Data-driven risk-averse newsvendor problems: developing the CVaR criteria and support vector machines

Abstract

Incorporating decision-makers' risk preferences into data-driven newsvendor models and developing machine learning methods to solve the models are the challenging problems addressed in this study. To consider different distributions and decision-makers' different risk preferences for the two losses of the total cost newsvendor model, the symmetrical, the partial symmetrical and the asymmetrical CVaR criteria are introduced. The regularisation, the primal-dual approach and the kernels in support vector machines are used to transform the data-driven risk-averse newsvendor problems under the CVaR criterion into the convex quadratic programming problems with good theoretical properties. Computational experiments are conducted on a real-world dataset. The models under the partial symmetrical and the asymmetrical CVaR criteria obtained good performances, but that under the symmetrical CVaR criterion suffered the underfitting problem. Two factors including the degrees of risk aversion for the two losses in the total cost newsvendor model and the empirical errors of data-driven models affect order decisions. The degrees of risk aversion for the two losses have anti-directional effects on order quantities. The introduction of asymmetrical CVaR criterion paves a new way to reveal the effects of different risk references for different losses on order decisions, and has the potential to improve newsvendor decisions.

KEYWORDS:

• Data science
• newsvendor model
• risk aversion
• conditional value-at-risk
• support vector machine

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available upon request from the corresponding author.

Additional information

Notes on contributors

Zhen-Yu Chen

Zhen-Yu Chen received his Ph.D. from the University of Chinese Academy of Sciences in 2008. He is an associate professor in Northeastern University, China. His research interests include data mining, operational research and data-driven optimisation. He has coauthored some journal publications including INFORMS Journal on Computing, European Journal of Operational Research, Journal of the Operational Research Society, Knowledge and Information Systems, Knowledge-based Systems, among others.