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At least 307 records · Page 17

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NASA NTRS2003

Distributed Scheduling Architecture for Multi-Center Time-Based Metering

The Traffic Management Advisor (TMA) is an air traffic control automation system currently in use in seven Air Route Traffic Control Centers (ARTCCs) to enable time based metering to busy airports within their airspace. However, this system is limited to operation within a single ARTCC, within about a 200 nautical mile radius of the airport, and on relatively simple streams of traffic. The need for coordinated metering within a greater (300+ nautical mile) radius of an airport, on streams of traffic with significant branching, and across ARTCC boundaries, has been identified. Early tests revealed that TMA could not simply be scaled up to handle such a problem. Instead, a loosely coupled hierarchy of schedules, in which constraints from downstream schedules are passed upstream, is required. Such an architecture reduces the reliance on distant projections of arrival times, making schedules robust to changes in sequence and to additions of aircraft (such as aircraft departing inside the system s scheduling horizon). This architecture is also scaleable, easily reconfigurable, and can be networked together. As such, it can be adapted for use in any size or configuration of airspace and with any number of airports delivering restrictions. An implementation of this distributed scheduling architecture is currently undergoing testing in the TMA-Multi Center system. This paper describes the architecture and its motivation.

Landry, Steven↗

NASA NTRS2006

In-Space Crew-Collaborative Task Scheduling

As humans venture farther from earth for longer durations, it will become essential for those on the journey to have significant control over the scheduling of their own activities as well as the activities of their companion systems and robots. However, there are many reasons why the crew will not do all the scheduling; timelines will be the result of collaboration with ground personnel. Emerging technologies such as in-space message buses, delay-tolerant networks, and in-space internet will be the carriers on which the collaboration rides. Advances in scheduling technology, in the areas of task modeling, scheduling engines, and user interfaces will allow the crew to become virtual scheduling experts. New concepts of operations for producing the timeline will allow the crew and the ground support to collaborate while providing safeguards to ensure that the mission will be effectively accomplished without endangering the systems or personnel.

Jaap, John↗

NASA NTRS2006

In-Space Crew-Collaborative Task Scheduling

As humans venture farther from Earth for longer durations, it will become essential for those on the journey to have significant control over the scheduling of their own activities as well as the activities of their companion systems and robots. However, the crew will not do all the scheduling; timelines will be the result of collaboration with ground personnel. Emerging technologies such as in-space message buses, delay-tolerant networks, and in-space internet will be the carriers on which the collaboration rides. Advances in scheduling technology, in the areas of task modeling, scheduling engines, and user interfaces will allow the crew to become virtual scheduling experts. New concepts of operations for producing the timeline will allow the crew and the ground support to collaborate while providing safeguards to ensure that the mission will be effectively accomplished without endangering the systems or personnel.

Jaap, John↗

NASA NTRS2006

Remote Collaboration on Task Scheduling for Humans at Mars

Jaap, John↗

NASA NTRS2006

Scheduling Software for Complex Scenarios

Preparing a vehicle and its payload for a single launch is a complex process that involves thousands of operations. Because the equipment and facilities required to carry out these operations are extremely expensive and limited in number, optimal assignment and efficient use are critically important. Overlapping missions that compete for the same resources, ground rules, safety requirements, and the unique needs of processing vehicles and payloads destined for space impose numerous constraints that, when combined, require advanced scheduling. Traditional scheduling systems use simple algorithms and criteria when selecting activities and assigning resources and times to each activity. Schedules generated by these simple decision rules are, however, frequently far from optimal. To resolve mission-critical scheduling issues and predict possible problem areas, NASA historically relied upon expert human schedulers who used their judgment and experience to determine where things should happen, whether they will happen on time, and whether the requested resources are truly necessary.

Source record↗

NASA NTRS2007

DSN Resource Scheduling

TIGRAS is client-side software, which provides tracking-station equipment planning, allocation, and scheduling services to the DSMS (Deep Space Mission System). TIGRAS provides functions for schedulers to coordinate the DSN (Deep Space Network) antenna usage time and to resolve the resource usage conflicts among tracking passes, antenna calibrations, maintenance, and system testing activities. TIGRAS provides a fully integrated multi-pane graphical user interface for all scheduling operations. This is a great improvement over the legacy VAX VMS command line user interface. TIGRAS has the capability to handle all DSN resource scheduling aspects from long-range to real time. TIGRAS assists NASA mission operations for DSN tracking of station equipment resource request processes from long-range load forecasts (ten years or longer), to midrange, short-range, and real-time (less than one week) emergency tracking plan changes. TIGRAS can be operated by NASA mission operations worldwide to make schedule requests for the DSN station equipment.

