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Multi-layered security investment optimization using a simulation embedded within a genetic algorithm

Proceedings - Winter Simulation Conference

Brown, Nathanael J.; Jones, Katherine A.; Nozick, Linda K.; Xu, Ningxiong

The performance of a multi-layered security system, such as those protecting high-value facilities or critical infrastructures, is characterized using several different attributes including detection and interruption probabilities, costs, and false/nuisance alarm rates. The multitude of technology options, alternative locations and configurations for those technologies, threats to the system, and resource considerations that must be weighed make exhaustive evaluation of all possible architectures extremely difficult. This paper presents an optimization model and a computationally efficient solution procedure to identify an estimated frontier of system configuration options which represent the best design choices for the user when there is uncertainty in the response time of the security force, once an intrusion has been detected. A representative example is described.

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Contingency Contractor Optimization Engineering Prototype – Release 2.3 (Phase 3 Extension – Training Examples)

Bandlow, Alisa B.; Adair, Kristin L.; Durfee, Justin D.; Frazier, Christopher R.; Jones, Katherine A.; Gearhart, Jared L.; Nozick, Linda K.

This document provides training examples to provide users practice in using the tool. Detailed instructions on how to use the tool can be found in the User Manual (SAND2015-6028).The Contingency Contractor Optimization project is intended to address former Secretary Gates’ mandate in a January 2011 memo and DoDI 3020.41 by delivering a centralized strategic planning tool that allows senior decision makers to quickly and accurately assess the impacts, risks, and mitigation strategies associated with utilizing contract support. Based on an electronic storyboard prototype developed in Phase 2, the CCOT-P engineering prototype was refined in Phase 3 of the OSD ATL Contingency Contractor Optimization project to support strategic planning for contingency contractors. CCOT-P uses a model to optimize the total workforce mix by minimizing the combined total costs for the selected mission scenarios. The model will optimize the match of personnel groups (military, DoD civilian, and contractors) and capabilities to meet the mission requirements as effectively as possible, based on risk, cost, and other requirements.

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Contingency Contractor Optimization Tool Prototype (Phase 3 Extension- Analyst Tutorial)

Bandlow, Alisa B.; Gearhart, Jared L.; Jones, Katherine A.; Adair, Kristin L.; Durfee, Justin D.; Frazier, Christopher R.; Nozick, Linda K.

This tutorial walks the user through analysis examples using the Contingency Contractor Optimization Tool Prototype. The examples are designed to showcase key capabilities of the tool. The main goal of this tutorial is to provide examples of how to use the tool to perform analyses to those users acting in the analyst role. All examples and locations used in the prototype are fictional, but are intended to be realistic. Users reading this manual are expected to have a basic understanding and familiarity with the Contingency Contractor Optimization Tool Prototype. This tutorial includes scenarios for the occurrence of two wars, Prussia and New Granada.

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Estimation of an origin-destination table for U.S. imports of waterborne containerized freight

Transportation Research Record

Jones, Katherine A.; Gearhart, Jared L.; Nozick, Linda K.; Wang, Hao; Frazier, Christopher R.; Jones, Dean A.; Levine, Brian

This paper presents a probabilistic origin-destination table for waterborne containerized imports. The analysis makes use of 2012 Port Import/Export Reporting Service data, 2012 Surface Transportation Board waybill data, a gravity model, and information on the landside transportation mode split associated with specifc ports. This analysis suggests that about 70% of the origin-destination table entries have a coeffcient of variation of less than 20%. This 70% of entries is associated with about 78% of the total volume. This analysis also makes evident the importance of rail interchange points in Chicago, Illinois; Memphis, Tennessee; Dallas, Texas; and Kansas City, Missouri, in supporting the transportation of containerized goods from Asia through West Coast ports to the eastern United States.

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Contingency Contractor Optimization Phase 3 Extension Quick Start Guide: Contingency Contractor Optimization Tool - Engineering Prototype (Release 2.3)

Bandlow, Alisa B.; Adair, Kristin L.; Durfee, Justin D.; Frazier, Christopher R.; Gearhart, Jared L.; Nozick, Linda K.

This Quick Start Guide is an abbreviated version of the Contingency Contractor Optimization Phase 3, User Manual for the Contingency Contractor Optimization Tool engineering prototype. It focuses on providing quick access instructions to the core activities of the two main user roles: Planning Manager and Analyst. Based on an electronic storyboard prototype developed in Phase 2, the Contingency Contractor Optimization Tool engineering prototype was refined in Phase 3 of the OSD ATL Contingency Contractor Optimization to support strategic planning for contingency contractors. The tool uses a model to optimize the total workforce mix by minimizing the combined total costs for the selected mission scenarios. The model will optimize the match of personnel types (military, DoD civilian, and contractors) and capabilities to meet the mission requirements as effectively as possible, based on risk, cost, and other requirements.

