Surveillance Planning against Smart Insurgents in Complex Terrain

This study is concerned with finding a way to solve a surveillance system allocation problem based on the need to consider intelligent insurgency that takes place in a complex geographical environment. Although this effort can be generalized to other situations, it is particularly geared towards protecting military outposts in foreign lands. The technological assets that are assumed available include stare-devices, such as tower-cameras and aerostats, as well as manned and unmanned aerial systems. Since acquiring these assets depends on the ability to control and monitor them on the target terrain, their operations on the geo-location of interest ought to be evaluated. Such an assessment has to also consider the risks associated with the environmental advantages that are accessible to a smart adversary. Failure to consider these aspects might render the forces vulnerable to surprise attacks. The problem of this study is formulated as follows: given a complex terrain and a smart adversary, what types of surveillance systems, and how many entities of each kind, does a military outpost need to adequately monitor its surrounding environment? To answer this question, an analytical framework is developed and structured as a series of problems that are solved in a comprehensive and realistic fashion. This includes digitizing the terrain into a grid of cell objects, identifying high-risk spots, generating flight tours, and assigning the appropriate surveillance system to the right route or area. Optimization tools are employed to empower the framework in enforcing constraints--such as fuel/battery endurance, flying assets at adequate altitudes, and respecting the climbing/diving rate limits of the aerial vehicles--and optimizing certain mission objectives--e.g. revisiting critical regions in a timely manner, minimizing manning requirements, and maximizing sensor-captured image quality. The framework is embedded in a software application that supports a friendly user interface, which includes the visualization of maps, tours, and related statistics. The final product is expected to support designing surveillance plans for remote military outposts and making critical decisions in a more reliable manner

A terrain risk assessment method for military surveillance applications for mobile assets

This study proposes an analytical and flexible terrain risk assessment method for military surveillance applications for mobile assets. Considering the risk as the degree of possibility of insurgent presence, the assessment method offers an efficient evaluation of risk in the surrounding terrain for military combat operating posts or observation posts. The method is designed for unmanned aerial vehicles as the surveillance assets of choice to improve the effectiveness of their use. Starting with the area map and geographical data, the target terrain is first digitized for space representation. Then the data of nine geographical parameters are used to formulate five contributing risk factors. These factors are incorporated in an analytical framework to generate a composite map with risk scores that reveal the potential high-risk spots in the terrain. The proposed method is also applied to a real-life case study of COP Kahler in Afghanistan, which was a target for insurgent attacks in 2008. The results confirm that when evaluated with the developed method, the region that the insurgents used to approach COP Kahler has high concentration of high-risk cells

Data standards in healthcare supply chain operations

This paper presents the challenges and benefits associated with adoption of healthcare supply chain data standards in a hospital environment. In a highly fragmented industry like healthcare with several stakeholders, the adoption and use of common data standards for identifying delivery locations and products is critical. Common data standards ensure system wide interoperability and visibility across the supply chain, contributing to improvements in patient safety and streamlined internal and external supply chain operations. However, the global healthcare industry has been significantly slow in adopting data standards in comparison to other industries like retail, manufacturing. We discuss the results from data standard adoption pilot project conducted by Center for Innovation in Healthcare Logistics (CIHL), University of Arkansas at Washington Regional Medical Center, a 325 bed not-for-profit hospital in Fayet-teville, Arkansas. CIHL data standards pilot involved studying the existing supply chain processes, design, pilot-test, and evaluation of GS1 data standards adoption over a sample of products and a single delivery location at the hospital. We present the results, which demonstrate the capabilities of systemwide improvements and roadblocks likely to be encountered. Findings from the pilot can be expanded to develop a broad implementation plan of data standards adoption for healthcare providers

A levels, readiness, and impact evaluation model for GS1 adoption in healthcare

The use of GS1 data standards is envisioned to improve the efficiency of healthcare supply chain, as it did for retail. Care providers willing to adopt such a strategy in some or all of their operations often need to invest in process changes and technological installations or upgrades; however, they struggle to project returns on those investments and hence, find uncertain ROI a barrier to adoption. We present a hierarchical comprehensive model that helps potential adopters of various parts of the standards (what we term levels) evaluate their readiness requirements and quantify the impacts of their potential decisions in terms of non-monetary performance measures, such as productivity. The model design is showcased through practical examples