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Results for “Planning Scheduling Algorithms”

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 NTRS2010

Multi-Objective Multi-User Scheduling for Space Science Missions

We have developed an architecture called MUSE (Multi-User Scheduling Environment) to enable the integration of multi-objective evolutionary algorithms with existing domain planning and scheduling tools. Our approach is intended to make it possible to re-use existing software, while obtaining the advantages of multi-objective optimization algorithms. This approach enables multiple participants to actively engage in the optimization process, each representing one or more objectives in the optimization problem. As initial applications, we apply our approach to scheduling the James Webb Space Telescope, where three objectives are modeled: minimizing wasted time, minimizing the number of observations that miss their last planning opportunity in a year, and minimizing the (vector) build up of angular momentum that would necessitate the use of mission critical propellant to dump the momentum. As a second application area, we model aspects of the Cassini science planning process, including the trade-off between collecting data (subject to onboard recorder capacity) and transmitting saved data to Earth. A third mission application is that of scheduling the Cluster 4-spacecraft constellation plasma experiment. In this paper we describe our overall architecture and our adaptations for these different application domains. We also describe our plans for applying this approach to other science mission planning and scheduling problems in the future.

science planning↗
NASA NTRSDate not supplied

Towards a Characterization of Scheduling Task Complexity

Future long-duration missions will require astronauts to act more autonomously, manage their schedules, and replan timelines as anomalies and discoveries occur. Astronauts are not professional planners, however, and the complexity of schedules that novice planners can complete successfully is not fully understood. To identify the primary factors which contribute to scheduling task complexity, we conducted a human-in-the-loop study and developed planning algorithms to investigate how the type and amount of constraints affect the difficulty of scheduling and rescheduling. We created rankings of difficulty using a combination of human performance metrics from experimental planning tasks and metrics describing the final plans that participants scheduled. Using the results of our scheduling and rescheduling algorithm algorithms, we created a similar ranking with which to compare. We created rankings which compared well between the experimental and algorithm results for the scheduling task, but the rescheduling task proved more difficult to estimate.

scheduling↗
NASA NTRSDate not supplied

Towards a Characterization of Scheduling Task Complexity

Future long-duration missions will require astronauts to act more autonomously, manage their schedules, and replan timelines as anomalies and discoveries occur. Astronauts are not professional planners, however, and the complexity of schedules that novice planners can complete successfully is not fully understood. To identify the primary factors which contribute to scheduling task complexity, we conducted a human-in-the-loop study and developed planning algorithms to investigate how the type and amount of constraints affect the difficulty of scheduling and rescheduling. We created rankings of difficulty using a combination of human performance metrics from experimental planning tasks and metrics describing the final plans that participants scheduled. Using the results of our scheduling and rescheduling algorithm algorithms, we created a similar ranking with which to compare. We created rankings which compared well between the experimental and algorithm results for the scheduling task, but the rescheduling task proved more difficult to estimate.

scheduling↗
NASA NTRS1982

Dynamic scheduling of runway operations

Automated ATM/C decision making is discussed. Runway scheduling and flight plan generator algorithms are considered. Terminal area geometry, ATM/C schematics, vector controller display and simulation work are reported.

Pararas, J.↗
NASA NTRS1991

Intelligent perturbation algorithms for space scheduling optimization

Intelligent perturbation algorithms for space scheduling optimization are presented in the form of the viewgraphs. The following subject areas are covered: optimization of planning, scheduling, and manifesting; searching a discrete configuration space; heuristic algorithms used for optimization; use of heuristic methods on a sample scheduling problem; intelligent perturbation algorithms are iterative refinement techniques; properties of a good iterative search operator; dispatching examples of intelligent perturbation algorithm and perturbation operator attributes; scheduling implementations using intelligent perturbation algorithms; major advances in scheduling capabilities; the prototype ISF (industrial Space Facility) experiment scheduler; optimized schedule (max revenue); multi-variable optimization; Space Station design reference mission scheduling; ISF-TDRSS command scheduling demonstration; and example task - communications check.

Kurtzman, Clifford R.↗
NASA NTRS2013

flexplan: Mission Planning System for the Lunar Reconnaissance Orbiter

flexplan is a mission planning and scheduling (MPS) tool that uses soft algorithms to define mission scheduling rules and constraints. This allows the operator to configure the tool for any mission without the need to modify or recompile code. In addition, flexplan uses an ID system to track every output on the schedule to the input from which it was generated. This allows flexplan to receive feedback as the schedules are executed, and update the status of all activities in a Web-based client. flexplan outputs include various planning reports, stored command loads for the Lunar Reconnaissance Orbiter (LRO), ephemeris loads, and pass scripts for automation.

Barnoy, Assaf↗
NASA NTRS1991

Future applications of artificial intelligence to Mission Control Centers

Future applications of artificial intelligence to Mission Control Centers are presented in the form of the viewgraphs. The following subject areas are covered: basic objectives of the NASA-wide AI program; inhouse research program; constraint-based scheduling; learning and performance improvement for scheduling; GEMPLAN multi-agent planner; planning, scheduling, and control; Bayesian learning; efficient learning algorithms; ICARUS (an integrated architecture for learning); design knowledge acquisition and retention; computer-integrated documentation; and some speculation on future applications.

