Mission Dependency Network Analysis: Mathematical Foundations for Modeling and Practice (Risk, Systems and Decisions)

This book describes opportunities in mission dependency network analysis made possible by advances in optimization methods.
Mission dependency networks are increasingly complex, interconnected, and critically important. They may be as physically large as an international logistics system or as small as an embedded network within a car. They may be primarily physical as is a national transportation system or primarily functional as is a complex series of tasks required to achieve a military objective. Many are vulnerable to impairment due to malicious or accidental intent, natural disaster, or the natural failure of their components. Optimizing such systems is difficult.
Advances in optimization methods are a great intellectual treasure which has strikingly less public visibility compared to its pervasive impact on people's lives. It has had a long history of improving the design, performance, and effectiveness of operational tasks.  Modern algorithms and computers can solve optimization problems over ten orders of magnitude faster than 25 years ago.  This opens the door to new analyses which previously could not be imaginable.

This book frames important mission dependency analysis questions and develops a vocabulary and language to start addressing these issues by presenting a body of optimization analysis developed internally at The MITRE Corporation. While it is solution oriented it seeks to push the state-of-the-art techniques to overcome hurdles which the current academic literature cannot directly address. Parts of this book are highly technical and might be understood only by a reader versed in Operations Research. However, this book also seeks to appeal to three different sets of readers:

-        a high-level decision maker interested in learning the big picture;

-        an applied researcher seeking to refine, extend, or adapt this work to systems of interest to them;

-        an operational practitioner interested in applying some of the algorithms or problem formulations while trusting their rigor.