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Computing non-stationary (s, S) policies using mixed integer linear programming
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DOI：10.1016/j.ejor.2018.05.030.png)
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Abstract
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This paper addresses the single-item single-stocking location non-stationary stochastic lot sizing problem under the (s, S) control 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 optimization software. Computational experiments demonstrate that optimality gaps of these models are less than 0.3% of the optimal policy cost and computational times are reasonable. (C) 2018 Elsevier B.V. All rights reserved.
Keywords:
Inventory
(s, S) policy
Stochastic lot-sizing
Mixed integer programming
Binary search
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