Friedland, Peter↗
NASA NTRS1985

A practical scheduling algorithm for Shuttle-based astronomy missions

In the Astro mission series (initial flight planned for March, 1986), the Shuttle will be used as a dedicated stellar astronomy observatory. A modified Spacelab pallet is to be used for the Astro payload, which will consist of three ultraviolet (UV) telescopes and a wide field camera mounted together on a single gimbal mount called the Inertial Pointing System (IPS). Three flights of 7-10 days duration are to be made with the same payload at intervals of 8-9 months. Previous experience has shown that changes in design requirements are inevitable, and the evolution of operational concepts will effect changes in scheduling algorithm software. For these reasons, the design goals of the Astron algorithm and its family of auxiliary software modules have been related to functional modularity, constraint flexibility, user friendliness, and 'light' input requirements. Attention is given to hardware characteristics, environmental constraints, the basic criteria function, 'Cinderella' logic, counters and constraints, and scheduling trends.

Guffin, O. T.↗
NASA NTRS1987

Computer Programs for Spacecraft Maneuvers

Multiple-encounter, multiple maneuver voyages analyzed in detail. Report describes ADAM (Advanced Analysis of Maneuvers) system, integrated collection of computer programs to aid design and analysis of maneuvers for deep space voyages involving multiple maneuvers and multiple encounters with planets and moons. Used in planning Galileo mission and used in both planning and operation of Voyager mission. Modularity of ADAM allows use of entirely new models, algorithms, trajectories, and maneuvering schedules. Characteristic great asset when spacecraft does not perform as planned.

Chadwick, Clyde↗
NASA NTRS2004

Integration of On-board EOS Schedule Revision with Space Communication Emulation System

The need for on-board decision-making for planning science observations on Earth Observing Satellites is based on the fact that the desirability of acquiring an image can change dynamically, because of changes in meteorological conditions (e.g. cloud cover), unforeseen events such as fires, floods, or volcanic eruptions, or unexpected changes in satellite or ground station capability. In such cases, satellite resources, such as power and SSR capacity can potentially be better utilized taking another image that is of higher quality. Currently, typical Earth observing satellites cannot communicate directly with each other, and can only communicate with ground stations about 5% to 10% of the time. Because of the limited communication windows, as well as the cost and effort that would need to be expended in revising a mission schedule, a ground-based scheduler would have little or no opportunity to revise the schedule in response to the contingencies that may arise. For this reason, a distributed science planning system combining a ground-based scheduler with on-board schedule revision capabilities is warranted. This paper will describe algorithms for on-board decision-making for science planning and their integration with the advanced satellite control and communications technology developed at the Space Communication Emulation Facility (SCEF) at NASA Glenn Research Center. Our objective of demonstrating how advanced communications and scheduling technology can be combined to improve the scientific utility of images acquired by Earth observing systems will be discussed via a description of a number of realistic flight scenarios.

Khatib, Lina↗
NASA NTRS2016

Transitioning Autonomous Systems Technology Research to a Flight Software Environment

NASA has developed methods and algorithms for autonomous spacecraft operations,including automated planning and scheduling, fault diagnostics and impact determination,procedure management and display. Making the transition from technology research tooperational flight software requires overcoming significant technical, programmatic andcultural challenges. Technology research is aimed at developing methods that performspecific functions correctly, but the resulting software may not be designed for flightprocessors with limited CPU, memory and network resources, and may not be easilyintegrated into spacecraft flight software. Our objective in the Autonomous Systems andOperations Project is to make significant strides toward the transformation from technologyto operational use. Our focus was twofold: maturing research grade autonomy software intoa flight software environment using broadly accepted languages and tools; and integratingautonomy applications with each other and with representative systems and their data andcommand interfaces. For a target flight software environment, we chose Core FlightSoftware, developed by Goddard Space Flight Center as a common operating systemindependent framework. Our hardware integration environment was provided by theIntegrated Power and Avionics Systems (iPAS) Lab at Johnson Space Center, in whichvarious subsystem development has been conducted to address engineering challenges forthe vehicles and systems required for long-duration missions into the solar system. The iPASand its network of connected facilities provides realistic subsystem hardware or simulationsof spacecraft power, life support, guidance, navigation and control, and command and datahandling subsystems. Interfaces between autonomy applications and the subsystems beingassessed and controlled were developed, assessed and refined. The hardware and softwareenvironment using CFS and the iPAS facility has proven to be a highly flexible and realisticenvironment in which to rapidly integrate applications in an iterative, low cost setting. Usingthe integration environment we have developed, we will turn our focus to performance andsizing analysis to determine the computational requirements for full-scale deployment ofautonomy technology. Scalability of reasoners and the spacecraft models upon which theyoperate, and robustness across the full range of spacecraft conditions and environments willbe explored and improved. We are making significant contributions to the future programsthat will build the spacecraft that will take humans beyond the Earth-Moon system, in whichprogram Systems Engineers will be able to accurately and confidently design in accurate,robust and mature autonomous operations systems.

