Optimal patrol to uncover threats in time when detection is imperfect

Consider a patrol problem, where a patroller traverses a graph through edges to detect potential attacks at nodes. An attack takes a random amount of time to complete. The patroller takes one time unit to move to and inspect an adjacent node, and will detect an ongoing attack with some probability. If an attack completes before it is detected, a cost is incurred. The attack time distribution, the cost due to a successful attack, and the detection probability all depend on the attack node. The patroller seeks a patrol policy that minimizes the expected cost incurred when, and if, an attack eventually happens. We consider two cases. A random attacker chooses where to attack according to predetermined probabilities, while a strategic attacker chooses where to attack to incur the maximal expected cost. In each case, computing the optimal solution, although possible, quickly becomes intractable for problems of practical sizes. Our main contribution is to develop efficient index policies—based on Lagrangian relaxation methodology, and also on approximate dynamic programming—which typically achieve within 1% of optimality with computation time orders of magnitude less than what is required to compute the optimal policy for problems of practical sizes

Quantitative models for police patrol deployment.

Thesis. 1975. Ph.D.--Massachusetts Institute of Technology. Alfred P. Sloan School of Management.Vita.Includes bibliographies.Ph.D

Opportunities for Decision Analysis in Engineering Management

The engineering management (EM) decision-making (DM) environment in modern organizations is becoming increasingly complex. This is partly due to the technical complexity of systems being developed and the managerial complexity of orchestrating the decisions made by multidisciplinary teams, usually with conflicting goals and objectives. Systematic approaches, such as decision and risk analysis (DRA), can play a significant role in improving EM DM methods by providing efficient, effective, repeatable, and communicative DM processes, procedures, and guidelines. In this paper, we propose the use of the DRA paradigm to provide a standard and repeatable methodology for EM DM environments. We introduce and explore the four phases of the proposed DM process and discuss barriers to its use. © 1973-2011 IEEE

Evaluating the Efficacy of Cattail (Typha spp.) Fiber for Oil Sorption

Oil spills pose a serious threat to aquatic life, the environment, and human health. Current methods to remove oil from waterways and mitigate damage include burning, skimming, and synthetic sorbents; however, they all have substantial limitations. Previous studies have shown that cattail (Typha spp.) fibers have potential as natural sorbents due to their hydrophobic and lipophilic properties. Additionally, cattail may be a more sustainable alternative than other natural sorbents including cotton. It is easily harvested, can be grown in a wide range of climates and has a smaller water footprint than cotton. The purpose of this study was to conduct a materials comparison between cattail fiber and other products such as cotton under varying water conditions for application as a sorbent during oil spill cleanup and remediation. Oil sorption of cattail and cotton was measured and expressed as gram-to-gram ratio of oil-to-fiber under a range of environmental conditions including water temperature and salinity. In cattail, the effectiveness of oil sorption decreased at higher temperatures and this also led to a decrease in selectivity in an oil/water mixture. Cotton sorbed more oil than cattail under any conditions, but also sorbed more water so cotton was not as selective as cattail in sorbing oil under moderate temperatures. These results demonstrate that cattail would be a better sorbent than cotton except in very warm waters since it is more selective for oil compared to water, and provide information necessary for future developments in sorbent technology using cattails