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DOE OSTI2024

Optimization Model and Algorithm for Capacity Planning and Operation of Reliable and Carbon-neutral Power Systems with High Penetration of Renewable Generation

In this work, we propose a Generalized Disjunctive Programming (GDP) model that optimizes both long-term capacity planning (such as the number and size of dispatchable/renewable generators, batteries, and transmission lines) and hourly operation (such as on/off schedules of dispatchable generators, power output from each generator, and power flow) to maximize power system reliability while minimizing total cost and CO2 emissions.

Cho, Seolhee↗

DOE OSTI2026

CRADA Number NFE-24-10110 with Qubit Engineering Inc. (CRADA Final Report)

Over the past year, the Qubit Engineering team has pushed the frontiers of power‑grid optimization, working in close collaboration with Oak Ridge National Laboratory (ORNL) and the Tennessee Valley Authority (TVA). Their progress is reflected in three newly submitted conference papers, “Unified Relational GNN Architecture for AC Optimal Power Flow Calculations in Electric Grids,” “Graph‑Based Attention Mechanisms for Solving the AC Optimal Power Flow Problem in Electrical‑Power Networks,” and “Enhanced Power‑Grid Maintenance Planning and Quantum‑Inspired Combinatorial Prospects.” These publications showcase state‑of‑the‑art graph‑neural‑network methods for AC‑OPF and novel quantum‑inspired heuristics for maintenance scheduling. Beyond the academic results, the Qubit team has converted the research into two production‑grade tools built on TVA data: Neuro‑Grid, an AI‑driven power‑flow simulator that provides instant, interactive full‑grid load‑flow visualizations, and Quanta‑Grid, a quantum‑inspired maintenance‑scheduling engine to support logistics optimization for power utilities. Together, these advances demonstrate how Qubit’s partnership with ORNL and TVA is delivering practical, physics‑grounded analytics for next‑generation grid management.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1991

A method for interference mitigation in space communications scheduling

Increases in the number of user spacecraft and data rates supported by NASA's Tracking and Data Relay Satellite System (TDRSS) in the S and Ku bands could result in communications conflicts due to mutual interference. A method to mitigate interference while minimizing unnecessary scheduling restrictions on both TDRSS network and user resources, based on consideration of all relevant communications parameters, was developed. The steps of this method calculate required separation angles at TDRS and produce interference intervals, which can be used in the production of schedules free of unacceptable interference. The method can also be used as a basis for analysis, evaluation, and optimization of user schedules with respect to communications performance. Described here are the proposed method and its potential application to scheduling in space communications. Test cases relative to planned missions, including the Earth Observing System, the Space Station Manned Base, and the Space Shuttle are discussed.

Wong, Yen F.↗

NASA NTRS1991

A method for interference mitigation in space communications scheduling

Increases in the number of user spacecraft and data rates supported by NASA's Tracking and Data Relay Satellite System (TDRSS) in the S and Ku bands could result in communications conflicts due to mutual interference. A method to mitigate interference while minimizing unnecessary scheduling restrictions on both TDRSS network and user resources, based on consideration of all relevant communications parameters, was developed. The steps of this method calculate required separation angles at TDRSS and produce interference intervals, which can be used in the production of schedules free of unacceptable interference. The method also can be used as the basis for analysis, evaluation, and optimization of user schedules with respect to communications performance. Described here are the proposed method and its potential application to scheduling in space communications. Test cases relative to planned missions, including the Earth Observing System, the Space Station Manned Base, and the Space Shuttle are discussed.