Wang, Yeou-Fang↗

NASA NTRS2010

An Optimization Model for Scheduling Problems with Two-Dimensional Spatial Resource Constraint

Traditional scheduling problems involve determining temporal assignments for a set of jobs in order to optimize some objective. Some scheduling problems also require the use of limited resources, which adds another dimension of complexity. In this paper we introduce a spatial resource-constrained scheduling problem that can arise in assembly, warehousing, cross-docking, inventory management, and other areas of logistics and supply chain management. This scheduling problem involves a twodimensional rectangular area as a limited resource. Each job, in addition to having temporal requirements, has a width and a height and utilizes a certain amount of space inside the area. We propose an optimization model for scheduling the jobs while respecting all temporal and spatial constraints.

Garcia, Christopher↗

NASA NTRS2010

Astronaut Office Scheduling System Software

AOSS is a highly efficient scheduling application that uses various tools to schedule astronauts weekly appointment information. This program represents an integration of many technologies into a single application to facilitate schedule sharing and management. It is a Windows-based application developed in Visual Basic. Because the NASA standard office automation load environment is Microsoft-based, Visual Basic provides AO SS developers with the ability to interact with Windows collaboration components by accessing objects models from applications like Outlook and Excel. This also gives developers the ability to create newly customizable components that perform specialized tasks pertaining to scheduling reporting inside the application. With this capability, AOSS can perform various asynchronous tasks, such as gathering/ sending/ managing astronauts schedule information directly to their Outlook calendars at any time.

Brown, Estevancio↗

NASA NTRS2010

An Optimizing Space Data-Communications Scheduling Method and Algorithm with Interference Mitigation, Generalized for a Broad Class of Optimization Problems

NASA's space data-communications infrastructure, the Space Network and the Ground Network, provide scheduled (as well as some limited types of unscheduled) data-communications services to user spacecraft via orbiting relay satellites and ground stations. An implementation of the methods and algorithms disclosed herein will be a system that produces globally optimized schedules with not only optimized service delivery by the space data-communications infrastructure but also optimized satisfaction of all user requirements and prescribed constraints, including radio frequency interference (RFI) constraints. Evolutionary search, a class of probabilistic strategies for searching large solution spaces, constitutes the essential technology in this disclosure. Also disclosed are methods and algorithms for optimizing the execution efficiency of the schedule-generation algorithm itself. The scheduling methods and algorithms as presented are adaptable to accommodate the complexity of scheduling the civilian and/or military data-communications infrastructure. Finally, the problem itself, and the methods and algorithms, are generalized and specified formally, with applicability to a very broad class of combinatorial optimization problems.

Rash, James L.↗

NASA NTRS2012

An Analysis of Shuttle Crew Scheduling Violations

From the early years of the Space Shuttle program, National Aeronautics and Space Administration (NASA) Shuttle crews have had a timeline of activities to guide them through their time on-orbit. Planners used scheduling constraints to build timelines that ensured the health and safety of the crews. If a constraint could not be met it resulted in a violation. Other agencies of the federal government also have scheduling constraints to ensure the safety of personnel and the public. This project examined the history of Space Shuttle scheduling constraints, constraints from Federal agencies and branches of the military and how these constraints may be used as a guide for future NASA and private spacecraft. This was conducted by reviewing rules and violations with regard to human aerospace scheduling constraints, environmental, political, social and technological factors, operating environment and relevant human factors. This study includes a statistical analysis of Shuttle Extra Vehicular Activity (EVA) related violations to determine if these were a significant producer of constraint violations. It was hypothesized that the number of SCSC violations caused by EVA activities were a significant contributor to the total number of violations for Shuttle/ISS missions. Data was taken from NASA data archives at the Johnson Space Center from Space Shuttle/ISS missions prior to the STS-107 accident. The results of the analysis rejected the null hypothesis and found that EVA violations were a significant contributor to the total number of violations. This analysis could help NASA and commercial space companies understand the main source of constraint violations and allow them to create constraint rules that ensure the safe operation of future human private and exploration missions. Additional studies could be performed to evaluate other variables that could have influenced the scheduling violations that were analyzed.

