Craig R. Lawton

Robust optimization for fleet planning under uncertainty

We create a formulation and a solution procedure for fleet sizing under uncertainty in future demands and operating conditions. The formulation focuses on robust optimization, using a partial moment measure of risk. This risk measure is incorporated into the expected recourse function of a two-stage stochastic programming formulation, and stochastic decomposition is used as a solution procedure. A numerical example illustrates the importance of including uncertainty in the fleet sizing problem formulation, and the nature of the fundamental tradeoff between acquiring more vehicles and accepting the risk of potentially high costs if insufficient resources are available.

Logistics planning under uncertainty for disposition of radioactive wastes

The US Department of Energy (DOE) faces an enormous environmental remediation challenge involving highly radioactive wastes at former weapons production facilities. The purpose of this analysis is to focus on equipment acquisition and fleet sizing issues related to transportation of wastes from remediation sites to disposal sites. Planning for the transportation of these wastes must be done with recognition of important uncertainties related to overall quantities of waste to be moved, the rate at which the wastes will be prepared for transport, and the certification of suitable transportation containers for use in the effort. However, deadlines for completion of the effort have already been set by the political process, without much regard for these uncertainties. To address this fleet sizing problem, we have created a robust optimization model that focuses on equipment investment decisions. Through this robust optimization, we illustrate how modeling can be used to explore the effects of uncertainty on the equipment acquisition strategy. The disposition of radioactive wastes from DOE sites is an important illustration of a category of problems where equipment investments must be made under conditions of considerable uncertainty. The methodology illustrated in this paper can be applied to this general class of problems.

Exploring Human-Technology Interaction in Layered Security Military Applications

System-of-systems modeling has traditionally focused on physical systems rather than humans, but recent events have proved the necessity of considering the human in the loop. As technology becomes more complex and layered security continues to increase in importance, capturing humans and their interactions with technologies within the system-of-systems will be increasingly necessary. After an extensive job-task analysis, a novel type of system-of-systems simulation model has been created to capture the human-technology interactions on an extra-small forward operating base to better understand performance, key security drivers, and the robustness of the base. In addition to the model, an innovative framework for using detection theory to calculate d’ for individual elements of the layered security system, and for the entire security system as a whole, is under development.

Human performance modeling in system of systems analytics

The Department of Defense has identified that integrating the human element into large scale System of Systems (SoS) models is a significant challenge that remains unaddressed. Failure in doing so leads to significant limitations in our SoS analytical capabilities as human performance is a large contributor to the performance of a SoS. The primary challenge is that, in most SoS domains, the problems being analyzed are large in scale. Conversely, most Human Performance Modeling (HPM) initiatives look at integrating detailed cognitive models that capture fine grained details of human perception, decision making, and response with detailed systems models and simulations (e.g., Lebiere et al., 2003). It is not feasible to integrate such fine grained cognitive models with systems models and perform SoS scale analysis. This paper documents a capability that integrates HPM into a large scale SoS simulation toolset and demonstrates the utility of the toolset.

Incorporating high-fidelity networked communications modeling in the evaluation of large-scale system-of-systems

Military force structures are becoming increasingly complex and net-centric as new technologies are developed and deployed. As such, there is an ever increasing need to provide system-of-systems (SoS) analysis tools to assist in evaluating a military combat teams survivability, lethality, sustainment and logistics. Evaluation of networked communications as part of this analysis is often overlooked due to the complexity and scope of the communications problem. A team lead by Sandia National Laboratories (SNL) is developing net-centric SoS modeling and analysis capabilities by integrating high-fidelity networked communications modeling with a large-scale, SoS simulation and analysis tool. The combined modeling approach brings together the strengths of each tool to achieve complex, net-centric SoS analysis. This paper describes the combined modeling and simulation(M&S) capability and presents example analysis results from the completed phase-one effort. Experiments conducted show the effect of functional communications on a combat teams survivability at a SoS-level. The result is an approach that bridges the gap in high-fidelity communications modeling with logistics and survivability for large-scale SoS problems.

Maximizing the U.S. Army’s Future Contribution to Global Security Using the Capability Portfolio Analysis Tool (CPAT)

Recent budget reductions have posed tremendous challenges to the U.S. Army in managing its portfolio of ground combat systems (tanks and other fighting vehicles), thus placing many important programs at risk. To address these challenges, the Army and a supporting team developed and applied the Capability Portfolio Analysis Tool (CPAT) to optimally invest in ground combat modernization over the next 25–35 years. CPAT provides the Army with the analytical rigor needed to help senior Army decision makers allocate scarce modernization dollars to protect soldiers and maintain capability overmatch. CPAT delivers unparalleled insight into multiple-decade modernization planning using a novel multiphase mixed-integer linear programming technique and illustrates a cultural shift toward analytics in the Army’s acquisition thinking and processes. CPAT analysis helped shape decisions to continue modernization of the

Evaluating communications system performance effects at a system of systems level

10 billion Stryker family of vehicles (originally slated for cancellation) and to strategically reallocate over

Applying system of systems and systems engineering to the military modernization challenge

20 billion to existing modernization programs by not pursuing the Ground Combat Vehicle program as originally envisioned. More than 40 studies have been completed using CPAT, applying operations research methods to optimally prioritize billions of taxpayer dollars and allowing Army acquisition executives to base investment decisions on analytically rigorous evaluations of portfolio trade-offs.

Advanced Production Planning Models

The complexity of net-centric system of systems (SoS) being fielded today has the military leadership increasingly dependent on modeling and simulation (M&S) tools for evaluating performance. Several types of M&S tools are required to model different aspects of military systems, yet these tools often have different computational fidelities in terms of time and scale. Current approaches using direct information transfer between M&S tools, such as High Level Architecture (HLA) and MATREX, do not provide the mechanisms for disparate tools to make direct use of each others information [1], [2]. Thus, many military SoS analyses assume perfect communications, an unrealistic assumption that leaves a gap for conducting more comprehensive analyses for large-scale, net-centric SoS problems. This research addresses this gap by developing general purpose methodologies to bridge the gap between diverse M&S tools resulting in a capability that enables military decision makers to evaluate comms system performance effects at a SoS level [3]. This paper discusses the methodology, including parameter selection, data generation, surrogate modeling and SoS simulation results.

The Pantex Process model: Formulations of the evaluation planning module

The challenge of modernizing the current force of military combat systems is one of competing priorities. While planning for future capability requirements and modernization efforts, military acquisition program managers must also consider the immediate demands from the field as the United States is engaged on multiple fronts. These competing demands, along with the challenge of managing large groups of complex systems, motivate the need for an expedient and repeatable systems engineering process. The U.S. Armys Program Executive Office for Ground Combat Systems developed such an approach to support analysis of acquisition programs to meet future capability needs. This paper describes the systems engineering approach and illustrates its usefulness.