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At least 91 records · Page 5

Evaluation of Self-Scheduling Exercises Completed by Analog Crewmembers in NASA's Human Exploration Research Analog (HERA)

NASA human spaceflight missions are inherently dynamic and require frequent scheduling changes in order to adapt to changing mission priorities and objectives. Tactical level changes to the mission plan are traditionally made by a team of expert planners and operations specialists on the ground. However, astronauts are expected to execute missions more autonomously during future long duration missions. Astronauts will need to take on some of the responsibility of managing their own schedule while still abiding by the numerous constraints required by human spaceflight operations. This paper summarizes salient elements of crew performance in NASA’s Human Exploration Research Analog Campaign 3. Analog crewmembers completed a series of self-scheduling exercises to evaluate Playbook’s usability towards enabling self-scheduling without support from ground control. Playbook is a self-scheduling software tool designed and developed by our team. We also investigated how to best communicate self-scheduling tasks and constraints to the crew in order to facilitate efficient self-scheduling during isolation in a realistic environment. Our analysis identified that 30 minutes was sufficient to complete complex self-scheduling tasks. Our evaluation also identified differences between individual and collaborative performance; analog crewmembers completed self-scheduling exercises more quickly as a team as opposed to individually and reported lower subjective difficulty ratings overall.

HERA

Quantum/AI Topology-Aware Latency-Adaptive HPC Workflow Scheduling Optimization

The growing demand for more powerful high-performance computing (HPC) systems has led to a steady rise in energy consumption by supercomputing worldwide. This study is focused on comparing our Application-Topology Mapper (ATMapper) to the popular Simple Linux Utility for Resource Management (SLURM) for the purpose of exploring methods that can further optimize job-scheduling within HPC systems. ATMapper is an Artificial-Intelligence based approach to job-scheduling that is currently being enhanced with quantum annealing (QA) to generate optimal schedules faster. We are applying QA to speedup our ATMapper process to achieve higher computing efficiency, thereby reducing HPC energy consumption. Here, we examine how four job-scheduling approaches perform in processor node assignment when using an example network architecture of 4 interconnected nodes. Using a specialized script, we are assessing the schedule of a computation flow with 11 interdependent tasks. The data movements among nodes were tracked to count for the number of interactions (network hops) between nodes needed to complete the tasks. The total number of hops and the job completion time were then used to quantify the efficiency of the different mapping approaches. In addition to SLURM, we also compare our ATMapper to the QA-enabled LBNL TIGER and the D-Wave Distributed Computing processor assignment approaches. The preliminary results showed that our topology-aware, latency-adaptive ATMapper is significantly more efficient when compared to the other scheduling approaches due to its load-imbalance network allocation. The scheduler displayed a computing efficiency of 53% by performing significantly fewer network hops than its alternatives. By reducing the number of hops, ATMapper was able to perform all 11 tasks by using only 3 nodes out of given 4. This research indicates the potential to use QA/AI for HPC job-scheduling. Later, we will test a SLURM simulator program to draw further comparisons on the effectiveness of ATMapper's scheduling approach. The results of this comparison will serve as a baseline for later improving SLURM's performance using a QA-enhanced ATMapper approach.

Caraveo, Braulio [University of Huston - Clear Lak

Analysis and design of gain scheduled control systems

Gain scheduling, as an idea, is to construct a global feedback control system for a time varying and/or nonlinear plant from a collection of local time invariant designs. However in the absence of a sound analysis, these designs come with no guarantees on the robustness, performance, or even nominal stability of the overall gain schedule design. Such an analysis is presented for three types of gain scheduling situations: (1) a linear parameter varying plant scheduling on its exogenous parameters, (2) a nonlinear plant scheduling on a prescribed reference trajectory, and (3) a nonlinear plant scheduling on the current plant output. Conditions are given which guarantee that the stability, robustness, and performance properties of the fixed operating point designs carry over to the global gain scheduled designs, such as the scheduling variable should vary slowly and capture the plants nonlinearities. Finally, an alternate design framework is proposed which removes the slowing varying restriction or gain scheduled systems. This framework addresses some fundamental feedback issues previously ignored in standard gain.