Bristol, Douglas↗

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 NTRS2008

Deep Space Network Scheduling Using Multi-Objective Optimization with Uncertainty

We have developed a novel technique to incorporate uncertainty modeling within an evolutionary algorithm approach to multi-objective scheduling, with the goal of identifying a Pareto frontier (tradeoff curve) that recognizes the likelihood of events that can impact the schedule outcome. Our approach is particularly applicable to the generation of multiobjective optimized robust schedules, where objectives are assigned a service level, for example that we require an objective value to be greater than or equal to X with Y% confidence. We have demonstrated that such an approach can, for example, minimize scheduling on less reliable resources, based solely on a resource reliability model and not on any ad hoc heuristics. We have also investigated an alternative method of optimizing for robustness, in which we add to the set of objectives a failure risk objective to minimize. We compare the advantages and disadvantages of these two approaches. Future plans for further developing this technology include its application to space-based observatory scheduling problems.

Johnston, Mark D.↗

NASA NTRS2016

A Human-in-the Loop Evaluation of a Coordinated Arrival Departure Scheduling Operations for Managing Departure Delays at LaGuardia Airport

LaGuardia (LGA) departure delay was identified by the stakeholders and subject matter experts as a significant bottleneck in the New York metropolitan area. Departure delay at LGA is primarily due to dependency between LGA's arrival and departure runways: LGA departures cannot begin takeoff until arrivals have cleared the runway intersection. If one-in one-out operations are not maintained and a significant arrival-to-departure imbalance occurs, the departure backup can persist through the rest of the day. At NASA Ames Research Center, a solution called "Departure-sensitive Arrival Spacing" (DSAS) was developed to maximize the departure throughput without creating significant delays in the arrival traffic. The concept leverages a Terminal Sequencing and Spacing (TSS) operations that create and manage the arrival schedule to the runway threshold and added an interface enhancement to the traffic manager's timeline to provide the ability to manually adjust inter-arrival spacing to build precise gaps for multiple departures between arrivals. A more complete solution would include a TSS algorithm enhancement that could automatically build these multi-departure gaps. With this set of capabilities, inter-arrival spacing could be controlled for optimal departure throughput. The concept was prototyped in a human-in-the- loop (HITL) simulation environment so that operational requirements such as coordination procedures, timing and magnitude of TSS schedule adjustments, and display features for Tower, TRACON and Traffic Management Unit could be determined. A HITL simulation was conducted in August 2014 to evaluate the concept in terms of feasibility, controller workload impact, and potential benefits. Three conditions were tested, namely a Baseline condition without scheduling, TSS condition that schedules the arrivals to the runway threshold, and TSS+DSAS condition that adjusts the arrival schedule to maximize the departure throughput. The results showed that during high arrival demand period, departure throughput could be incrementally increased under TSS and TSS+DSAS conditions without compromising the arrival throughput. The concept, operational procedures, and summary results were originally published in ATM20151 but detailed results were omitted. This paper expands on the earlier paper to provide the detailed results on throughput, conformance, safety, flight time/distance, etc. that provide extra insights into the feasibility and the potential benefits on the concept.

trajectory-based operations↗

NASA NTRS2019

Evolving Management Strategies to Improve NASA Flagship's Cost and Schedule Performance: LUVOIR as a Case Study

