http://hdl.handle.net/1721.1/5066 http://hdl.handle.net/1721.1/40878 Optimization-based allocation of force protection resources in an asymmetric environment DeGregory, Keith W. (Keith William) More than four years after the end of major combat operations in the 2003 Iraq War, the United States military continues to sustain casualties at rates higher than those during the ground campaign. Combat service support soldiers conducting daily convoy operations on the Iraqi road network account for a large number of these casualties. One reason for this is the threat's affinity to targeting soft, vulnerable, high-payoff targets through the use of roadside bombs, otherwise known as improvised explosive devices. This enemy tactic is characteristic of asymmetric warfare, in which a lesser opponent opposes a force far superior in numbers, equipment, and technology. In an asymmetric operating environment, threats blend in with the local populace making them hard to detect and are easily capable of multi-directional attacks; absent are the linear battlefields of past wars where logistical soldiers operated in the relative safety of the rear battlefield. This thesis explores a mathematical approach to decide how to use available resources to best protect logistical convoys. To achieve this we first model the threat using probabilistic models and identify input data requirements associated with the operating environment and other relevant factors. (cont.) Second, we identify a set of force protection resources and model their counter-effects on the threat. Next, we develop a binary integer program to optimally allocate the force protection resources to a set of planned logistical convoys. Our model uses an algorithm that assigns resources to either fixed areas or individual convoys in a way that minimizes overall threat effects to the convoys. The algorithm provides lower-risk plans yielding a lower expected number of casualties. We propose integrating this force protection algorithm in conjunction with convoy planning software that optimally builds and routes convoys based on minimizing exposure to the threat to achieve even better plans. We test the performance of a system that accomplishes this by comparing its resulting plans to human-generated plans in a controlled experiment. Additionally, we conduct Monte Carlo simulations to statistically analyze the system's performance. We find that the system produces lower-risk plans in less time than human planners. We describe future development of this methodology to reducing soldier casualties, and a proposed approach for its integration into existing Army systems and processes. Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2007. Includes bibliographical references (p. 137-138). Sun, 29 Oct 2006 22:58:59 GMT http://hdl.handle.net/1721.1/40389 Trust-based design of human-guided algorithms Thomer, Joseph L. (Joseph Louis) By combining the strengths of human and computers, Human Machine Collaborative Decision Making has been shown to generate higher quality solutions in less time than conventional computerized methods. In many cases, it is difficult to model continually changing problems and incorporate human objectives into the solution. Human-guided algorithms (HGAs) harness the power of sophisticated algorithms and computers to provide flexibility to the human decision maker to model correctly and dynamically the problem and steer the algorithm to solutions that match his/her objectives for the given problem. HGAs are designed to make the power of Operations Research accessible to problem domain experts and decision makers, and incorporate their expert knowledge into every solution. In order to appropriately utilize algorithms during a planner's decision making, HGA operators must appropriately trust the HGA and the final solution. Through the use of trust-based design (TBD), it was hypothesized that users of the HGA will gain better insight into the solution process, improve their calibration of trust, and generate superior solutions. The application of TBD requires the consideration of algorithms, solution steering methods, and displays required to best match human and computer complimentary strengths and to generate solutions that can be appropriately trusted. (cont.) Abstract hierarchy, Ecological Interface Design, and various trust models are used to ensure that the HGA operators' evaluation of trust can be correctly calibrated to all necessary HGA trust attributes. A human-subject evaluation was used to test the effectiveness of the TBD design approach for HGAs. An HGA, including the appropriate controls and displays, was designed and developed using the described TBD approach. The participants were presented with the task of using the HGA to develop a routing plan for military aircraft to prosecute enemy targets. The results showed that TBD had a significant effect on trust, HGA performance, and in some cases the quality of final solutions. Another finding was that, HGA operators must be provided with additional trust related information to improve their understanding of the HGA, the solution process, and the final solution in order to calibrate properly their trust in the system. Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2007. Includes bibliographical references (p. 227-229). Sun, 29 Oct 2006 22:58:59 GMT http://hdl.handle.net/1721.1/40388 Large-scale dynamic observation planning for unmanned surface vessels Miller, John V. (John Vaala) With recent advances in research and technology, autonomous surface vessel capabilities have steadily increased. These autonomous surface vessel technologies enable missions and tasks to be performed without the direction of human operators, and have changed the way scientists and engineers approach problems. Because these robotic devices can work without manned guidance, they can execute missions that are too difficult, dangerous, expensive, or tedious for human operators to attempt. The United States government is currently expanding the use of autonomous surface vessel technologies through the United States Navy's Spartan Scout unmanned surface vessel (USV) and NASA's Ocean-Atmosphere Sensor Integration System (OASIS) USV. These USVs are well-suited to complete monotonous, dangerous, and time-consuming missions. The USVs provide better performance, lower cost, and reduced risk to human life than manned systems. In this thesis, we explore how to plan multiple USV observation schedules for two significant notional observation scenarios, collecting water temperatures ahead of the path of a hurricane, and collecting fluorometer readings to observe and track a harmful algal bloom. (cont.) A control system must be in place that coordinates a fleet of USVs to targets in an efficient manner. We develop three algorithms to solve the unmanned surface vehicle observation-planning problem. A greedy construction heuristic runs fastest, but produces suboptimal plans; a 3-phase algorithm which combines a greedy construction heuristic with an improvement phase and an insertion phase, requires more execution time, but generates significantly better plans; an optimal mixed integer programming algorithm produces optimal plans, but can only solve small problem instances. Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2007. Includes bibliographical references (p. 129-134). Sun, 29 Oct 2006 22:58:59 GMT http://hdl.handle.net/1721.1/40387 Dynamic planning under uncertainty for theater airlift operations Martin, Kiel M. (Kiel Michael) In this thesis, we analyze intratheater airlift operations, and propose methods to improve the planning process. The United States Air Mobility Command is responsible for the air component of the world wide U.S. military logistics network. Due to the current conflict in Iraq, a small cell within Air Mobility Command, known as Theater Direct Delivery, is responsible for supporting ongoing operations by assisting with intratheater airlift. We develop a mathematical programming approach to schedule airlift missions that pick up and deliver prioritized cargo within time windows. In our approach, we employ composite variables to represent entire missions and associated decisions, with each decision variable including information pertaining to the mission routing and scheduling, and assigned aircraft and cargo. We compare our optimization-based approach to one using a greedy heuristic that is representative of the current planning process. Using measures of efficiency and effectiveness, we evaluate and compare the performance of these different approaches. Finally, we adjust selected parameters of our model and measure the resulting changes in operating performance of our solutions, and the required computational effort to generate the solutions. Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2007. Includes bibliographical references (p. 92-93). Sun, 29 Oct 2006 22:58:59 GMT