Simulation-based Optimization Approach to Supply Chain Network Design Under Uncertainty Incorporating Tactical and Strategic Decisions
Open AccessModern firms invest in supply chains to achieve or preserve competitive advantage. Supply chains are growing larger and more complex while they are expected to be more responsive to changes in customer demand, product design, manufacturing technology, transportation costs, and international trade agreements. Since decisions made at the operational and tactical levels of the supply chain can alter the effectiveness of decisions made at the strategic level, designs must incorporate decisions made at multiple levels. Many decisions encountered in practice result in objective functions which are non-convex, and these decisions must be made with uncertain or incomplete information. The design of modern supply chain networks is NP-hard, leaving many optimization approaches ill-suited for large problems. This research proposes a simulation-based optimization approach using a discrete event simulation combined with a genetic algorithm to find a near-optimal solution to a large supply chain network design problem, incorporating decisions made at the operational, tactical and strategic levels, in the presence of uncertainty. The results show this approach provides solutions to problems relevant to the practitioner, without requiring unrealistic assumptions, and that solving the supply chain network design problem incorporating decisions at all levels yields an additional 3.5% savings in total operating cost over solutions sequentially optimizing the strategic decisions and then the tactical decisions.
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