Flight Software↗
NASA NTRS1995

Knowledge-Based Scheduling of Arrival Aircraft in the Terminal Area

A knowledge based method for scheduling arrival aircraft in the terminal area has been implemented and tested in real time simulation. The scheduling system automatically sequences, assigns landing times, and assign runways to arrival aircraft by utilizing continuous updates of aircraft radar data and controller inputs. The scheduling algorithm is driven by a knowledge base which was obtained in over two thousand hours of controller-in-the-loop real time simulation. The knowledge base contains a series of hierarchical 'rules' and decision logic that examines both performance criteria, such as delay reductions, as well as workload reduction criteria, such as conflict avoidance. The objective of the algorithm is to devise an efficient plan to land the aircraft in a manner acceptable to the air traffic controllers. This paper describes the scheduling algorithms, gives examples of their use, and presents data regarding their potential benefits to the air traffic system.

Krzeczowski, K. J.↗
NASA NTRS1994

The Final Approach Spacing Tool

A system for assisting terminal area air traffic controllers in the management and control of arrival traffic, referred to as the Final Approach Spacing Tool (FAST), is being developed at NASA Ames Research Center. In a cooperative program, NASA and FAA have efforts underway to install and evaluate the system at the Dallas/Fort Worth Terminal Radar Approach Control facility. This paper will review the software architecture, the algorithms components, and the human-machine interface. The system is based on continuous updates of a detailed trajectory analyses of all arrival aircraft. FAST interprets the results of these trajectory analyses to build an efficient and procedurally acceptable plan for the arrival traffic that consists of a sequence, schedule, and runway assignment. The system utilizes a heuristically-based conflict resolution algorithm to build a solution trajectory that satisfies the plan, It extracts a series of speed and heading advisories from the solution trajectory to assist the controller in efficiently managing and controlling the arrival traffic down to the runway. The advisories are displayed in a graphical format to the controller. In addition to the radar tracking data, the system also relies on a series of data bases. These data bases contain aircraft performance models, airline preferred operational procedures, airspace structure, air traffic procedural models, and a three dimensional wind model. Field evaluation of FAST is expected to begin in 1994.

Davis, Thomas J.↗
NASA NTRS2014

Automated Scheduling of Personnel to Staff Operations for the Mars Science Laboratory

Leveraging previous work on scheduling personnel for space mission operations, we have adapted ASPEN (Activity Scheduling and Planning Environment) [1] to the domain of scheduling personnel for operations of the Mars Science Laboratory. Automated scheduling of personnel is not new. We compare our representations to a sampling of employee scheduling systems available with respect to desired features. We described the constraints required by MSL personnel schedulers and how each is handled by the scheduling algorithm.

MSL↗
NASA NTRS1990

Distributed decision-making for space operations

A programmatic and technical perspective in the context of future space applications is presented, that includes some of the management challenges that arise as the decision-making process becomes increasingly more decentralized. Three challenges are discussed: (1) the degree to which the planners must communicate with each other and with those who are seeking space operations resources, (2) the collection, management, employment and dissemination of the information needed to make decisions, and (3) the challenges connected with schedule integration. The technical perspective presented leads to recommended adaptations to the normal scheduling algorithms that retain the 'degrees of freedom' in the planning result. It is shown that these adaptations are specific technical responses to the programmatic challenges discussed.

Hornstein, Rhoda Shaller↗
NASA NTRS1986

Study of onboard expert systems to augment space shuttle and space station autonomy

The feasibility of onboard crew activity planning was examined. The use of expert systems technology to aid crewmembers in locating stowed equipment was also investigated. The crew activity planning problem, along with a summary of past and current research efforts, was discussed in detail. The requirements and specifications used to develop the crew activity planning system was also defined. The guidelines used to create, develop, and operate the MFIVE Crew Scheduler and Logistics Clerk were discussed. Also discussed is the mathematical algorithm, used by the MFIVE Scheduler, which was developed to aid in optimal crew activity planning.

Kurtzman, C. R.↗
NASA NTRS1995

Knowledge-based scheduling of arrival aircraft

A knowledge-based method for scheduling arrival aircraft in the terminal area has been implemented and tested in real-time simulation. The scheduling system automatically sequences, assigns landing times, and assigns runways to arrival aircraft by utilizing continuous updates of aircraft radar data and controller inputs. The scheduling algorithms is driven by a knowledge base which was obtained in over two thousand hours of controller-in-the-loop real-time simulation. The knowledge base contains a series of hierarchical 'rules' and decision logic that examines both performance criteria, such as delay reduction, as well as workload reduction criteria, such as conflict avoidance. The objective of the algorithms is to devise an efficient plan to land the aircraft in a manner acceptable to the air traffic controllers. This paper will describe the scheduling algorithms, give examples of their use, and present data regarding their potential benefits to the air traffic system.

Krzeczowski, K.↗