| Contingencies are unexpected crises that cause a major threat to the safety and well being of a specific population. The current literature has focused primarily on the economic issues related to contingencies. This research effort differs in that it focuses on the underlying elements that contribute to the success or failure of a contingency response. This effort builds upon and extends the preliminary work on contingency logistics reliability modeling by Thomas (2004).;The modeling approach takes a mission orientation and focuses on the ability to recover from or prevent a contingency logistics failure. First, the existing contingency logistic network models are extended by including safety stocks in the operational nodes. Importance measures are developed and used to allocate safety stocks for specific levels of network reliability. To cope with the uncertainties associated with demand and supply as well as the difficulties associated with the use of interference theory models, an approximation measure is developed based on the mean value first order second moment technique to assist in modeling the risk of a logistics node. Later, a decision maker's tolerance for the risk associated with a node failure is incorporated through the use of distortion. Specifically, the dual power and proportional hazard distortion models are investigated in this modeling paradigm. Resource allocation and risk mitigation models are formulated for complex contingency networks.;Next, the sustainability of a contingency logistics network is modeled using the concept of selective maintenance. This problem, once formulated, is a non-convex, non-linear, non-separable, multi-dimensional, discrete knapsack problem. These problems are known to be NP hard. A memetic algorithm is developed and proposed for identifying the best set of maintenance actions to sustain the contingency logistics network. Finally, a network manager's preference toward the numerous criteria (reliability, cost, time, resource utilization etc...) is judiciously exploited through the use of Physical Programming, a multi criteria optimization procedure. This application provides a logistic manager greater flexibility to express their preference for a variety of scenarios and concurrently optimize competing objectives based on the desirability levels set by the manager.;In brief, this research effort fills several voids in the areas of contingency logistic network design and management with the development of the proposed models and techniques. This provides an initial modeling framework that has the potential to motivate researchers in related fields to extend the models and add more realism and complexity to assist logisticians when faced with the next set of catastrophes requiring emergency logistics support. These basic models have been oriented toward government and military type scenarios by focusing on mission success. Opportunities exist for extending much of this work to the service sector. |