Wong, Yen F.↗

DOE OSTI2025

Enhanced Power Grid Maintenance Planning and Quantum-Inspired Combinatorial Prospects

Efficient and reliable scheduling of maintenance for power generation and transmission infrastructure is essential for minimizing operational costs and ensuring grid stability. This paper introduces an integrated optimization framework for coordinated maintenance scheduling of generators and transmission lines under resource and reliability constraints. The model minimizes a composite cost function including maintenance and generation costs, as well as penalties for delayed maintenance, while satisfying N−1 security constraints, operational limits, and crew availability. Case studies on the IEEE 300-bus test system demonstrate the effectiveness of the proposed approach in producing feasible and cost-effective maintenance schedules. To address scalability and combinatorial complexity, the model is mapped into a Quadratic Unconstrained Binary Optimization (QUBO) problem, enabling exploration of solution approaches based on Quantum Imaginary Time Evolution (QITE). While the QUBO reformulation provides a foundation for future quantum-inspired optimization, this study focuses primarily on the development and demonstration of the classical optimization framework and illustrates the potential applicability of QITE in large-scale maintenance scheduling.

Chen, Yang [ORNL] (ORCID:0000000271693874)↗

NASA NTRS2005

Software for Optimizing Plans Involving Interdependent Goals

A computer program enables construction and optimization of plans for activities that are directed toward achievement of goals that are interdependent. Goal interdependence is defined as the achievement of one or more goals affecting the desirability or priority of achieving one or more other goals. This program is overlaid on the Automated Scheduling and Planning Environment (ASPEN) software system, aspects of which have been described in a number of prior NASA Tech Briefs articles. Unlike other known or related planning programs, this program considers interdependences among goals that can change between problems and provides a language for easily specifying such dependences. Specifications of the interdependences can be formulated dynamically and provided to the associated planning software as part of the goal input. Then an optimization algorithm provided by this program enables the planning software to reason about the interdependences and incorporate them into an overall objective function that it uses to rate the quality of a plan under construction and to direct its optimization search. In tests on a series of problems of planning geological experiments by a team of instrumented robotic vehicles (rovers) on new terrain, this program was found to enhance plan quality.

Estlin, Tara↗

NASA NTRS1995

Planning as a Precursor to Scheduling for Space Station Payload Operations

Contemporary schedulers attempt to solve the problem of best fitting a set of activities into an available timeframe while still satisfying the necessary constraints. This approach produces results which are optimized for the region of time the scheduler is able to process, satisfying the near term goals of the operation. In general the scheduler is not able to reason about the activities which precede or follow the window into which it is inputs to scheduling so that the intermediate placing activities. This creates a problem for operations which are composed of many activities spanning long durations (which exceed the scheduler's reasoning horizon) such as the continuous operations environment for payload operations on the Space Station. Not only must the near term scheduling objectives be met, but somehow the results of near term scheduling must be made to support the attainment of long term goals.

Howell, Eric↗

NASA NTRS1987

Asset - An application in mission automation for science planning

Recent advances in computer technology were used to great advantage in planning science observation sequences for the Voyager 2 encounter with Uranus in 1986. Despite a loss of experienced personnel, a challenging schedule, workforce limitations, and the complex nature of the Uranus encounter itself, the resultant science observation timelines were the most highly optimized of the five Voyager encounters with the outer planets. In part, this was due to the development of a microcomputer-based system, called ASSET (Automated Science Sequence Encounter Timelines generator), which was used to design those science observation timelines. This paper details the development of that system. ASSET demonstrates several features essential to the design of the first expert systems for science planning which will be applied for future missions.

Finnerty, D. F.↗

NASA NTRS2020

Characterization of International Space Station Crew Members' Workload Contributing to Fatigue, Sleep Disruption and Circadian De-synchronization

