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Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/403
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Article Citation - WoS: 18Citation - Scopus: 23Computing Non-Stationary (S, S) Policies Using Mixed Integer Linear Programming(Elsevier Science Bv, 2018) Xiang, Mengyuan; Rossi, Roberto; Martin-Barragan, Belen; Tarim, S. ArmaganThis 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.Article Citation - WoS: 9Citation - Scopus: 11Confidence-Based Reasoning in Stochastic Constraint Programming(Elsevier, 2015) Rossi, Roberto; Hnich, Brahim; Tarim, S. Armagan; Prestvvich, Steven; Prestwich, StevenIn this work we introduce a novel approach, based on sampling, for finding assignments that are likely to be solutions to stochastic constraint satisfaction problems and constraint optimisation problems. Our approach reduces the size of the original problem being analysed; by solving this reduced problem, with a given confidence probability, we obtain assignments that satisfy the chance constraints in the original model within prescribed error tolerance thresholds. To achieve this, we blend concepts from stochastic constraint programming and statistics. We discuss both exact and approximate variants of our method. The framework we introduce can be immediately employed in concert with existing approaches for solving stochastic constraint programs. A thorough computational study on a number of stochastic combinatorial optimisation problems demonstrates the effectiveness of our approach. (C) 2015 Elsevier B.V. All rights reserved.