Algorithms that search for good solutions to optimization problems present a trace of current best objective values over time. We describe parametric models of this progression that are both interesting as ways to characterize the search progression in a compact way and useful as means of...

Progressive hedging (PH) is a scenario-based decomposition technique for solving stochastic programs. While PH has been successfully applied to a number of problems, a variety of issues arise when implementing PH in practice, especially when dealing with very difficult or large-scale...

A Stochastic Program for Interdicting Smuggled Nuclear Material -- Enumerating Near-Min S-T Cuts -- A Decomposition-Based Pseudoapproximation Algorithm for Network Flow Inhibition -- Interdicting Stochastic Networks with Binary Interdiction Effort -- Stochastic Batch-Sizing Problems: Models and...

Computer Science and Operations Research continue to have a synergistic relationship and this book - as a part of the Operations Research and Computer Science Interface Series - sits squarely in the center of the confluence of these two technical research communities. The research presented in...

The book begins with an easy-to-read introduction to the concepts associated with the creation of optimization models for production planning. These concepts are then applied to well-known planning models, namely mrp and MRP II. From this foundation, fairly sophisticated models for supply chain...