Robust ranking and selection with optimal computing budget allocation

Gao, Siyang; Xiao, Hui; Zhou, Enlu; Chen, Weiwei

doi:10.1016/j.automatica.2017.03.019

Cited by 51 publications

(17 citation statements)

References 18 publications

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“…In this research, the robust ranking technique, employed in many papers [14][15][16][17] for easier and faster ordering of results, is used to rank the information obtained from experts.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Evaluation of the green supply chain management practices: A novel neutrosophic approach

Abdel-Baset

Chang

Gamal

2019

Computers in Industry

120

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A B S T R A C TTo attain competitive advantages and promote environmental performance, a proactive approach called green supply chain management (GSCM), has been extensively employed. In this paper, we use the robust ranking technique with neutrosophic set to handle practices and performances in GSCM. We evaluate GSCM practices using the robust ranking technique in order to detect practices leading to better economic and environmental performances. We employ the neutrosophic set theory to handle vague data, imprecise knowledge, incomplete information and linguistic imprecision. The efficiency of the proposed method is evaluated by using the first case study from petroleum industry in Egypt and the second case study from manufacturing firm in China. The results display that "reverse logistics", "supplier environmental collaboration", "carbon management" are the significant in GSCM practices. Both case studies verified that our proposal could be adopted for effectiveness and improvement. Our work could help managers and decision makers to become more environmentally responsible.

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“…In this research, the robust ranking technique, employed in many papers [14][15][16][17] for easier and faster ordering of results, is used to rank the information obtained from experts.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Evaluation of the green supply chain management practices: A novel neutrosophic approach

Abdel-Baset

Chang

Gamal

2019

Computers in Industry

120

View full text Add to dashboard Cite

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“…By doing so, the problem scale is reduced from km to k + m − 1, and thus the computing efficiency has the potential to be substantially improved. This observation is consistent with the selection rules for unconstrained robust R&S (Fan et al, in press;Gao, Xiao, et al, 2017).…”

Section: Asymptotic Optimality Conditionsupporting

confidence: 89%

“…Following this direction, Corlu and Biller () developed a robust R&S procedure for subset selection. Fan, Hong, and Zhang (in press) and Gao, Xiao, Zhou, and Chen () selected the best design with respect to the worst‐case choices among a finite collection of possible input models using the IZ and OCBA approaches respectively. However, none of these developments address the input uncertainty for R&S under stochastic constraints.…”

Section: Introductionmentioning

confidence: 99%

A worst‐case formulation for constrained ranking and selection with input uncertainty

Shi

Gao

Xiao

et al. 2019

Naval Research Logistics

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In this research, we consider robust simulation optimization with stochastic constraints. In particular, we focus on the ranking and selection problem in which the computing time is sufficient to evaluate all the designs (solutions) under consideration. Given a fixed simulation budget, we aim at maximizing the probability of correct selection (PCS) for the best feasible design, where the objective and constraint measures are assessed by their worst‐case performances. To simplify the complexity of PCS, we develop an approximated probability measure and derive the asymptotic optimality condition (optimality condition as the simulation budget goes to infinity) of the resulting problem. A sequential selection procedure is then designed within the optimal computing budget allocation framework. The high efficiency of the proposed procedure is tested via a number of numerical examples. In addition, we provide some useful insights into the efficiency of a budget allocation procedure.

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“…Although these studies are also based on partitioning or metamodeling, they do not aim to select the top m designs, and are therefore different in objectives from this research. Other variants of OCBA include selecting the best design considering resource sharing and allocation (Peng et al, 2013) and input uncertainty (Gao et al, 2017b).…”

Section: Introductionmentioning

confidence: 99%

Efficient simulation budget allocation for subset selection using regression metamodels

et al. 2019

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This research considers the ranking and selection (R&S) problem of selecting the optimal subset from a finite set of alternative designs. Given the total simulation budget constraint, we aim to maximize the probability of correctly selecting the top-m designs. In order to improve the selection efficiency, we incorporate the information from across the domain into regression metamodels. In this research, we assume that the mean performance of each design is approximately quadratic. To achieve a better fit of this model, we divide the solution space into adjacent partitions such that the quadratic assumption can be satisfied within each partition. Using the large deviation theory, we propose an approximately optimal simulation budget allocation rule in the presence of partitioned domains. Numerical experiments demonstrate that our approach can enhance the simulation efficiency significantly.

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Robust ranking and selection with optimal computing budget allocation

Cited by 51 publications

References 18 publications

RETRACTED: Evaluation of the green supply chain management practices: A novel neutrosophic approach

RETRACTED: Evaluation of the green supply chain management practices: A novel neutrosophic approach

A worst‐case formulation for constrained ranking and selection with input uncertainty

Efficient simulation budget allocation for subset selection using regression metamodels

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