Abstract
This paper addresses the problem of designing an integrated multi-echelon, multi-product, multi-period supply chain network under uncertainty in which products can be outsourced to improve responsiveness and the overall performance. The network consists of production plants, distribution centres and retailers. The demands of retailers, production capacities, and transportation costs as well as costs of opening distribution centres are subject to uncertainty. A deterministic mixed-integer linear programming (MILP) model is developed with the objective of minimising sum of the fixed, transportation, and outsourcing costs. In order to capture the uncertainties, a robust optimisation model is proposed. A set of numerical experiments using nominal and realisation data is investigated and the relative performance of the robust and deterministic models is compared based on the standard deviation and mean of the objective function values. In case of data realisation, results confirm that the proposed robust model outperforms the deterministic counterpart.
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