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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Exploring Self-Scheduling Strategies and Heuristics in Novice Schedulers

Missions beyond low Earth orbit will require crews to act with greater autonomy. Increasing communication delays will require crews to take on tasks currently supported by Mission Control, including the involved process of scheduling and rescheduling their own complex spaceflight timelines to fit a variety of restrictions and constraints. Astronauts, unlike Mission Control, are not expert planners, however, and determining simple strategies and heuristics that enable them to schedule successfully can increase the range of problems they can solve successfully. In two human-in-the-loop scheduling experiments, we analyzed 1) common strategies among novice schedules, and 2) the development of self-scheduling heuristics. We find that, even when participants are instructed to follow a given strategy, they rapidly develop their own self-scheduling heuristics as they learn to successfully complete the scheduling task. Participants conducting naturally learn to schedule activities with the most constraints first, while participants vary in heuristics in the rescheduling task.

self-scheduling↗
NASA NTRSDate not supplied

Exploring Self-Scheduling Strategies and Heuristics in Novice Schedulers

Missions beyond low Earth orbit will require crews to act with greater autonomy. Increasing communication delays will require crews to take on tasks currently supported by Mission Control, including the involved process of scheduling and rescheduling their own complex spaceflight timelines to fit a variety of restrictions and constraints. Unlike Mission Control, astronauts are not expert planners, and determining strategies and heuristics that enable crews to schedule successfully may increase the range of problems they can solve. In two human-in-the-loop scheduling experiments, we analyzed 1) common strategies among novice schedulers and 2) the development of self-scheduling heuristics. We find that, even when participants are instructed to follow a given strategy, they rapidly develop their own self-scheduling heuristics as they learn to successfully complete the scheduling task. While scheduling, participants naturally learn to arrange activities with the most constraints first, whereas while rescheduling, they display greater variability in their heuristics.

self-scheduling↗
DOE OSTI2018

A Heterogeneity-Aware Task Scheduler for Spark

Big data processing systems such as Spark are employed in an increasing number of diverse applications-such as machine learning, graph computation, and scientific computing-each with dynamic and different resource needs. These applications increasingly run on heterogeneous hardware, e.g., with out-of-core accelerators. However, big data platforms do not factor in the multi-dimensional heterogeneity of applications and hardware. This leads to a fundamental mismatch between the application and hardware characteristics, and the resource scheduling adopted in big data platforms. For example, Hadoop and Spark consider only data locality when assigning tasks to nodes, and typically disregard the hardware capabilities and suitability to specific application requirements. In this paper, we present RUPAM, a heterogeneity-aware task scheduling system for big data platforms, which considers both task-level resource characteristics and underlying hardware characteristics, as well as preserves data locality. RUPAM adopts a simple yet effective heuristic to decide the dominant scheduling factor (e.g., CPU, memory, or I/O), given a task in a particular stage. Our experiments show that RUPAM is able to improve the performance of representative applications by up to 62.3% compared to the standard Spark scheduler.

Computer Science↗
NASA NTRS1996

Investigations into Generalization of Constraint-Based Scheduling Theories with Applications to Space Telescope Observation Scheduling

This final report summarizes research performed under NASA contract NCC 2-531 toward generalization of constraint-based scheduling theories and techniques for application to space telescope observation scheduling problems. Our work into theories and techniques for solution of this class of problems has led to the development of the Heuristic Scheduling Testbed System (HSTS), a software system for integrated planning and scheduling. Within HSTS, planning and scheduling are treated as two complementary aspects of the more general process of constructing a feasible set of behaviors of a target system. We have validated the HSTS approach by applying it to the generation of observation schedules for the Hubble Space Telescope. This report summarizes the HSTS framework and its application to the Hubble Space Telescope domain. First, the HSTS software architecture is described, indicating (1) how the structure and dynamics of a system is modeled in HSTS, (2) how schedules are represented at multiple levels of abstraction, and (3) the problem solving machinery that is provided. Next, the specific scheduler developed within this software architecture for detailed management of Hubble Space Telescope operations is presented. Finally, experimental performance results are given that confirm the utility and practicality of the approach.