Shamma, Jeff S.

The scheduling techniques of ESP2

The Mission Analysis Division of the Systems Analysis and Integration Laboratory at the Marshall Space Flight Center has developed a robust automatic scheduler which can produce detailed schedules for the multi-step activities required for payload operations on the Space Station. This scheduler, a part of the Expert Scheduling Program (ESP2), has five components: the bookkeeper, checker, loader, selector, and explainer. The bookkeeper maintains the usage profiles for nondepletable resources, consumables, equipment, crew, and the times of all the steps for the payload activities for several different schedules simultaneously. The checker searches the data maintained by the bookkeeper and finds times when the constraints of each step of an activity are satisfied. The loader is an expert system that uses the techniques of forward chaining, depth-first searching, and backtracking to manage the workings of the checker so that activities are placed in the schedule without violating constraints (such as crew, resources, and orbit opportunities). The checker searches the data maintained by the bookkeeper and finds times when the constraints of each step of an activity are satisfied. The loader is an expert system which uses the techniques of forward chaining, depth-first searching, and backtracking to manage the workings of the checker so that activities are placed in the schedule without violating the constraints. The selector has several methods of choosing the next activity for the loader to schedule. The explainer shows the user why an activity was or was not scheduled at a certain time; it offers a unique graphical explanation of how the expert system (the loader) works.

Jaap, John P.

Spike: Artificial intelligence scheduling for Hubble space telescope

Efficient utilization of spacecraft resources is essential, but the accompanying scheduling problems are often computationally intractable and are difficult to approximate because of the presence of numerous interacting constraints. Artificial intelligence techniques were applied to the scheduling of the NASA/ESA Hubble Space Telescope (HST). This presents a particularly challenging problem since a yearlong observing program can contain some tens of thousands of exposures which are subject to a large number of scientific, operational, spacecraft, and environmental constraints. New techniques were developed for machine reasoning about scheduling constraints and goals, especially in cases where uncertainty is an important scheduling consideration and where resolving conflicts among conflicting preferences is essential. These technique were utilized in a set of workstation based scheduling tools (Spike) for HST. Graphical displays of activities, constraints, and schedules are an important feature of the system. High level scheduling strategies using both rule based and neural network approaches were developed. While the specific constraints implemented are those most relevant to HST, the framework developed is far more general and could easily handle other kinds of scheduling problems. The concept and implementation of the Spike system are described along with some experiments in adapting Spike to other spacecraft scheduling domains.

Johnston, Mark

Balancing antagonistic time and resource utilization constraints in over-subscribed scheduling problems

In this paper, we report work aimed at applying concepts of constraint-based problem structuring and multi-perspective scheduling to an over-subscribed scheduling problem. Previous research has demonstrated the utility of these concepts as a means for effectively balancing conflicting objectives in constraint-relaxable scheduling problems, and our goal here is to provide evidence of their similar potential in the context of HST observation scheduling. To this end, we define and experimentally assess the performance of two time-bounded heuristic scheduling strategies in balancing the tradeoff between resource setup time minimization and satisfaction of absolute time constraints. The first strategy considered is motivated by dispatch-based manufacturing scheduling research, and employs a problem decomposition that concentrates local search on minimizing resource idle time due to setup activities. The second is motivated by research in opportunistic scheduling and advocates a problem decomposition that focuses attention on the goal activities that have the tightest temporal constraints. Analysis of experimental results gives evidence of differential superiority on the part of each strategy in different problem solving circumstances. A composite strategy based on recognition of characteristics of the current problem solving state is then defined and tested to illustrate the potential benefits of constraint-based problem structuring and multi-perspective scheduling in over-subscribe scheduling problems.

Smith, Stephen F.

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.

