Robust newsvendor problems with compound Poisson demands

We consider the robust newsvendor problem where the demand follows a compound Poisson distribution, but its distribution is only partly known. This assumption means that customers arrive according to a Poisson process with a given intensity, while the size of customer demand is another random variable. Yet, the newsvendor only knows the expectation and variance of the demand sizes. Given limited information, a plausible approach, put forth in prior work, is to evaluate the moments of the aggregate demand (from all customers) and then determine the respective order quantity. Instead, this paper suggests to make the best use of all the information contained in the first few moments of demand sizes as well as the structural properties of a compound demand distribution to compute a better ordering quantity. To achieve this goal, we propose a new decision model employing Panjer’s recursion as constraints. The attendant optimization problem is then solved via convex relaxation using McCormick envelopes. Numerical results confirm that the newsvendor can gain a significant increase in expected profit using the new modeling approach to make his/her ordering decision.