The focus of this paper is to characterize how the International Space Station (ISS) crewmembers’ workload may be contributing to sleep loss, circadian misalignment and fatigue. Both sleep quantity and subjective sleep quality are reduced in ISS crewmembers (Barger, Flynn-Evans, Kubey, Walsh, Ronda, Wang, Wright, & Czeisler, 2014). Evidence indicates that the use of hypnotic drugs does not appear to promote extended sleep duration. Because sleep is often driven by psychosocial as well as somatic attributes, traditional therapies may only partially moderate the problem for some individuals. Accordingly, searching for additional abatement tactics is a sensible plan. Scientific studies have shown that sleep can be disrupted from work-related stressors. On the ISS, to optimize their time, the crewmembers follow prescribed, ambitious and rigorous schedules with shared deadlines. Here it will be argued that these human capital leveraging techniques may be undermining the astronaut’s sleep, which could negatively impact performance. Along with half of the Earth-bound working population (Paoli & Merllié, 2001), ISS crewmembers may not be adequately recovering from their workload. This paper begins with a characterization of the working conditions of ISS crewmembers, describes the development of rigorous schedules and portrays a typical workday. Terrestrially-based research is compiled to describe how full and partial sleep deprivation affect physical and cognitive performance and how ISS work characteristics may disrupt sleep and subsequent performance. The literature points toward potential solutions to astronaut fatigue that is related to their workload. Finally, throughout the text, evidence is provided from semi-structured interviews, biographies and textual databases to support the argument that astronaut workload is contributing to their sleep loss and fatigue and that research, development and mitigation strategies should focus on enhancing the restorative process.

fatigue↗

DOE OSTI2025

Methods and Tools To Assess Robustness of Nuclear Plant Outages

Refueling outages are one of the most challenging phases in a nuclear power plant (NPP) operating cycle. Refueling outages are extremely costly for an NPP due to the large amount of required resources and because of lost revenue due to plant being off the grid. Outage durations have steadily decreased across the industry over that last few decades primarily due to improved planning and coordination, but there are still many plants that struggle to meet the performance metrics accomplished by other utilities. Schedule resilience is one of the issues. NPP outages require scheduling thousands of activities within 30 days on average. Despite detailed planning, once the outage starts, numerous emergent issues typically appear along with schedule delays requiring continuous replanning and adjustment. When schedule disruption occurs during an outage, plant staff make urgent efforts to recover but are often not able to maintain the planned outage duration. These outage delays can cost a utility several million dollars per day. Tools that could help outage schedulers create a more resilient schedule and allow them to optimally reschedule emergent work could significantly reduce outage delays. One key aspect of creating a resilient schedule is to have accurate estimates for activity duration. Another important outage scheduling capability is the ability to schedule emergent work with minimal disruption. This paper focuses on developing tools and methods to support NPPs with outage schedule optimization and it describes the initial development of tools to support outage management that leverage computational and machine learning methods.

97 - MATHEMATICS AND COMPUTING↗

NASA NTRS1994

Generic mission planning and scheduling: The AXAF solution

During SpaceOps 92 the idea of generic mission planning concepts for space astronomy missions, that could be applied to future missions in order to simplify software development, was introduced. It was proposed that mission planning systems could be decomposed into functional elements that could be standardized and then organized into optimal functional flows for each individual mission. In addition, it was further suggested that these flows themselves could be reduced to a small set of possibilities by describing them in terms of generic mission type, such as manned, unmanned, high orbit, low orbit, etc. The Advanced X-ray Astrophysics Facility (AXAF), planned for launch in the latter part of '98, represents the first application of this idea on an unmanned mission. This paper examines the AXAF Mission Planning and Scheduling concept in light of the generic system theory. Each functional element is evaluated according to AXAF characteristics and requirements and then compared to its generic counterpart. Functional flow considerations are then derived from the overall AXAF mission planning concept to determine the viability and sensitivity of the generic flow to actual requirements. The results of this analysis are then used to update the generic system concept and to define the level of commonality and core system components that are practical to achieve across multiple missions.

Guffin, O. T.↗

NASA NTRS1994

Generic astronomy mission planning and scheduling: The AXAF solution

During SpaceOps 92 the idea of generic mission planning concepts for space astronomy missions, that could be applied to future missions in order to simplify software development, was introduced. It was proposed that mission planning systems could be decomposed into functional elements that could be standardized and then organized into optimal functional flows for each individual mission. In addition, it was further suggested that these flows themselves could be reduced to a small set of possibilities by describing them in terms of generic mission type, such as manned, unmanned, high orbit, low orbit, etc. The Advanced X-ray Astrophysics Facility (AXAF), planned for launch in the latter part of 1998, represents the first application of this idea on an unmanned mission. This paper examines the AXAF Mission Planning and Scheduling concept in light of the generic system theory. Each functional element is evaluated according to AXAF characteristics and requirements and then compared to its generic counterpart. Functional flow considerations are then derived from the overall AXAF mission planning concept to determine the viability and sensitivity of the generic flow to actual requirements. The results of this analysis are then used to update the generic system concept and to define the level of commonality and core system components that are practical to achieve across multiple missions.