The MICRO-BOSS scheduling system: Current status and future efforts

In this paper, a micro-opportunistic approach to factory scheduling was described that closely monitors the evolution of bottlenecks during the construction of the schedule and continuously redirects search towards the bottleneck that appears to be most critical. This approach differs from earlier opportunistic approaches, as it does not require scheduling large resource subproblems or large job subproblems before revising the current scheduling strategy. This micro-opportunistic approach was implemented in the context of the MICRO-BOSS factory scheduling system. A study comparing MICRO-BOSS against a macro-opportunistic scheduler suggests that the additional flexibility of the micro-opportunistic approach to scheduling generally yields important reductions in both tardiness and inventory. Current research efforts include: adaptation of MICRO-BOSS to deal with sequence-dependent setups and development of micro-opportunistic reactive scheduling techniques that will enable the system to patch the schedule in the presence of contingencies such as machine breakdowns, raw materials arriving late, job cancellations, etc.

Sadeh, Norman M.

Automatic generation of efficient orderings of events for scheduling applications

In scheduling a set of tasks, it is often not known with certainty how long a given event will take. We call this duration uncertainty. Duration uncertainty is a primary obstacle to the successful completion of a schedule. If a duration of one task is longer than expected, the remaining tasks are delayed. The delay may result in the abandonment of the schedule itself, a phenomenon known as schedule breakage. One response to schedule breakage is on-line, dynamic rescheduling. A more recent alternative is called proactive rescheduling. This method uses statistical data about the durations of events in order to anticipate the locations in the schedule where breakage is likely prior to the execution of the schedule. It generates alternative schedules at such sensitive points, which can be then applied by the scheduler at execution time, without the delay incurred by dynamic rescheduling. This paper proposes a technique for making proactive error management more effective. The technique is based on applying a similarity-based method of clustering to the problem of identifying similar events in a set of events.

Morris, Robert A.

Integrated planning and scheduling for Earth science data processing

Several current NASA programs such as the EOSDIS Core System (ECS) have data processing and data management requirements that call for an integrated planning and scheduling capability. In this paper, we describe the experience of applying advanced scheduling technology operationally, in terms of what was accomplished, lessons learned, and what remains to be done in order to achieve similar successes in ECS and other programs. We discuss the importance and benefits of advanced scheduling tools, and our progress toward realizing them, through examples and illustrations based on ECS requirements. The first part of the paper focuses on the Data Archive and Distribution (DADS) V0 Scheduler. We then discuss system integration issues ranging from communication with the scheduler to the monitoring of system events and re-scheduling in response to them. The challenge of adapting the scheduler to domain-specific features and scheduling policies is also considered. Extrapolation to the ECS domain raises issues of integrating scheduling with a product-generation planner (such as PlaSTiC), and implementing conditional planning in an operational system. We conclude by briefly noting ongoing technology development and deployment projects being undertaken by HTC and the ISTB.

Boddy, Mark

Achieving reutilization of scheduling software through abstraction and generalization

Reutilization of software is a difficult goal to achieve particularly in complex environments that require advanced software systems. The Request-Oriented Scheduling Engine (ROSE) was developed to create a reusable scheduling system for the diverse scheduling needs of the National Aeronautics and Space Administration (NASA). ROSE is a data-driven scheduler that accepts inputs such as user activities, available resources, timing contraints, and user-defined events, and then produces a conflict-free schedule. To support reutilization, ROSE is designed to be flexible, extensible, and portable. With these design features, applying ROSE to a new scheduling application does not require changing the core scheduling engine, even if the new application requires significantly larger or smaller data sets, customized scheduling algorithms, or software portability. This paper includes a ROSE scheduling system description emphasizing its general-purpose features, reutilization techniques, and tasks for which ROSE reuse provided a low-risk solution with significant cost savings and reduced software development time.

Wilkinson, George J.

