Computing non-stationary $(s, S)$ policies using mixed integer linear programming
Optimization and Control
2018-09-17 v1
Abstract
This paper addresses the single-item single-stocking location stochastic lot sizing problem under the policy. We first present a mixed integer non-linear programming (MINLP) formulation for determining near-optimal 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 of the optimal policy cost and computational times are reasonable.
Cite
@article{arxiv.1702.08820,
title = {Computing non-stationary $(s, S)$ policies using mixed integer linear programming},
author = {Mengyuan Xiang and Roberto Rossi and Belen Martin-Barragan and S. Armagan Tarim},
journal= {arXiv preprint arXiv:1702.08820},
year = {2018}
}