Erica Gralla

A Modeling Framework for Interplanetary Supply Chains

As NASA looks ahead to next-generation human space exploration, it is essential to consider the cost of operations and support in order to ensure the development of affordable programs. The life-cycle costs of future exploration ventures can be reduced by focusing on the interplanetary logistics strategy. By leveraging proven supply chain management techniques from the military and commercial sectors and applying them to the unique challenges of human space exploration, space logistics can be streamlined, and mission affordability and robustness can be increased. This paper describes a modeling framework for space logistics which enables description, evaluation, and optimization of various types of logistics strategies. The framework is embodied in SpaceNet, a discrete event simulation and optimization software program.

Case study of a humanitarian logistics simulation exercise and insights for training design

Purpose – The purpose of this paper is to describe and analyze a successful training exercise in detail, through both a practical and a theoretical lens, in order to identify critical aspects of its success and enable others to build upon it; and to capture insights and lessons learned in a framework that will facilitate the design of future trainings for a variety of goals and audiences. Design/methodology/approach – The authors document and analyze the case study of a successful humanitarian logistics training exercise: the World Food Programme’s Logistics Response Team (WFP’s LRT) training. The LRT is described in detail in order to capture the extensive knowledge and experience that went into developing the full-scale, immersive exercise. Findings – The authors evaluate the LRT training through a theoretical lens, considering how it teaches the diverse set of skills required and identifying reasons for its success. The authors contrast the LRT with a light version developed for classroom use, and capt...

The Future of Asset Management for Human Space Exploration: Supply Classification and an Integrated Database

One of the major logistical challenges in human space exploration is asset management. This paper presents observations on the practice of asset management in support of human space flight to date and discusses a functional-based supply classification and a framework for an integrated database that could be used to improve asset management and logistics for future human missions to the Moon, Mars and beyond.

Optimal Launch Vehicle Size Determination for Moon-Mars Transportation Architectures

NASA’s human Lunar and Mars exploration (HLE/HME) program requires a sustainable and affordable transportation system between Earth and Moon/Mars. A crucial element of this system is the launch vehicle. Much debate has centered on the trade between expendable launch vehicles and heavy-lifters; however, arguments to date have been largely qualitative or limited in scope. This paper provides a quantitative enumeration of the launch vehicle trade space (in terms of both cost and risk), based on a generalizable process for generating launch manifests from transportation architectures (sets of vehicles for carrying out Lunar/Mars missions). For the baseline HLE/HME architecture considered here, an optimal launch vehicle size is found at approximately 82 metric tons; a 28-mt EELV emerges as another good option. The results show optimal launch vehicle selection is highly dependent on the transportation architecture. Therefore, launch vehicle selection should be considered an integral part of the design of the Moon/Mars exploration transportation system.

Remote Terrestrial Sites as Operational/Logistics Analogs for Moon/Mars Bases: the Haughton Mars Project

*† ‡ § ** Mission planners for future human space exploration enterprises face several challenges in the area of operations, including coordinating the logistics and resupply of far-flung planetary bases. A number of logistics methods have been perfected by commercial and military experts, but these are not well understood in the context of space exploration. This paper describes a field expedition to a Mars analog site in the high Arctic, at which terrestrial logistics methods were tested in the context of (analog) planetary exploration. A comprehensive comparison is drawn between the logistics scenarios at HMP and a potential lunar or Mars base, in order to determine the extent of the analogy between them. It appears that the analogy is quite good in certain categories of supplies and shipment, but breaks down in others. When certain straightforward differences are accounted for, the data gathered from HMP can be used to validate and inform planetary base logistics models in support of future human lunar and Mars exploration.

Humanitarian Transportation Planning: Evaluation of Practice-Based Heuristics and Recommendations for Improvement

Abstract Transportation bottlenecks are a common and critical problem in humanitarian response. There is a need for better planning and prioritization of vehicles to transport humanitarian aid to affected communities. Optimization approaches have been developed for transportation planning, but adoption has been limited, due in part to the difficulty of implementation. This paper develops the basis for an easily implementable decision support tool by building on current planning practices in the humanitarian sector. We draw on an observational study to describe current planning practices, then develop heuristic algorithms that represent the observed planning processes, and compare their solutions to each other and to those of a mixed-integer linear program. We identify key weaknesses to guide the development of more sophisticated heuristics or optimization models that fit with current planning practices. We also find that a simple practice-driven heuristic performs well when it prioritizes deliveries based on destination priority or distance, and we argue that automating such a heuristic in a decision support tool would maintain the simplicity and transparency to enable implementation in practice, and improve planning by saving time and increasing accuracy and consistency.

