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54 records from EconBiz based on author Name
1. Discrete-time survival models with neural networks for age-period-cohort analysis of credit risk
abstractSurvival models have become popular for credit risk estimation. Most current credit risk survival models use an underlying linear model. This is beneficial in terms of interpretability but is restrictive for real-life applications since it cannot discover hidden nonlinearities and interactions within the data. This study uses discrete-time survival models with embedded neural networks as estimators of time to default. This provides flexibility to express nonlinearities and interactions between variables and hence allows for models with better overall model fit. Additionally, the neural networks are used to estimate age-period-cohort (APC) models so that default risk can be decomposed into time components for loan age (maturity), origination (vintage), and environment (e.g., economic, operational, and social effects). These can be built as general models or as local APC models for specific customer segments. The local APC models reveal special conditions for different customer groups. The corresponding APC identification problem is solved by a combination of regularization and fitting the decomposed environment time risk component to macroeconomic data since the environmental risk is expected to have a strong relationship with macroeconomic conditions. Our approach is shown to be effective when tested on a large publicly available US mortgage dataset. This novel framework can be adapted by practitioners in the financial industry to improve modeling, estimation, and assessment of credit risk.
Wang, Hao; Bellotti, Anthony; Qu, Rong; Bai, Ruibin;2024
Type: Aufsatz in Zeitschrift; Article in journal;
Availability: Link Link
2. Container port truck dispatching optimization using Real2Sim based deep reinforcement learning
Jin, Jiahuan; Cui, Tianxiang; Bai, Ruibin; Qu, Rong;2024
Type: Aufsatz in Zeitschrift; Article in journal;
Availability:
Link 3. Controlling understaffing with conditional Value-at-Risk constraint for anintegrated nurse scheduling problem under patient demand uncertainty
abstractNursing workforce management is a challenging decision-making task in hospitals. The decisions are made across different timescales and levels from strategic long-term staffing budget to mid-term scheduling. These decisions are interconnected and impact each other, therefore are best taken by considering staffing and scheduling together. Moreover, this decision-making needs to be made in a stochastic setting to meet uncertain patient demand. A sufficient and cost-efficient staffing level with desirable schedule is essential to provide good working conditions for nurses and consequently good quality of care. On the other hand, understaffing can severely deteriorate the quality of care thus should be strictly controlled. To help with the decision making, based on our previous research we formulate in this paper an integrated nurse staffing and scheduling model under patient demand uncertainty into a two-stage stochastic programming model with an emphasis on understaffing risk control. Conditional Value-at-Risk (CVaR), a risk control measure primarily used in the financial domain, is integrated in the stochastic programming model to control understaffing risk. The IBM ILOG CPLEX solver is applied to solve the stochastic model. The model and solution approaches are tested using a case study in a real-world environment setting. We have evaluated the performance of the stochastic model and the benefit of CVaR in terms of impact on schedule quality.
He, Fang; Chaussaleta, Thierry; Qu, Rong;2019
Type: Aufsatz in Zeitschrift; Article in journal;
Availability: Link Link Link
Citations: 4 (based on OpenCitations)
4. A deep reinforcement learning based hyper-heuristic for combinatorial optimisation with uncertainties
Zhang, Yuchang; Bai, Ruibin; Qu, Rong; Tu, Chaofan; Jin, Jiahuan;2022
Type: Aufsatz in Zeitschrift; Article in journal;
Availability: Link
Citations: 24 (based on OpenCitations)
5. Assessing hyper-heuristic performance
Pillay, Nelishia; Qu, Rong;2021
Type: Aufsatz in Zeitschrift; Article in journal;
Availability: Link Link Link
Citations: 9 (based on OpenCitations)
6. A hybrid pricing and cutting approach for the multi-shift full truckload vehicle routing problem
Xue, Ning; Bai, Ruibin; Qu, Rong; Aickelin, Uwe;2021
Type: Aufsatz in Zeitschrift; Article in journal;
Availability: Link
Citations: 5 (based on OpenCitations)
7. Mean-VaR portfolio optimization : a nonparametric approach
Lwin, Khin T.; Qu, Rong; MacCarthy, Bart;2017
Type: Aufsatz in Zeitschrift; Article in journal;
Availability: Link
Citations: 80 (based on OpenCitations)
8. Good Laboratory Practice for optimization research
Kendall, Graham; Bai, Ruibin; Błażewicz, Jacek; De Causmaecker, Patrick; Gendreau, Michel; John, Robert; Li, Jiawei; McCollum, Barry; Pesch, Erwin; Qu, Rong; Nasser Sabar; Vanden Berghe, Greet; Yee, Angelina;2016
Type: Aufsatz in Zeitschrift; Article in journal;
Availability: Link
Citations: 57 (based on OpenCitations)
9. An application programming interface with increased performance for optimisation problems data
Pinheiro, Rodrigo Lankaites; Landa-Silva, Dario; Qu, Rong; Constantino, Ademir Aparecido; Yanaga, Edson;2016
Type: Aufsatz in Zeitschrift; Article in journal;
Availability: Link
Citations: 4 (based on OpenCitations)
10. Search with evolutionary ruin and stochastic rebuild : a theoretic framework and a case study on exam timetabling
Li, Jingpeng; Bai, Ruibin; Shen, Yindong; Qu, Rong;2015
Type: Aufsatz in Zeitschrift; Article in journal;