{
  "id": "1702.08820",
  "version": "v1",
  "published": "2017-02-28T15:18:30.000Z",
  "updated": "2017-02-28T15:18:30.000Z",
  "title": "Computing non-stationary $(s, S)$ policies using mixed integer linear programming",
  "authors": [
    "Mengyuan Xiang",
    "Roberto Rossi",
    "Belen Martin-Barragan",
    "S. Armagan Tarim"
  ],
  "categories": [
    "math.OC"
  ],
  "abstract": "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 $0.3\\%$ of the optimal policy cost and computational times are reasonable.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-02-28T15:18:30.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "mixed integer linear programming",
      "computing non-stationary",
      "single-item single-stocking location stochastic lot",
      "location stochastic lot sizing problem"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}