Parametric Distributionally Robust Optimisation Models for Budgeted Multi-period Newsvendor Problems

In this paper, we consider a static, multi-period newsvendor model under a budget constraint. In the case where the true demand distribution is known, we develop a heuristic algorithm to solve the problem. By comparing this algorithm with off-the-shelf solvers, we show that it generates near-optimal solutions in a short time. We then consider a scenario in which limited information on the demand distribution is available. It is assumed, however, that the true demand distribution lies within some given family of distributions and that samples can be obtained from it. We consider the cases of normal and Poisson demands. For each case, we show that using maximum likelihood estimates in place of the true parameters can lead to poor estimates of the true cost associated with an order quantity. Hence, we make use of likelihood inference to develop confidence sets for the true parameters. These are used as ambiguity sets in a distributionally robust model, where we enforce that the worst-case distribution lies in the same family as the true distribution. We solve these models by discretising the ambiguity set and reformulating them as piecewise linear models. We show that these models quickly become large as the ambiguity set grows, resulting in long computation times. To overcome this, we propose a heuristic cutting surface algorithm that exploits theoretical properties of the objective function to reduce the size of the ambiguity set. We illustrate that our cutting surface algorithm solves orders of magnitude faster than the piecewise linear model, while generating very near-optimal solutions