(577g) A Functional Approach to Optimization of Supply Chain Networks with Both Efficiency and Robustness Objectives Conference: AIChE Annual MeetingYear: 2008Proceeding: 2008 AIChE Annual MeetingGroup: Computing and Systems Technology DivisionSession: Poster Session: Computers in Operations and Information Processing Time: Wednesday, November 19, 2008 - 6:00pm-8:30pm Authors: Shukla, A., Purdue University Agarwal, V., Purdue University As an event driven system, a supply chain network will face not only the uncertainties in the supply chain, but also the unexpected events in the supply chain network such as contingency, disruption, disaster and terrorism. These uncertainties and unexpected events have a negative impact on the survival and performance of the supply chain network (Dong 2006). Some work has been done in this field based on a graph-theoretic approach to define robustness indices for networks (Dong 2006; Venkatasubramanian et al. 2004). Since most firms are interested in analyzing the effect of network disruptions on their bottom-line, a graph-theoretic approach might not always be the best way to tackle this problem. Snyder and Daskin have looked at this problem for a case when risk pooling is possible by supply from the unaffected nodes, in case of failure of some nodes in the network (Lawrence and Mark 2005). In several cases high transportation cost or absence of a link between two nodes might render risk pooling from other nodes impractical. We have used a simple supply chain facility location model (Meepetchdee and Shah 2007) for determining the location of warehouses. Scenario planning is a valuable tool for analyzing the behavior of the network due to disruptions (Gaonkar and Vishwanadham 2004). We have incorporated this approach in our model to calculate the extra cost incurred to the network due to anticipated failures. Since network disruption can occur, both due to failure of nodes and the links connecting them, separate models were developed for both of these cases. The networks that emerge show interesting insights into designing optimal supply chain networks with least long-term costs. References Dong, M. (2006). "Development of supply chain network robustness index." International Journal of Services Operations and Informatics, 1, 54-66. Gaonkar, R., and Vishwanadham, N. (2004). "A conceptual and analytical framework for the management of risk in supply chains." IEEE International Conference on Robotics and Automation, 26, 2699-2704. Lawrence, V. S., and Mark, S. D. (2005). "Reliability Models for Facility Location: The Expected Failure Cost Case." Transportation Science, 39(3), 400-416. Meepetchdee, Y., and Shah, N. (2007). "Logistical network design with robustness and complexity considerations." International Journal of Physical Distribution & Logistics Management, 37(3), 201-222. Venkatasubramanian, V., Katare, S., Patkar, P. R., and Mu, F.-P. (2004). "Spontaneous emergence of complex optimal networks through evolutionary adaptation." Computers and Chemical Engineering, 28(9), 1789-1798.