Identification of Subproblems in Complex Design Problems: A Study of Facility Design

Many design problems are too difficult to solve all at once; therefore, design teams often decompose these problems into more manageable subproblems. While there has been much interest in engineering design teams, no standard method has been developed to understand how teams solve design problems. This paper describes a method for analyzing a team’s design activities and identifying the subproblems that they considered. This method uses both qualitative and quantitative techniques; in particular, it uses association rule learning to group variables into subproblems. We used the method on data from ten teams who redesigned a manufacturing facility. This approach provides researchers with a clear structure for using observational data to identify the problem decomposition patterns of human designers. INTRODUCTION Decomposition is an important component of design processes. When solving a complex design problem, designers often make decisions in stages because the problem may be too complex to solve all at once. To enable this approach, the problem is decomposed into manageable subproblems. The specific set of subproblems used by designers to solve complex problems may affect the quality of their solutions. These ∗Address all correspondence to this author. decomposition strategies may be a predefined formal process set up by the organization or a set of informal activities and responsibilities determined by a team of designers. In either case, various problem decomposition strategies could be used, resulting in final solutions of varying quality. Therefore, identifying decomposition strategies used by design teams when solving complex design problems will give us the ability to compare how various strategies affect the quality of the outcome. Identifying the decomposition strategies of design teams requires observing and analyzing their activities. However, there is no standard method to “capture” the decompositions used by teams of human designers. Designers typically do not explicitly describe the subproblems that they are solving [1]; the decomposition must be inferred based on their discussions about the variables that need to be determined during a design process. Although verbal protocol analysis is often used to describe the activities of a design team [2], there is no standard method to use a design team’s discussions to identify different decomposition patterns of multiple teams over time. Therefore, we developed such a method, which results in a set of subproblems that group together variables that the teams likely considered together, and a timeline that shows which subproblems that team was working on at each point in time. Although it is general and can be applied to other design domains, the method presented here was developed as part 1 Copyright

Logistics Information Systems for Human Space Exploration

Space agencies around the world are gearing up for new human space exploration missions. In order to ensure that such programs are sustainable, it is worthwhile to examine the lessons learned from past experiences with space logistics and supply chain management. This paper offers an overview of the current state of the art in logistics management for space exploration focused on information systems, and highlights some emerging technologies that have the potential to significantly improve both the study and operation of space logistics systems.

Using clustering algorithms to identify subproblems in design processes

Designers work in teams to design complex systems. They separate the design problem into subproblems and solve the smaller, more manageable subproblems. Because this affects the overall quality of their design, it is important to understand how teams decompose system design problems, which will ultimately enable future research on how to design better design processes. We studied teams of experts solving two different facility design problems. We developed a novel approach that combines qualitative and quantitative techniques. It records a teams discussion, identifies the design variables using qualitative coding techniques, and groups these variables into subproblems. A subproblem is a set of variables that are considered together. We evaluated four clustering algorithms that group the coded variables into subproblems. This paper discusses the data collection, the clustering algorithms, and the evaluation techniques. The the algorithms generated similar but not identical clusters, and no algorithms clusters consistently out-performed the others on quantitative measures of cluster quality. The clusters do provide insights into the subproblems that the design team solved.

A Capabilities-Based Framework for Disaster Response Exercise Design and Evaluation: Findings from Oil Spill Response Exercises

Abstract The responder community must be ready to respond quickly and effectively in the event of a disaster. In order to maintain readiness, many disaster response communities exercise their response capabilities on a regular basis. The critical challenge is to design, conduct, and evaluate exercises in a manner that effectively tests responders’ readiness and generates lessons that can improve readiness. This paper describes a framework to enable assessment of response readiness through evaluation of critical capabilities in exercises. It was developed for oil spill response based on the observation and analysis of four response exercises. The framework (1) identifies critical capabilities that lead to readiness for spill response, and maps them to (2) exercise design components that test each capability and (3) evaluation measures to evaluate each capability within an exercise. The framework enables continuous improvement by linking the evaluation of exercises to the critical capabilities required of an oil spill response organization; by evaluating the performance of specific capabilities, areas for improvement are clearly identified and can be re-tested in a future exercise. While the findings are necessarily specific to oil spill response, the principles apply to any disaster response context.