Guffin, O. T.↗

NASA NTRSDate not supplied

Analysis and Optimization of Test Plans for Advanced Exploration Systems Reliability and Supportability

Future crewed exploration missions beyond Low Earth Orbit (LEO) will operate farther from Earth and be logistically isolated for longer than any previous human spaceflight mission. Under these conditions, supportability and reliability willbestronger drivers of mission mass and risk than they have been in the past. Items with high failure rates, or uncertain failure rates, can result in high spares mass requirements and/or high risk on deep space missions. Testing is a critical element of system development which provides the opportunity to identify and resolve design issues, defects, or other failure modes before they cause problems during a mission. Reliability growth programs can reduce failure rates by identifying and remove failure modes via design changes, and long-duration life testing can provide valuable data to reduce failure rate estimate uncertainty and verify (to some level of confidence) that components are as reliable as expected. Testing activities take time and resources, however, and must be incorporated into program plans in order to be fully effective. This paper presents an integrated reliability test plan analysis and optimization methodology, which has been used to inform Advanced Exploration Systems (AES) Life Support Systems (LSS) ground test planning for future missions. The methodology determines the optimal number of test units to purchase and allocation of test time –split between reliability growth and uncertainty reduction testing –across a given set of items in order to minimize spares mass for a given mission under constraints on total test cost and schedule. Model outputs also include expected spares mass after testing and the expected number of modifications or refurbishments during testing, both of which can inform program planning. Discussion of the model, conclusions, and future work are also presented.

Testing↗

NASA NTRSDate not supplied

Analysis and Optimization of Test Plans for Advanced Exploration Systems Reliability and Supportability

Testing↗

NASA NTRS1984

Practical considerations in aeroelastic design

The structural design process for large transport aircraft is described. Critical loads must be determined from a large number of load cases within the flight maneuver envelope. The structural design is also constrained by considerations of producibility, reliability, maintainability, durability, and damage tolerance, as well as impact dynamics and multiple constraints due to flutter and aeroelasticity. Aircraft aeroelastic design considerations in three distinct areas of product development (preliminary design, advanced design, and detailed design) are presented and contrasted. The present state of the art is challenged to solve the practical difficulties associated with design, analysis, and redesign within cost and schedule constraints. The current practice consists of largely independent engineering disciplines operating with unorganized data interfaces. The need is then demonstrated for a well-planned computerized aeroelastic structural design optimization system operating with a common interdisciplinary data base. This system must incorporate automated interfaces between modular programs. In each phase of the design process, a common finite-element model for static and dynamic optimization is required to reduce errors due to modeling discrepancies. As the design proceeds from the simple models in preliminary design to the more complex models in advanced and detailed design, a means of retrieving design data from the previous models must be established.

