Abstract
The modern enterprise has developed highly complex supply chains in order to efficiently satisfy demand while remaining competitive. Supply chains have become distributed global networks that encompass not only the manufacture and delivery of goods but also the activities associated with their development. Moreover, local “here and now” decisions must be made in the presence of future uncertainty while also considering their global and long-term implications. This coupling of wide problem scope with multiple sources of internal and external uncertainties, such as production line breakdowns, raw material availability, market demand, exchange rate fluctuations, developmental failures, etc., has resulted in supply chain decision-making processes that are of high complexity and a very large scale (Zapata et al. 2008).
Keywords
- Response Surface Methodology
- Stochastic Approximation
- Safety Stock
- Decision Vector
- Simulation Optimization
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Zapata, J.C., Pekny, J., Reklaitis, G.V. (2011). Simulation-Optimization in Support of Tactical and Strategic Enterprise Decisions. In: Kempf, K., Keskinocak, P., Uzsoy, R. (eds) Planning Production and Inventories in the Extended Enterprise. International Series in Operations Research & Management Science, vol 151. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-6485-4_20
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