Large-scale optimization problems arise in a wise variety of important application areas, such as the design of supply chains, logistics networks, telecommunications networks, cancer treatment plans, and pharmaceutical products. Many of the models that arise in these important areas involve variables that must take on integer values. Such models are notoriously difficult to solve and the data that are needed to form the models are rarely known with certainty in advance. Hence, such models are subject to change, even after the solution process is underway. Current solution methods addresses only static models and does not allow for this kind of change. A change to the model once the solution process is underway generally means that the model must be resolved from scratch. This SGER grant provides funding for the development of tools that will help alleviate this problem by providing post-solution sensitivity analysis and resolve capabilities that can be integrated with existing solution algorithms. Such tools will enable the user to either quickly approximate the effect of a given change in the input data on the final solution or to efficiently resolve the problem without starting from scratch. If successful, this work will allow the development of tools for solving difficult, large-scale optimization problems for which the input data are subject to change or for which it is desirable to be able to answer various "what-if" questions after the fact. It will also enable more efficient implementation of various methodologies that require the iterative solution of a series of very similar models. Such methodologies will benefit greatly from the development of the "warm-starting" procedures that we are proposing, which will allow the solution process to be started from an advanced starting point based on information gained from prior solution of a similar model. All of the methodology developed will be implemented and made freely available as open source software in order to reach the widest possible audience
