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.
We present a simulation model constructed in collaboration with Intel Corporation to measure and gauge the interaction of non-linear supply chain phenomena (such as waste, uncertainty, congestion, bullwhip, and vulnerability). A representative model that mimics part of Intel's supply chain from fabrication to delivery is modeled using discrete-event simulation in ARENA. A "phenomena evaluation" framework is proposed to link model inputs and supply chain phenomena in order to evaluate supply chain configurations. Using a sample supply chain decision (safety stock level determination) we follow the "phenomena evaluation" framework to illustrate a final recommendation. Results show that our supply chain phenomena evaluation approach helps better illustrate some trade-offs than an evaluation approach based only on the traditional metrics (cost, service, assets etc.).
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.
This paper develops the concept of representational uncertainty to frame a critical challenge in systems engineering. Representational uncertainty arises in complex systems problems when the correct system representation cannot practically be known until some initial work has been undertaken. Drawing on empirical evidence from two very different system design problems, we illustrate the nature and prevalence of representational uncertainty in systems engineering practice. Our findings show that errors in the system representation may lead to wasted design work that explores the wrong tradespaces, expects the wrong value from design choices, and organizes work on the wrong set of decomposed subproblems. We find that mitigating representational uncertainty requires design processes that incorporate discovery of the system properties through a "reality check" early in the design process. We consider the implications for systems engineering processes and tools, and highlight directions for future research.
Testing is critical to mitigating the COVID-19 pandemic, but testing capacity has fallen short of the need in the United States and elsewhere, and long wait times have impeded rapid isolation of cases. Operational challenges such as supply problems and personnel shortages have led to these bottlenecks and inhibited the scale-up of testing to needed levels. This paper uses operational simulations to facilitate rapid scale-up of testing capacity during this public health emergency. Specifically, discrete event simulation models were developed to represent the RT-PCR testing process in a large University of Maryland testing center, which retrofitted high-throughput molecular testing capacity to meet pandemic demands in a partnership with the State of Maryland. The simulation models support analyses that identify process steps which create bottlenecks, and evaluate “what-if” scenarios for process changes that could expand testing capacity. This enables virtual experimentation to understand the trade-offs associated with different interventions that increase testing capacity, allowing the identification of solutions that have high leverage at a feasible and acceptable cost. For example, using a virucidal collection medium which enables safe discarding of swabs at the point of collection removed a time-consuming “deswabbing” step (a primary bottleneck in this laboratory) and nearly doubled the testing capacity. The models are also used to estimate the impact of demand variability on laboratory performance and the minimum equipment and personnel required to meet various target capacities, assisting in scale-up for any laboratories following the same process steps. In sum, the results demonstrate that by using simulation modeling of the operations of SARS-CoV-2 RT-PCR testing, preparedness planners are able to identify high-leverage process changes to increase testing capacity.
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 team's 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 algorithm's 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.
Humanitarian aid agencies deliver emergency supplies and services to people affected by disasters. Scholars and practitioners have developed modeling approaches to support aid delivery planning, but they have used objective functions with little validation as to the trade‐offs among the multiple goals of aid delivery. We develop a method to value the performance of aid delivery plans based on expert preferences over five key attributes: the amount of cargo delivered, the prioritization of aid by commodity type, the prioritization of aid by delivery location, the speed of delivery, and the operational cost. Through a conjoint analysis survey, we measure the preferences of 18 experienced humanitarian logisticians. The survey results quantify the importance of each attribute and enable the development of a piecewise linear utility function that can be used as an objective function in optimization models. The results show that the amount of cargo delivered is the most valued objective and cost the least important. In addition, experts prioritize more vulnerable communities and more critical commodities, but not to the exclusion of others. With these insights and the experts’ utility functions, better humanitarian objective functions can be developed to enable better aid delivery in emergency response.
Large public transportation systems, like that of the Washington Metropolitan Area Transit Authority (WMATA), must appropriately locate response personnel to respond quickly to emergencies throughout the Metrorail system. This is particularly challenging in sprawling and congested metropolitan areas like Washington, DC. The aim of this project is to support the WMATA Office of Emergency Preparedness (OEP) in determining appropriate geographic locations for response personnel with reduced response times to all areas of the Metrorail system. To that end, we developed a simulation model that evaluates response times to emergencies at WMATA Metrorail stations. The model relies on historical data of WMATA emergency incidents to generate probability distributions of incidents, and queries Google Maps application programming interface (API) using Python to provide responder travel times that account for the traffic at that time of day. The user inputs the proposed responder locations (one or several bases) and the tool outputs the response times to a set of emergencies. Resulting response times are then analyzed, visualized, and compared across scenarios, using response time distributions and geographic heat maps, to show response times for the system overall as well as specific stations or geographic areas. In collaboration with the WMATA OEP, we evaluate several scenarios involving moving their current OEP base to a more central location and/or allocating response personnel to different geographic areas. Based on these results, we recommend better locations for WMATA response personnel, which could improve response times by up to 27 minutes or 67% throughout the Metrorail system. While these results are specific to WMATA, the tool could be easily adapted to other public transit systems to support decisions on the location of emergency response personnel.
