Optimization of Spare Parts Varieties Based on Stochastic DEA Model

Wen, Meilin; Zu, Tianpei; Guo, Miaomiao; Kang, Rui; Yang, Yi

doi:10.1109/access.2018.2829480

Cited by 9 publications

(7 citation statements)

References 30 publications

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“…To overcome such biases, Jradi and Ruggiero (2019) extended Banker's stochastic DEA model which focussed on most likely stochastic frontier model with constrained error analysis, and under different assumptions of distribution. Wen et al (2018) developed stochastic spares optimization model (SSOM) built based on a stochastic DEA model to tackle the problem of optimization under the uncertainty of spare parts. In this problem, the stochastic parts were converted into deterministic using probability theory.…”

Section: Dea Under Uncertaintymentioning

confidence: 99%

Evaluation of Efficiency in Ontario Universities Using Data Envelopment Analysis

Ghimire¹

2022

Preprint

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Data Envelopment Analysis (DEA) is a popular operation research technique for determining the relative efficiency of non-profit organizations. The main goal of this study is to develop a unique stochastic DEA model to evaluate the efficiency of Ontario universities using some inputs and outputs. It focuses on the stochastic measure because service industries like universities are interested in qualitative outputs whose measurement through deterministic model seems non-practicable. The results of this study show that the selection of inputs and outputs plays a crucial role in determining the ranks of universities using DEA. Keywords: Data Envelopment Analysis (DEA); Universities; Canada; Stochastic models; Optimization

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Section: Dea Under Uncertaintymentioning

confidence: 99%

Evaluation of Efficiency in Ontario Universities Using Data Envelopment Analysis

Ghimire¹

2022

Preprint

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“…There are already different modeling strategies for stochastic programming (SP) proposed in the literature, including the E-model, V-model, P-model, minimax-methods [18] and stochastic DEA model [19]. Since in most MOP approaches the analytical models f i (x) are usually obtained through Response Surface Methodology (RSM) [9], the SP method selected in the present study is specific for RSM.…”

Section: B Stochastic Programming In Mopmentioning

confidence: 99%

Stochastic Optimization of AISI 52100 Hard Turning With Six Sigma Capability Constraint

et al. 2019

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Hard turning optimization problems are usually approached using response surface methodology. By running designed experiments, researchers build analytical models to represent the outputs under interest. However, most studies focus on the expected values of the outputs, and only a few consider the variances of the models, even though there are several stochastic programming (SP) techniques available in the literature. Such variances may have a significant impact on the problem solution. This paper aims to optimize the AISI 52100 hardened steel turning process using SP. The decision variables are cutting speed, feed rate, and depth of cut, outputs have cost per part and material removal rate, and average surface roughness six sigma capability is modeled as a stochastic constraint. The SP method is also compared with the conventional one, which did not include the variances into the problem. The results show that taking the variability of the models into account is necessary to obtain a satisfactory process capability and to analyze different scenarios. Finally, this paper shows that competitive results can be achieved by simplifying the problem formulation.INDEX TERMS Capability engineering optimization methods, response surface methodology, stochastic processes.

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“…21 A stochastic DEA method has been employed to address the problem of optimization of spare parts varieties. 22 Kao et al developed a stochastic model to deal with stochastic efficiency by considering the correlation between the input and output factors and the multivariate normal distribution. 23 A multi-objective probabilistic linear fractional programming problem has been presented where the parameters of the model followed the Cauchy distribution.…”

Section: Introductionmentioning

confidence: 99%

“…discussed about a portfolio selection problem in mean–variance framework in which the security returns are uncertain variables 21 . A stochastic DEA method has been employed to address the problem of optimization of spare parts varieties 22 . Kao et al .…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of investment with DEA models under pure jump processes

Zoghi

Sanei

Tohidi

et al. 2022

Concurrency and Computation

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Summary Portfolio optimization is a selection of the best combination of financial assets that maximizes the return and minimizes the risk. As the stock return distributions exhibit skewness, kurtosis, and heavy‐tailedness, for asset performance evaluation through the data envelopment analysis (DEA), we assume that the asset returns follow a pure jump Lévy process. For this purpose, two risk measures are provided as value‐at‐risk (VaR) and conditional VaR (CVaR). To deal with negative values, the range directional measure (RDM) model is utilized in the appropriate Lévy process, and inefficiency measurements are compared. Moreover, the asset price jumps have significant effects on the input and output of DEA models, risk measures, mean returns, and efficiency scores. In this regard, there are two optimization problems integrating DEA based on the variance gamma (VG) Lévy process, in which the input and output are stochastic. One of these two problems is a multi‐objective optimization model that evaluates more reliable efficiency scores than the other models. For this purpose, the VG parameters are estimated through the moments estimation method in order to analyze seven companies listed in the Tehran stock exchange (TSE). Finally, the Kolmogorov–Smirnov goodness‐of‐fit test is conducted. The results indicate that the VG model fits properly and is well suited to assets returns.

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Optimization of Spare Parts Varieties Based on Stochastic DEA Model

Cited by 9 publications

References 30 publications

Evaluation of Efficiency in Ontario Universities Using Data Envelopment Analysis

Evaluation of Efficiency in Ontario Universities Using Data Envelopment Analysis

Stochastic Optimization of AISI 52100 Hard Turning With Six Sigma Capability Constraint

Performance evaluation of investment with DEA models under pure jump processes

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