index. DD is calculated (34), where E(R P ), R f , and σ(R P ) denote on average portfolio return, risk-free return rate, and standard deviation of portfolio return. Please note that higher values of SHR are desirable.…”

Section: Plos Onementioning

confidence: 99%

A novel two-phase robust portfolio selection and optimization approach under uncertainty: A case study of Tehran stock exchange

et al. 2020

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Portfolio construction is one of the most critical problems in financial markets. In this paper, a new two-phase robust portfolio selection and optimization approach is proposed to deal with the uncertainty of the data, increasing the robustness of investment process against uncertainty, decreasing computational complexity, and comprehensive assessments of stocks from different financial aspects and criteria are provided. In the first phase of this approach, all candidate stocks' efficiency is measured using a robust data envelopment analysis (RDEA) method. Then in the second phase, by applying robust mean-semi variance-liquidity (RMSVL) and robust mean-absolute deviation-liquidity (RMADL) models, the amount of investment in each qualified stock is determined. Finally, the proposed approach is implemented in a real case study of the Tehran stock exchange (TSE). Additionally, a sensitivity analysis of all robust models of this study is examined. Illustrative results show that the proposed approach is effective for portfolio selection and optimization in the presence of uncertain data.

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Section: Plos Onementioning

confidence: 99%

A novel two-phase robust portfolio selection and optimization approach under uncertainty: A case study of Tehran stock exchange

et al. 2020

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“…Robust DEA (RDEA) is another technique that enables DEA to handle imprecise data. RDEA has recently received increased attention from researchers (see, e.g., [50][51][52] ). It is an optimization technique to achieve reliability in the DEA outcomes by considering data perturbation [53] .…”

Section: Imprecise Dea Modelsmentioning

confidence: 99%

Reliable estimation of suppliers’ total cost of ownership: An imprecise data envelopment analysis model with common weights

Shabani

Visani

Barbieri

et al. 2019

Omega

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Total cost of ownership (TCO) is a management accounting technique that evaluates the total cost of a business partnership using a time-consuming activity-based costing procedure. Studies have suggested that TCO-based data envelopment analysis (DEA) can effectively estimate the results of TCO with substantially less effort and time; however, its adoption in practice is limited due to certain shortcomings. First, managers struggle to understand and accept the uncommon weighting schemes of existing TCObased DEA models because traditional TCO analyses require a common set of weights. Second, both the traditional TCO approach and TCO-based DEA models are designed to handle precise data, whereas TCO analyses often involve imprecise data from conflicting data sources and estimations. To address the managerial and technical issues of handling weighting schemes and imprecise data, this paper proposes a novel TCO-based model: common set of weights imprecise DEA (CSW-IDEA). We validate the proposed methodology using real-life datasets from 175 suppliers that serve five key components to two multinational mechanical manufacturers. For both precise data and imprecise data, the proposed CSW-IDEA reliably approximates traditional TCO calculations significantly better than existing TCO-based DEA. The cost savings that can be theoretically generated by applying the CSW-IDEA approach are similar to the cost savings estimated by the traditional TCO approach.

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“…It also contains the protection function j∈G t p tj + k t z t with robustness parameter k t to the uncertain cost, which is bounded for each combination of decision variables and goals via Constraint (15). Constraints (17) and (18) impose nonnegativity on all decision variables. The model involves t + t and = − ⌊ ⌋, is the remainder value when is non-integer.…”

Section: Kuchta's Rgp Approachmentioning

confidence: 99%

“…Although the RO was born as an approach to optimization problems with a single objective [15], it has been extended in the literature to address MCDM problems, for example, the multiobjective portfolio optimization [2,16] and the data envelopment analysis [14,17,18]. One growing area of research in this field is the robust goal programming (RGP), which combines the RO with the GP technique.…”

Section: Introductionmentioning

confidence: 99%

Light Robust Goal Programming

Mensah

Rocca

2019

MCA

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Robust goal programming (RGP) is an emerging field of research in decision-making problems with multiple conflicting objectives and uncertain parameters. RGP combines robust optimization (RO) with variants of goal programming techniques to achieve stable and reliable goals for previously unspecified aspiration levels of the decision-maker. The RGP model proposed in Kuchta (2004) and recently advanced in Hanks, Weir, and Lunday (2017) uses classical robust methods. The drawback of these methods is that they can produce optimal values far from the optimal value of the “nominal” problem. As a proposal for overcoming the aforementioned drawback, we propose light RGP models generalized for the budget of uncertainty and ellipsoidal uncertainty sets in the framework discussed in Schöbel (2014) and compare them with the previous RGP models. Conclusions regarding the use of different uncertainty sets for the light RGP are made. Most importantly, we discuss that the total goal deviations of the decision-maker are very much dependent on the threshold set rather than the type of uncertainty set used.

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A new robust DEA model and super-efficiency measure

Cited by 31 publications

References 28 publications

A novel two-phase robust portfolio selection and optimization approach under uncertainty: A case study of Tehran stock exchange

A novel two-phase robust portfolio selection and optimization approach under uncertainty: A case study of Tehran stock exchange

Reliable estimation of suppliers’ total cost of ownership: An imprecise data envelopment analysis model with common weights

Light Robust Goal Programming

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