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Contingency Contractor Optimization Phase 3 Extension User Manual Contingency: Contractor Optimization Tool - Engineering Prototype (Release 2.3)

Bandlow, Alisa B.; Adair, Kristin L.; Durfee, Justin D.; Frazier, Christopher R.; Gearhart, Jared L.; Nozick, Linda K.

This User Manual provides step-by-step instructions on the Contingency Contractor Optimization Tool's major features. Activities are organized by user role. The Contingency Contractor Optimization project is intended to address former Secretary Gates' mandate in a January 2011 memo and DoDI 3020.41 by delivering a centralized strategic planning tool that allows senior decision makers to quickly and accurately assess the impacts, risks, and mitigation strategies associated with utilizing contract support. Based on an electronic storyboard prototype developed in Phase 2, the Contingency Contractor Optimization Tool engineering prototype was refined in Phase 3 of the OSD ATL Contingency Contractor Optimization project to support strategic planning for contingency contractors. The planning tool uses a model to optimize the Total Force mix by minimizing the combined total costs for the selected mission scenarios. The model will optimize the match of personnel groups (military, DoD civilian, and contractors) and capabilities to meet the mission requirements as effectively as possible, based on risk, cost, and other requirements.

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Contingency Contractor Optimization Phase 3 Quick Start Guide Contingency Contractor Optimization Tool Prototype - Release 2.2

Bandlow, Alisa B.; Adair, Kristin L.; Durfee, Justin D.; Frazier, Christopher R.; Gearhart, Jared L.; Nozick, Linda K.

This Quick Start Guide is an abbreviated version of the Contingency Contractor Optimization Phase 3, User Manual for the Contingency Contractor Optimization Tool engineering prototype. It focuses on providing quick access instructions to the core activities of the two main user roles: Planning Manager and Analyst. Based on an electronic storyboard prototype developed in Phase 2, the Contingency Contractor Optimization Tool engineering prototype was refined in Phase 3 of the OSD ATL Contingency Contractor Optimization to support strategic planning for contingency contractors. The tool uses a model to optimize the total workforce mix by minimizing the combined total costs for the selected mission scenarios. The model will optimize the match of personnel types (military, DoD civilian, and contractors) and capabilities to meet the mission requirements as effectively as possible, based on risk, cost, and other requirements.

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Contingency Contractor Optimization Phase 3 User Manual - Contingency Contractor Optimization Tool Prototype - Release 2.2

Bandlow, Alisa B.; Adair, Kristin L.; Durfee, Justin D.; Frazier, Christopher R.; Gearhart, Jared L.; Nozick, Linda K.

This User Manual provides step-by-step instructions on the Contingency Contractor Optimization Tool's major features. Activities are organized by user role. The Contingency Contractor Optimization project is intended to address former Secretary Gates' mandate in a January 2011 memo and DoDI 3020.41 by delivering a centralized strategic planning tool that allows senior decision makers to quickly and accurately assess the impacts, risks, and mitigation strategies associated with utilizing contract support. Based on an electronic storyboard prototype developed in Phase 2, the Contingency Contractor Optimization Tool engineering prototype was refined in Phase 3 of the OSD ATL Contingency Contractor Optimization project to support strategic planning for contingency contractors. The planning tool uses a model to optimize the Total Force mix by minimizing the combined total costs for the selected mission scenarios. The model will optimize the match of personnel groups (military, DoD civilian, and contractors) and capabilities to meet the mission requirements as effectively as possible, based on risk, cost, and other requirements.

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Optimizing the Configuration of Sensor Networks to Detect Intruders

Sandia journal manuscript; Not yet accepted for publication

Brown, Nathanael J.; Jones, Katherine A.; Nozick, Linda K.; Xu, Ningxiong

This paper focuses on optimizing the selection and configuration of detection technologies to protect a target of interest. The ability of an intruder to simply reach the target is assumed to be sufficient to consider the security system a failure. To address this problem, we develop a game theoretic model of the strategic interactions between the system owner and a knowledgeable intruder. A decomposition-based exact method is used to solve the resultant model.

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Results 26–38 of 38
Results 26–38 of 38