Re-scheduling as a tool for the power management on board a spacecraft

The scheduling of events on board a spacecraft is based on forecast energy levels. The real time values of energy may not coincide with the forecast values; consequently, a dynamic revising to the allocation of power is needed. The re-scheduling is also needed for other reasons on board a spacecraft like the addition of new event which must be scheduled, or a failure of an event due to many different contingencies. This need of rescheduling is very important to the survivability of the spacecraft. In this presentation, a re-scheduling tool will be presented as a part of an overall scheme for the power management on board a spacecraft from the allocation of energy point of view. The overall scheme is based on the optimal use of energy available on board a spacecraft using expert systems combined with linear optimization techniques. The system will be able to schedule maximum number of events utilizing most energy available. The outcome is more events scheduled to share the operation cost of that spacecraft. The system will also be able to re-schedule in case of a contingency with minimal time and minimal disturbance of the original schedule. The end product is a fully integrated planning system capable of producing the right decisions in short time with less human error. The overall system will be presented with the re-scheduling algorithm discussed in detail, then the tests and results will be presented for validations.

Albasheer, Omar

Tool for Merging Proposals Into DSN Schedules

A Practical Extraction and Reporting Language (Perl) script called merge7da has been developed to facilitate determination, by a project scheduler in NASA's Deep Space Network, of whether a proposal for use of the DSN could create a conflict with the current DSN schedule. Prior to the development of merge7da, there was no way to quickly identify potential schedule conflicts: it was necessary to submit a proposal and wait a day or two for a response from a DSN scheduling facility. By using merge7da to detect and eliminate potential schedule conflicts before submitting a proposal, a project scheduler saves time and gains assurance that the proposal will probably be accepted. merge7da accepts two input files, one of which contains the current DSN schedule and is in a DSN-standard format called '7da'. The other input file contains the proposal and is in another DSN-standard format called 'C1/C2'. merge7da processes the two input files to produce a merged 7da-format output file that represents the DSN schedule as it would be if the proposal were to be adopted. This 7da output file can be loaded into various DSN scheduling software tools now in use.

Khanampornpan, Teerapat

Utilizing AI in Temporal, Spatial, and Resource Scheduling

Aurora is a software system enabling the rapid, easy solution of complex scheduling problems involving spatial and temporal constraints among operations and scarce resources (such as equipment, workspace, and human experts). Although developed for use in the International Space Station Processing Facility, Aurora is flexible enough that it can be easily customized for application to other scheduling domains and adapted as the requirements change or become more precisely known over time. Aurora s scheduling module utilizes artificial-intelligence (AI) techniques to make scheduling decisions on the basis of domain knowledge, including knowledge of constraints and their relative importance, interdependencies among operations, and possibly frequent changes in governing schedule requirements. Unlike many other scheduling software systems, Aurora focuses on resource requirements and temporal scheduling in combination. For example, Aurora can accommodate a domain requirement to schedule two subsequent operations to locations adjacent to a shared resource. The graphical interface allows the user to quickly visualize the schedule and perform changes reflecting additional knowledge or alterations in the situation. For example, the user might drag the activity corresponding to the start of operations to reflect a late delivery.

Stottler, Richard

NASA Schedule Management Handbook

The purpose of schedule management is to provide the framework for time-phasing, resource planning, coordination, and communicating the necessary tasks within a work effort. The intent is to improve schedule management by providing recommended concepts, processes, and techniques used within the Agency and private industry. The intended function of this handbook is two-fold: first, to provide guidance for meeting the scheduling requirements contained in NPR 7120.5, NASA Space Flight Program and Project Management Requirements, NPR 7120.7, NASA Information Technology and Institutional Infrastructure Program and Project Requirements, NPR 7120.8, NASA Research and Technology Program and Project Management Requirements, and NPD 1000.5, Policy for NASA Acquisition. The second function is to describe the schedule management approach and the recommended best practices for carrying out this project control function. With regards to the above project management requirements documents, it should be noted that those space flight projects previously established and approved under the guidance of prior versions of NPR 7120.5 will continue to comply with those requirements until project completion has been achieved. This handbook will be updated as needed, to enhance efficient and effective schedule management across the Agency. It is acknowledged that most, if not all, external organizations participating in NASA programs/projects will have their own internal schedule management documents. Issues that arise from conflicting schedule guidance will be resolved on a case by case basis as contracts and partnering relationships are established. It is also acknowledged and understood that all projects are not the same and may require different levels of schedule visibility, scrutiny and control. Project type, value, and complexity are factors that typically dictate which schedule management practices should be employed.