Muscettola, Nicola↗
NASA NTRS2005

Considerations for Using an Incremental Scheduler for Human Exploration Task Scheduling

As humankind embarks on longer space missions farther from home, the requirements and environments for scheduling the activities performed on these missions are changing. As we begin to prepare for these missions it is appropriate to evaluate the merits and applicability of the different types of scheduling engines. Scheduling engines temporally arrange tasks onto a timeline so that all constraints and objectives are met and resources are not overbooked. Scheduling engines used to schedule space missions fall into three general categories: batch, mixed-initiative, and incremental. This paper presents an assessment of the engine types, a discussion of the impact of human exploration of the moon and Mars on planning and scheduling, and the applicability of the different types of scheduling engines. This paper will pursue the hypothesis that incremental scheduling engines may have a place in the new environment; they have the potential to reduce cost, to improve the satisfaction of those who execute or benefit from a particular timeline (the customers), and to allow astronauts to plan their own tasks.

Jaap, John↗
NASA NTRS2006

Projecting Future Scheduled Airline Demand, Schedules and NGATS Benefits Using TSAM

The Transportation Systems Analysis Model (TSAM) developed by Virginia Tech s Air Transportation Systems Lab and NASA Langley can provide detailed analysis of the effects on the demand for air travel of a full range of NASA and FAA aviation projects. TSAM has been used to project the passenger demand for very light jet (VLJ) air taxi service, scheduled airline demand growth and future schedules, Next Generation Air Transportation System (NGATS) benefits, and future passenger revenues for the Airport and Airway Trust Fund. TSAM can project the resulting demand when new vehicles and/or technology is inserted into the long distance (100 or more miles one-way) transportation system, as well as, changes in demand as a result of fare yield increases or decreases, airport transit times, scheduled flight times, ticket taxes, reductions or increases in flight delays, and so on. TSAM models all long distance travel in the contiguous U.S. and determines the mode choice of the traveler based on detailed trip costs, travel time, schedule frequency, purpose of the trip (business or non-business), and household income level of the traveler. Demand is modeled at the county level, with an airport choice module providing up to three airports as part of the mode choice. Future enplanements at airports can be projected for different scenarios. A Fratar algorithm and a schedule generator are applied to generate future flight schedules. This paper presents the application of TSAM to modeling future scheduled air passenger demand and resulting airline schedules, the impact of NGATS goals and objectives on passenger demand, along with projections for passenger fee receipts for several scenarios for the FAA Airport and Airway Trust Fund.

Dollyhigh, Samuel↗
DOE OSTI2023

Outage Forecast-Based Preventative Scheduling Model for Distribution System Resilience Enhancement

Distribution system resilience enhancement is an important topic to ensure customers have access to power supply during extreme events. In fact, certain weather-related extreme events can be predicted ahead of time. Therefore, it is important to investigate how to predict grid outages using extreme weather forecasts, and how outage predictions can be incorporated into distribution system resilience enhancement. In this paper, a preventative scheduling model for distribution systems is proposed. The model targets at allocating resources, especially mobile responsive resources such as mobile backup generators and mobile energy storage systems, to prepare for an extreme event in the day-ahead context. To achieve efficient resource allocation and scheduling, a machine learning-based outage prediction module is developed to predict vulnerable or risky segments of the distribution system based on historical operating records and extreme weather event forecast. By integrating the outage prediction results into the scheduling model, optimal resource allocation can be derived to help distribution systems prepare for an upcoming event and improve resilience performance. A real distribution feeder in North Carolina, U.S. is used in the case study to validate the proposed approach.

distributed energy resources↗
NASA NTRS1993

Scheduling the future NASA Space Network: Experiences with a flexible scheduling prototype

NASA's Space Network (SN) provides telecommunications and tracking services to low earth orbiting spacecraft. One proposal for improving resource allocation and automating conflict resolution for the SN is the concept of flexible scheduling. In this concept, each Payload Operations Control Center (POCC) will possess a Space Network User POCC Interface (SNUPI) to support the development and management of flexible requests. Flexible requests express the flexibility, constraints, and repetitious nature of the user's communications requirements. Flexible scheduling is expected to improve SN resource utilization and user satisfaction, as well as reduce the effort to produce and maintain a schedule. A prototype testbed has been developed to better understand flexible scheduling as it applies to the SN. This testbed consists of a SNUPI workstation, an SN scheduler, and a flexible request language that conveys information between the two systems. All three are being evaluated by operations personnel. Benchmark testing is being conducted on the scheduler to quantify the productivity improvements achieved with flexible requests.