Rommel, B. A.↗

DOE OSTI2024

Leverage modern artificial intelligence (AI) enabled systems for waste reduction

Manufacturing industries continue to face challenges in reducing waste, as upstream strategies such as source reduction and product redesign require a deeper understanding of processes compared to conventional recycling methods. Recent advancements in artificial intelligence (AI) and machine learning (ML) have opened new opportunities to integrate modern computational techniques with traditional waste minimization strategies. This paper explores AI-enabled approaches for product redesign, source reduction, and recycling that can significantly reduce waste generation while improving efficiency and sustainability. AI-driven material substitution and lightweighting in product design enable discovery of novel materials with optimized properties, reducing waste without compromising performance. Reinforcement learning models optimize process parameters, raw material specifications, and machine sequencing to minimize production losses, while Industrial Internet of Things (IIoT) systems paired with AI analytics enhance real-time waste tracking, predictive maintenance, and quality inspection. Furthermore, AI-based demand forecasting and production planning reduce overproduction and excess inventory, as demonstrated in industrial applications. In recycling, ML-powered pattern recognition and robotic sorting technologies achieve higher accuracy in waste segregation, directly improving recycling efficiency. Complementary solutions such as smart bins and AI-enabled waste pickup scheduling optimize collection logistics, reducing both costs and emissions. Although implementation requires upfront investment in infrastructure and training, the long-term benefits include higher material efficiency, reduced waste, improved product quality, and stronger sustainability outcomes across the supply chain. By leveraging AI-enabled systems, manufacturers can align waste minimization efforts with circular economy principles, creating scalable solutions for both industry and society.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2004

Software for Optimizing Quality Assurance of Other Software

Software assurance is the planned and systematic set of activities that ensures that software processes and products conform to requirements, standards, and procedures. Examples of such activities are the following: code inspections, unit tests, design reviews, performance analyses, construction of traceability matrices, etc. In practice, software development projects have only limited resources (e.g., schedule, budget, and availability of personnel) to cover the entire development effort, of which assurance is but a part. Projects must therefore select judiciously from among the possible assurance activities. At its heart, this can be viewed as an optimization problem; namely, to determine the allocation of limited resources (time, money, and personnel) to minimize risk or, alternatively, to minimize the resources needed to reduce risk to an acceptable level. The end result of the work reported here is a means to optimize quality-assurance processes used in developing software.

Feather, Martin↗

NASA NTRS2000

Wind Tunnel Management and Resource Optimization: A Systems Modeling Approach

Time, money, and, personnel are becoming increasingly scarce resources within government agencies due to a reduction in funding and the desire to demonstrate responsible economic efficiency. The ability of an organization to plan and schedule resources effectively can provide the necessary leverage to improve productivity, provide continuous support to all projects, and insure flexibility in a rapidly changing environment. Without adequate internal controls the organization is forced to rely on external support, waste precious resources, and risk an inefficient response to change. Management systems must be developed and applied that strive to maximize the utility of existing resources in order to achieve the goal of "faster, cheaper, better". An area of concern within NASA Langley Research Center was the scheduling, planning, and resource management of the Wind Tunnel Enterprise operations. Nine wind tunnels make up the Enterprise. Prior to this research, these wind tunnel groups did not employ a rigorous or standardized management planning system. In addition, each wind tunnel unit operated from a position of autonomy, with little coordination of clients, resources, or project control. For operating and planning purposes, each wind tunnel operating unit must balance inputs from a variety of sources. Although each unit is managed by individual Facility Operations groups, other stakeholders influence wind tunnel operations. These groups include, for example, the various researchers and clients who use the facility, the Facility System Engineering Division (FSED) tasked with wind tunnel repair and upgrade, the Langley Research Center (LaRC) Fabrication (FAB) group which fabricates repair parts and provides test model upkeep, the NASA and LARC Strategic Plans, and unscheduled use of the facilities by important clients. Expanding these influences horizontally through nine wind tunnel operations and vertically along the NASA management structure greatly increases the complexity of developing a model that can be used for successfully implementing a standardized management planning tool. The objective of this study was to implement an Integrated Wind Tunnel Planning System to improve the operations within the aeronautics testing and research group, in particular Wind Tunnel Enterprise. The study included following steps: Conducted literature search and expert discussions (NASA and Old Dominion University faculty), Performed environmental scan of NASA Langley wind tunnel operations as foundation for problem definition. Established operation requirements and evaluation methodologies. Examined windtunnel operations to map out the common characteristics, critical components, and system structure. Reviewed and evaluated various project scheduling and management systems for implementation, Evaluated and implemented "Theory of Constraints (TOC)" project scheduling methodology at NASA Langley wind tunnel operations together with NASA staff.

Jacobs, Derya, A.↗