In Conditional Value-at-Risk robust optimization, we try to find an optimal decision that performs well in a subset of some worst-case estimators of the random parameters of the optimization problem. With funct
Rockafellar, R., Uryasev, S.: Optimization of conditional value-at-risk. J. Risk 2, 21–42 (2000) Article Google Scholar Rusmevichientong, P., Topaloglu, H.: Robust assortment optimization in revenue management under the multinomial logit choice model. Oper. Res. 60(4), 865–882 (201...
Liu, Chen, Lisser, and Xu (2019) also construct a robust mean-conditional value at risk portfolio selection model where the uncertainty set is developed under the framework of the technique proposed by Delage and Ye (2010). Other than moment-based uncertainty sets that relies on data-driven ...
(2013) respectively adapted Value-at-Risk and Conditional Value-at-Risk models in hazmat transportation. From a different perspective, when an incident occurs during transit, the surrounding environment, property, and people are considered potential risk factors. As a result, the risks can be ...
Received: 14 April 2024 Revised: 20 May 2024 DOI: 10.1049/rpg2.13021 Accepted: 22 May 2024 ORIGINAL RESEARCH IET Renewable Power Generation Low-carbon demand response program for power systems considering uncertainty based on the data-driven distributionally robust chance constrained optimization Ruifeng ...
Open banking is a customer consent-driven data-sharing framework to maintain interoperability among financial and non-financial institutions through secure application programming interfaces. Traditional retail banks are losing their competitive edge aga
The conventional derivation of equations inevitably involves a degree of empiricism, such as selecting and defining variables, conditional assumptions, and simplifications. The new paradigm offers an alternative to these locally empirical methods and promotes improved subsequent derivation, leading to better ...
Table 8 demonstrates the conditional probability of the node and the range of efficiency values. From Table 8, the minimum and maximum values are taken as the range of transportation time efficiency values for the optimization model, i.e.,[0.05091660, 0.331437]. Scientific Reports | (2025) 15...
To solve this problem, researchers have tried different optimization methods, mainly stochastic optimization (SO) [[20], [21], [22]] [[20], [21], [22]] [[20], [21], [22]] and robust optimization (RO) [23,24]. Some researchers proposed a CVaR-IGDT model based on Conditional ...
In summary, the proposed uncertainty set is employed in a novel two-stage robust optimization dispatch model. The upper robust dispatching model updates the constraints according to the number of real-time EV insertions to obtain the lowest multi-microgrid operating cost under worst-case scenarios....