Mason Archival Repository Service

Using Operational Patterns to Influence Attacker Decisions on a Contested Transportation Network

Show simple item record

dc.contributor.advisor Ganesan, Ragesh Stimpson, Daniel Edward
dc.creator Stimpson, Daniel Edward 2018-10-22T01:21:18Z 2018-10-22T01:21:18Z 2017
dc.description.abstract Ambushes, in the form of improvised explosive devices (IEDs), have posed grave risk to targeted vehicles operating on supply routes in recent theaters of war. History shows that this is an enduring problem that U.S. military forces will likely face again in the future. This research provides an underpinning argument and model demonstration of a previously unexplored approach to the attack prediction problem when conducting repetitive operations on a contested transportation network. The problem being addressed goes beyond the typical objective of maximizing IED detection and avoidance, or minimizing damage and delay. Rather the problem is re-framed to focus on using the defender's activities (that are being observed by the attacker) as a direct means to shape the attacker's expectations and therefore his attack choices. Thus, in contrast to most previous work, there is an explicit assumption of dependence between the defender's actions and the attacker's choices. Approximate dynamic programming (ADP) is applied in a reinforcement learning (RL) construct to determine convoy schedules and route clearance assignments in light of a responsive attacker. There are currently few analytical approaches for this problem in the literature, but RL algorithms offer opportunities for meaningful improvements by optimizing individual movements across an extended planning horizon, accounting for downstream attacker-defender interaction. Computational results show meaningful performance improvements over a one-step, myopic decision rule. Further, the decision policies that are discovered by the RL agent would be difficult for unaided human planners to duplicate.
dc.format.extent 148 pages
dc.language.iso en
dc.rights Copyright 2017 Daniel Edward Stimpson
dc.subject Operations research en_US
dc.subject Military studies en_US
dc.subject Transportation en_US
dc.subject Attacker-Defender en_US
dc.subject Dynamic Programming en_US
dc.subject Improvised Explosive Device en_US
dc.subject Reinforcement Learning en_US
dc.subject Route Clearance en_US
dc.subject Vehicle Routing en_US
dc.title Using Operational Patterns to Influence Attacker Decisions on a Contested Transportation Network
dc.type Dissertation Ph.D. Systems Engineering and Operations Research George Mason University

Files in this item

This item appears in the following Collection(s)

Show simple item record

Search MARS


My Account