Data envelopment analysis and robust optimization: A review

Peykani, Pejman; Mohammadi, Emran; Saen, Reza Farzipoor; Sadjadi, Seyed Jafar; Rostamy-Malkhalifeh, Mohsen

doi:10.1111/exsy.12534

Cited by 71 publications

(37 citation statements)

References 94 publications

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“…An optimization model was proposed in Shahhosseini et al 60 , which accounts for the tradeoff between bias and variance of the predictions, as it uses mean squared error (MSE) to form the objective function for the optimization problem 68 . In addition, out-of-bag predictions generated by -fold cross-validation are used as emulators of unseen test observations to create the input matrices of the optimization problem, which are out-of-bag predictions made by each base learner.…”

Section: Methodsmentioning

confidence: 99%

Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt

Shahhosseini

Huber

et al. 2021

Sci Rep

216

100

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This study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt. The main objectives are to explore whether a hybrid approach (crop modeling + ML) would result in better predictions, investigate which combinations of hybrid models provide the most accurate predictions, and determine the features from the crop modeling that are most effective to be integrated with ML for corn yield prediction. Five ML models (linear regression, LASSO, LightGBM, random forest, and XGBoost) and six ensemble models have been designed to address the research question. The results suggest that adding simulation crop model variables (APSIM) as input features to ML models can decrease yield prediction root mean squared error (RMSE) from 7 to 20%. Furthermore, we investigated partial inclusion of APSIM features in the ML prediction models and we found soil moisture related APSIM variables are most influential on the ML predictions followed by crop-related and phenology-related variables. Finally, based on feature importance measure, it has been observed that simulated APSIM average drought stress and average water table depth during the growing season are the most important APSIM inputs to ML. This result indicates that weather information alone is not sufficient and ML models need more hydrological inputs to make improved yield predictions.

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Section: Methodsmentioning

confidence: 99%

Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt

Shahhosseini

Huber

et al. 2021

Sci Rep

216

100

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“…The compact form of CCR-OO model is as Model (15). If uy 0 = 1 become to uy 0 � 1, the optimal solution does not change.…”

Section: Plos Onementioning

confidence: 99%

“…Especially when the efficiencies of units are close, it is essential to develop a procedure and models for ranking the stocks and, consequently, decision-making about weights of the stocks in the portfolio that is capable of being employed under uncertainty. Robust optimization (RO) methodology is one of the popular methods that can be used to deal with uncertainty [ 13 – 15 ].…”

Section: Introductionmentioning

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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“…Different strategies have been presented in the literature to deal with these inaccurate and ambiguous data. According to Peykani et al (2020) studies, the uncertain theory-based approaches in the UDEA could be categorized into the following five classes:…”

Section: Introductionmentioning

confidence: 99%

“…As a novel strategy in the UDEA literature, RDEA is based on the RO method, an appropriate and common approach employed to deal with discrete and continuous optimization problems. This strategy does not require notable historical data and probability distribution function and ensures the solution feasibility for all feasible amounts of uncertain variables in the considered convex uncertainty set (Peykani et al (2020)). Stochastic DEA (SDEA) is the last UDEA method utilized for the performance evaluation of DMUs under uncertain data.…”

Section: Introductionmentioning

confidence: 99%

Inverse data envelopment analysis with stochastic data

et al. 2021

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The inverse Data Envelopment Analysis (InvDEA) is an exciting and significant topic in the DEA area. Also, uncertain data in various real-life applications can degrade the efficiency results. The current work addresses the InvDEA in the presence of stochastic data. Under maintaining the efficiency score, the inputs/outputs-estimation problem is investigated when some or all of its outputs/inputs increase. A novel optimality concept for multiple-objective programming problems, stochastic (weak) Pareto optimality in the level of significance [[EQUATION]] , is introduced to derive necessary and sufficient conditions for input/output estimation. Furthermore, the performance of the developed theory in a banking sector application is verified.

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Data envelopment analysis and robust optimization: A review

Cited by 71 publications

References 94 publications

Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt

Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt

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

Inverse data envelopment analysis with stochastic data

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