Computing non-stationary $(s,S)$ policies using mixed integer linear programming

This paper addresses the single-item single-stocking location stochastic lot sizing problem under the $(s,S)$ policy. We first present a mixed integer non-linear programming (MINLP) formulation for determining near-optimal $(s, S)$ policy parameters. To tackle larger instances, we then combine the previously introduced MINLP model and a binary search approach. These models can be reformulated as mixed integer linear programming (MILP) models which can be easily implemented and solved by using off-the-shelf optimisation software. Computational experiments demonstrate that optimality gaps of these models are around $0.3\mathrm{\%}$ of the optimal policy cost and computational times are reasonable.