Publications

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The purpose of the Scope, Complexity, Options, Risks, Excursions (SCORE) model is to estimate the relative complexity of design variants of future warhead options. The results of this model allow those considering these options to understand the complexity tradeoffs between proposed warhead options. The core idea of SCORE is to divide a warhead option into a well- defined set of scope elements and then estimate the complexity of each scope element against a well understood reference system. The uncertainty associated with estimates can also be captured. A weighted summation of the relative complexity of each scope element is used to determine the total complexity of the proposed warhead option or portions of the warhead option (i.e., a National Work Breakdown Structure code). The SCORE analysis process is a growing multi-organizational Nuclear Security Enterprise (NSE) effort, under the management of the NA- 12 led Enterprise Modeling and Analysis Consortium (EMAC), that has provided the data elicitation, integration and computation needed to support the out-year Life Extension Program (LEP) cost estimates included in the Stockpile Stewardship Management Plan (SSMP).

The purpose of the Scope, Complexity, Options, Risks, Excursions (SCORE) model is to estimate the relative complexity of design variants of future warhead options, resulting in scores. SCORE factors extend this capability by providing estimates of complexity relative to a base system (i.e., all design options are normalized to one weapon system). First, a clearly defined set of scope elements for a warhead option is established. The complexity of each scope element is estimated by Subject Matter Experts (SMEs), including a level of uncertainty, relative to a specific reference system. When determining factors, complexity estimates for a scope element can be directly tied to the base system or chained together via comparable scope elements in a string of reference systems that ends with the base system. The SCORE analysis process is a growing multi-organizational Nuclear Security Enterprise (NSE) effort, under the management of the NA-12 led Enterprise Modeling and Analysis Consortium (EMAC). Historically, it has provided the data elicitation, integration, and computation needed to support the out-year Life Extension Program (LEP) cost estimates included in the Stockpile Stewardship Management Plan (SSMP).

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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.

This paper develops a two-stage stochastic program and solution procedure to optimize the selection of seismic retrofit strategies to increase the resilience of electric power systems against earthquake hazards. The model explicitly considers the range of earthquake events that are possible and, for each, an approximation of the distribution of damage experienced. This is important because electric power systems are spatially distributed and so their performance is driven by the distribution of component damage. We test this solution procedure against the nonlinear integer solver in LINGO 13 and apply the formulation and solution strategy to the Eastern Interconnection, where seismic hazard stems from the New Madrid seismic zone.

This report has been written for the Department of Energy’s Energy Policy and Systems Analysis Office to inform their writing of the Quadrennial Energy Review in the area of energy resilience. The topics of measuring and increasing energy resilience are addressed, including definitions, means of measuring, and analytic methodologies that can be used to make decisions for policy, infrastructure planning, and operations. A risk-based framework is presented which provides a standard definition of a resilience metric. Additionally, a process is identified which explains how the metrics can be applied. Research and development is articulated that will further accelerate the resilience of energy infrastructures.

The goal of Phase 3 the OSD ATL Contingency Contractor Optimization (CCO) project is to create an engineering prototype of a tool for the contingency contractor element of total force planning during the Support for Strategic Analysis (SSA). An optimization model was developed to determine the optimal mix of military, Department of Defense (DoD) civilians, and contractors that accomplishes a set of user defined mission requirements at the lowest possible cost while honoring resource limitations and manpower use rules. An additional feature allows the model to understand the variability of the Total Force Mix when there is uncertainty in mission requirements.

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Currently, much of protection planning is conducted separately for each infrastructure and hazard. Limited funding requires a balance of expenditures between terrorism and natural hazards based on potential impacts. This report documents the results of a Laboratory Directed Research & Development (LDRD) project that created a modeling framework for investment planning in interdependent infrastructures focused on multiple hazards, including terrorism. To develop this framework, three modeling elements were integrated: natural hazards, terrorism, and interdependent infrastructures. For natural hazards, a methodology was created for specifying events consistent with regional hazards. For terrorism, we modeled the terrorists actions based on assumptions regarding their knowledge, goals, and target identification strategy. For infrastructures, we focused on predicting post-event performance due to specific terrorist attacks and natural hazard events, tempered by appropriate infrastructure investments. We demonstrate the utility of this framework with various examples, including protection of electric power, roadway, and hospital networks.

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The goal of Phase 3 the OSD ATL Contingency Contractor Optimization (CCO) project is to create an engineering prototype of a tool for the contingency contractor element of total force planning during the Support for Strategic Analysis (SSA). An optimization model was developed to determine the optimal mix of military, Department of Defense (DoD) civilians, and contractors that accomplishes a set of user defined mission requirements at the lowest possible cost while honoring resource limitations and manpower use rules. An additional feature allows the model to understand the variability of the Total Force Mix when there is uncertainty in mission requirements.

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This paper presents an extension to generalized blockmodeling where there are more than two types of objects to be clustered based on valued network data. We use the ideas in homogeneity block modeling to develop an optimization model to perform the clustering of the objects and the resulting partitioning of the ties so as to minimize the inconsistency of an empirical block with an ideal block. The ideal block types used in this modeling are null, complete and a new type that is related to that developed in Ziberna (2007). Three case studies are presented, two based on the Southern Women dataset (Davis et al. 1941) and a third based on passenger air travel in the Continental United States.

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A model of malicious intrusions in infrastructure facilities is developed, using a network representation of the system structure together with Markov models of intruder progress and strategy. This structure provides an explicit mechanism to estimate the probability of successful breaches of physical security, and to evaluate potential improvements. Simulation is used to analyze varying levels of imperfect information on the part of the intruders in planning their attacks. An example of an intruder attempting to place an explosive device on an airplane at an airport gate illustrates the structure and potential application of the model.

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>This report describes the innovative modeling approach developed as a result of a 3-year Laboratory Directed Research and Development project. The overall goal of this project was to provide an effective suite of solvers for advanced production planning at facilities in the nuclear weapons complex (NWC). We focused our development activities on problems related to operations at the DOE's Pantex Plant. These types of scheduling problems appear in many contexts other than Pantex--both within the NWC (e.g., Neutron Generators) and in other commercial manufacturing settings. We successfully developed an innovative and effective solution strategy for these types of problems. We have tested this approach on actual data from Pantex, and from Org. 14000 (Neutron Generator production). This report focuses on the mathematical representation of the modeling approach and presents three representative studies using Pantex data. Results associated with the Neutron Generator facility will be published in a subsequent SAND report. The approach to task-based scheduling described here represents a significant addition to the literature for large-scale, realistic scheduling problems in a variety of production settings.

This paper describes formulations of the Evaluation Planning Module that have been developed since its inception. This module is one of the core algorithms in the Pantex Process Model, a computerized model to support production planning in a complex manufacturing system at the Pantex Plant, a US Department of Energy facility. Pantex is responsible for three major DOE programs -- nuclear weapons disposal, stockpile evaluation, and stockpile maintenance -- using shared facilities, technicians, and equipment. The model reflects the interactions of scheduling constraints, material flow constraints, and the availability of required technicians and facilities.