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

• Published in: International journal of production research Vol. 62; no. 4; pp. 1221 - 1238
• Main Author: Chen, Zhen-Yu
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 16.02.2024; Taylor & Francis LLC
• Subjects: Asymmetry; conditional value-at-risk; Criteria; Data science; Decisions; Kernel functions; Machine learning; newsvendor model; Quadratic programming; Regularization; Risk; Risk aversion; support vector machine; Support vector machines

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 t...