Source record

Characterization of Tactical Departure Scheduling in the National Airspace System

This paper discusses and analyzes current day utilization and performance of the tactical departure scheduling process in the National Airspace System (NAS) to understand the benefits in improving this process. The analysis used operational air traffic data from over 1,082,000 flights during the month of January, 2011. Specific metrics included the frequency of tactical departure scheduling, site specific variances in the technology's utilization, departure time prediction compliance used in the tactical scheduling process and the performance with which the current system can predict the airborne slot that aircraft are being scheduled into from the airport surface. Operational data analysis described in this paper indicates significant room for improvement exists in the current system primarily in the area of reduced departure time prediction uncertainty. Results indicate that a significant number of tactically scheduled aircraft did not meet their scheduled departure slot due to departure time uncertainty. In addition to missed slots, the operational data analysis identified increased controller workload associated with tactical departures which were subject to traffic management manual re-scheduling or controller swaps. An analysis of achievable levels of departure time prediction accuracy as obtained by a new integrated surface and tactical scheduling tool is provided to assess the benefit it may provide as a solution to the identified shortfalls. A list of NAS facilities which are likely to receive the greatest benefit from the integrated surface and tactical scheduling technology are provided.

Capps, Alan

Assessing Tactical Scheduling Options for Time-Based Surface Metering

This paper presents a parametric analysis of the most recent tactical scheduler design for NASA’s Airspace Technology Demonstration 2 (ATD-2) sub-project, committed to demonstrating time-based surface metering at Charlotte Douglas International Airport (CLT). The tactical scheduler design is implemented in a fast-time simulation model of CLT using NASA’s Surface Operations Simulator and Scheduler. The tactical scheduler is supported by three basic functions: trajectory prediction, runway scheduling, and advisory generation. A key parameter of the advisory generation function is the taxi time delay buffer used when calculating target gate pushback times from runway schedule. Multiple simulations that varied the amount of taxi time delay buffer were analyzed to determine the effect on tactical scheduler performance. The results show an improvement in tactical scheduler performance when the buffer is made sufficiently large to release departures from their gates early enough to maintain scheduler predicted runway throughput.

Zelinski, Shannon

A Stochastic Scheduler for Integrated Arrival, Departure and Surface Operations in Los Angeles

In terminal airspace, integrating arrivals, departures, and surface operations with competing resources provides the potential of improving operational efficiency by removing barriers between different operations. This work develops a centralized stochastic scheduler for operations in a terminal area including airborne and surface operations using Non-dominated sorting genetic algorithm and Monte Carlo simulations. The scheduler handles completing resources between different flows, such as runway allocations, runway crossing, departure fixes, and other interaction way points between arrivals and departures. Meanwhile, the scheduler also takes time-varied uncertainties into account when optimizing schedules. The scheduler is run sequentially to identify the best and robust schedule for the next planning window. Resulting schedules decide the routes, speed or delays, and runway assignments with separation constraints at mergingdiverging waypoints in the air and crossing and separations on runways. The Los Angels terminal area was used as an example. The implementation of this stochastic scheduler for integrated arrival, departure and surface operations is completed. And several preliminary runs are finished for over 1,200 flights in LAX in a typical day. Sensitivity studies on various planning window sizes are presented, which shows that trade-off exits between planning window size and achievable minimum delay. Preliminary results on runway usage are also presented in this abstract. Because arrivals on the outer runways have to be followed by crossings on the inner runways, algorithmic runway allocation prefers inner runways for arrivals and outer runways for departures. More results will be presented in the final paper. And current terminal arrival and departure procedures based on first-come-first-serve procedure will also be set up and used as a baseline for comparison.

integrated scheduler