  The newsvendor problem is one of the most basic and widely applied inventory
models. There are numerous extensions of this problem. If the probability
distribution of the demand is known, the problem can be solved analytically.
  However, approximating the probability distribution is not easy and is prone
to error; therefore, the resulting solution to the newsvendor problem may be
not optimal. To address this issue, we propose an algorithm based on deep
learning that optimizes the order quantities for all products based on features
of the demand data. Our algorithm integrates the forecasting and
inventory-optimization steps, rather than solving them separately, as is
typically done, and does not require knowledge of the probability distributions
of the demand. Numerical experiments on real-world data suggest that our
algorithm outperforms other approaches, including data-driven and machine
learning approaches, especially for demands with high volatility. Finally, in
order to show how this approach can be used for other inventory optimization
problems, we provide an extension for (r,Q) policies.