Happell, Nadine↗
NASA NTRS2019

Crew Autonomy Scheduling: Scheduling Performance Pilot Study

The purpose of this pilot study is to quantify crew performance in self-scheduling through Playbook, a mobile-based scheduling and planning tool. By investigating human performance within the task of self-scheduling, we can further develop countermeasures that can mitigate deficient scheduling performance, and evaluate changes as a result of these countermeasures. Moreover, this research can advise the development of standards and guidelines for autonomous crews in future missions. In human spaceflight today, the task of planning crew member’s schedules falls to Ops Planners. It takes many weeks to plan due to the complex impact each day-to-day activity can have on other activities, crew members, and resources. These impacts are measured as constraints, and these constraints can result in temporal, ordering, or resource requirements. As future spaceflight missions span longer distances and the latency of communication between the crew and Mission Control Center (MCC) increases, the need for crew members to work independently from MCC will also increase. This results in a need for crew members to be able to autonomously plan and adjust their own schedules.

planning↗
NASA NTRS1989

State-based scheduling: An architecture for telescope observation scheduling

The applicability of constraint-based scheduling, a methodology previously developed and validated in the domain of factory scheduling, is extended to problem domains that require attendance to a wider range of state-dependent constraints. The problem of constructing and maintaining a short-term observation schedule for the Hubble Space Telescope (HST), which typifies this type of domain is the focus of interest. The nature of the constraints encountered in the HST domain is examined, system requirements are discussed with respect to utilization of a constraint-based scheduling methodology in such domains, and a general framework for state-based scheduling is presented.

Muscettola, Nicola↗
NASA NTRS1991

Polynomial optimization techniques for activity scheduling. Optimization based prototype scheduler

Polynomial optimization techniques for activity scheduling (optimization based prototype scheduler) are presented in the form of the viewgraphs. The following subject areas are covered: agenda; need and viability of polynomial time techniques for SNC (Space Network Control); an intrinsic characteristic of SN scheduling problem; expected characteristics of the schedule; optimization based scheduling approach; single resource algorithms; decomposition of multiple resource problems; prototype capabilities, characteristics, and test results; computational characteristics; some features of prototyped algorithms; and some related GSFC references.

Reddy, Surender↗
NASA NTRS1996

A Generic Expert Scheduling System Architecture and Toolkit: GUESS (Generically Used Expert Scheduling System)

Scheduling has become an increasingly important element in today's society and workplace. Within the NASA environment, scheduling is one of the most frequently performed and challenging functions. Towards meeting NASA's scheduling needs, a research version of a generic expert scheduling system architecture and toolkit has been developed. This final report describes the development and testing of GUESS (Generically Used Expert Scheduling System).

Liebowitz, Jay↗
NASA NTRS2011

Scheduling Results for the THEMIS Observation Scheduling Tool

We describe a scheduling system intended to assist in the development of instrument data acquisitions for the THEMIS instrument, onboard the Mars Odyssey spacecraft, and compare results from multiple scheduling algorithms. This tool creates observations of both (a) targeted geographical regions of interest and (b) general mapping observations, while respecting spacecraft constraints such as data volume, observation timing, visibility, lighting, season, and science priorities. This tool therefore must address both geometric and state/timing/resource constraints. We describe a tool that maps geometric polygon overlap constraints to set covering constraints using a grid-based approach. These set covering constraints are then incorporated into a greedy optimization scheduling algorithm incorporating operations constraints to generate feasible schedules. The resultant tool generates schedules of hundreds of observations per week out of potential thousands of observations. This tool is currently under evaluation by the THEMIS observation planning team at Arizona State University.

Thermal Emission Imaging System (THEMIS)↗
DOE OSTI2013

Optimal investment and scheduling of distributed energy resources with uncertainty in electric vehicles driving schedules

The large scale penetration of electric vehicles (EVs) will introduce technical challenges to the distribution grid, but also carries the potential for vehicle-to-grid services. Namely, if available in large enough numbers, EVs can be used as a distributed energy resource (DER) and their presence can influence optimal DER investment and scheduling decisions in microgrids. In this work, a novel EV fleet aggregator model is introduced in a stochastic formulation of DER-CAM [1], an optimization tool used to address DER investment and scheduling problems. This is used to assess the impact of EV interconnections on optimal DER solutions considering uncertainty in EV driving schedules. Optimization results indicate that EVs can have a significant impact on DER investments, particularly if considering short payback periods. Furthermore, results suggest that uncertainty in driving schedules carries little significance to total energy costs, which is corroborated by results obtained using the stochastic formulation of the problem.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