The Construction of a Newsvendor Model Based on Conditional Value at Risk (CVaR) and the Determination of Optimal Order Quantity
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Abstract
To address the growing uncertainty in demand for electric vehicles in the market, this article develops an extended CVaR Newsvendor model. Compared to existing Newsvendors, there are two contributions: (i) embedding both risk aversion and loss aversion in utility-oriented objectives; (ii) handling limited additions using the “three scenarios” (no stockouts/no additions, no stockouts/limited additions, and stockouts/limited additions). Based on 12 months of data from a single retailer, the model was calibrated under a normal distribution demand and fixed cost structure, and evaluated using a Monte Carlo simulation. The model had a high fitting degree (98.08%) and a low root mean square absolute error (0.8 vehicles). This study assumes a single cycle and a single project, with parameters considered stable during the sample period, and does not explicitly incorporate exogenous factors such as policies and input prices. These assumptions limit the research's generality, but clarify how to adjust the framework in practice through scenario-based parameter tuning.
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