The LUVOIR study process has brought to fruition an extremely exciting scientific mission concept. The 3.5 year LUVOIR study duration enabled an unprecedented level of scientific, engineering, and technology thoroughness prior to the Astro2020 Decadal. This detail also shed light on many technical and programmatic challenges for efficiently developing a mission of this scale. While NASA's flagships perform exquisitely once on-orbit, there is understandable growing frustration in their development cost and schedule overruns. We felt it incumbent upon ourselves to ask how we could improve on delivering LUVOIR (or any of NASA's future flagships) on schedule and on budget, not just for the next mission, but for all NASA large strategic missions to come. We researched past and current NASA flagship's lessons learned publications and other large government projects that pointed to some systemic challenges that will only grow with larger and more complex strategic missions. Our findings pointed us to some ways that could potentially evolve NASA's current flagship management practices to help improve on their development cost and schedule performance despite their growing complexity.. This paper briefly comments on the science motivation for NASA's flagships and on the science motivation for a LUVOIR-like mission. We argue the motivation for improving NASA's flagships development cost and schedule performance. We review the specific challenges of NASA's flagships to acknowledge their specific issues. We then examine the most repeated systemic challenges we found from previous NASA flagship and other large government project lessons learned/observed. Lastly, we offer recommendations to tackle these repeated systemic challenges facing NASA's flagships. The recommendations culminate into a proactive integrated development and funding framework to enable improving the execution of NASA's future flagship's cost and schedule performance.

Crooke, Julie A.↗

NASA NTRS2020

Enabling Limited Resource-Bounded Disjunction in Scheduling

We describe three approaches to enabling a severely computationallylimited embedded scheduler to consider a smallnumber of alternative activities based on resource availability.We consider the case where the scheduler is so computationallylimited that it cannot backtrack search. The first twoapproaches precompile resource checks (called guards) thatonly enable selection of a preferred alternative activity if sufficientresources are estimated to be available to schedule theremaining activities. The third approach mimics backtrackingby invoking the scheduler multiple times with the alternativeactivities. We present an evaluation of these techniques onMars mission scenarios (called sol types) from NASA’s nextplanetary rover where these techniques are being evaluatedfor inclusion in an onboard scheduler.

Vaquero, Tiago↗

NASA NTRS2026

Deep RL for Fast Long-Horizon Operations Scheduling on NASA's Carruthers Geocorona Observatory Mission

Spacecraft operations scheduling is a highly constrained, long-horizon combinatorial optimization problem that traditionally relies on heuristics, constraint programming, or manual planning. We present a scalable deep reinforcement learning framework developed and deployed for NASA’s Carruthers Geocorona Observatory mission. Our framework introduces a macro-action abstraction known as activity blocks coupled with dynamic action-masking to navigate the intractably large search space and strictly enforce complex power, thermal, and instrument constraints. The resulting architecture generates globally feasible schedules with overwhelming probability, establishes operational trust, and executes a full training cycle in under six hours, circumventing the need for policy robustness by enabling rapid, on-demand retraining. Further, resulting schedules outperform baseline heuristics in scheduled science quality. The deep reinforcement learning framework was deployed as the default operational scheduler for the Carruthers Geocorona Observatory mission from the outset of the mission, demonstrating that deep reinforcement learning can be trusted for real spacecraft operations under complex, evolving constraints.

Geocorona↗

DOE OSTI2022

Spread spectrum scheduling requests for wireless communication resource

Apparatuses, systems and methods are disclosed for wireless communication. A base station may communicate with a plurality of user equipments (UEs) without allocating spectral resources to the UEs for communicating scheduling requests. The base station may detect a scheduling request from a requesting UE of the plurality of UEs. The scheduling request may include a spread spectrum signal based on a spreading sequence. The scheduling request may indicate an identity for a requesting UE without indicating information other than the identity. The base station may grant spectral resources to the requesting UE in response to detecting a scheduling request.

Moradi, Hussein↗

DOE OSTI2023

DeepSurveySim: Simulation Software and Benchmark Challenges for Astronomical Observation Scheduling

Modern astronomical surveys have multiple competing scientific goals. Optimizing the observation schedule for these goals presents significant computational and theoretical challenges, and state-of-the-art methods rely on expensive human inspection of simulated telescope schedules. Automated methods, such as reinforcement learning, have recently been explored to accelerate scheduling. However, there do not yet exist benchmark data sets or user-friendly software frameworks for testing and comparing these methods. We present DeepSurveySim -- a high-fidelity and flexible simulation tool for use in telescope scheduling. DeepSurveySim provides methods for tracking and approximating sky conditions for a set of observations from a user-supplied telescope configuration. We envision this tool being used to produce benchmark data sets and for evaluating the efficacy of ground-based telescope scheduling algorithms, particularly for machine learning algorithms that would suffer in efficacy if limited to real data for training.We introduce three example survey configurations and related code implementations as benchmark problems that can be simulated with DeepSurveySim.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