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Scheduling NASA's Deep Space Network: Priorities, Preferences, and Optimization
NASA's Deep Space Network (DSN) is the primary resource for communications and navigation for interplanetary space missions, for both NASA and partner agencies. Growth in mission demand, both in number of spacecraft and in data return, has led to increased loading levels on the network, and actual demand frequently exceeds network capacity. The DSN scheduling process involves peer-to-peer collaborative negotiation, which consumes significant time and resources in order to reach a baseline version of the schedule, and then to manage and agree to changes. Process delays are exacerbated by the high level of oversubscription experienced by the DSN: it is not unusual for the scheduling process to start with 20-40\% more requested time can be accommodated on the available antennas. The other NASA networks make use of a static mission priority list to address a similar problem: missions are ranked in priority order, then the schedule is populated by priority from highest to lowest. Such a mechanism would not work for DSN due to the heterogeneity of the mission set, and to the time-varying mission requirements with mission phase. This paper describes an alternative approach for the DSN that addresses key problems inherent in the current process --- oversubsubscription and how to "fairly'" reduce it to a manageable level. The main characteristics of the new approach are the use of loading-based limits based on balancing requested time, along with priorities and user preferences as the basis for optimization criteria that can be used by new algorithms.
Johnston, Mark D↗
User Preference Optimization for Oversubscribed Scheduling of NASA’s Deep Space Network
NASA’s Deep Space Network (DSN) is the primary resource for communications and navigation for interplanetary space missions, for both NASA and partner agencies. Growth in mission demand, both in number of spacecraft and in data return, has led to increased loading levels on the network, and actual demand frequently exceeds network capacity. The DSN scheduling process involves peer-to-peer collaborative negotiation, which consumes significant time and resources in order to reach a baseline version of the schedule, and then to manage and agree to changes. Process delays are exacerbated by the high level of oversubscription experienced by the DSN: it is not unusual for the scheduling process to start with 20-40% more requested time can be accommodated on the available antennas. The other NASA networks make use of a static mission priority list to address a similar problem: missions are ranked in priority order, then the schedule is populated by priority from highest to lowest. Such a mechanism would not work for DSN due to the heterogeneity of the mission set, and to the time-varying mission requirements with mission phase. This paper describes an alternative approach for the DSN that addresses key problems inherent in the current process — oversubsubscription and how to “fairly” reduce it to a manageable level. The main characteristics of the new approach are the use of loading-based limits based on balancing requested time, along with priorities and user preferences as the basis for optimization criteria that can be used by new algorithms.
Johnston, Mark D↗
OMP-26 - An interactive scheduling tool for the 26 meter subnetwork
Operations Mission Planner for the 26 Meter (26M) Antenna Subnetwork (OMP-26) is an interactive scheduling tool developed by the Jet Propulsion Laboratory (JPL) to assist in allocating the Deep Space Network (DSN) 26M antennas to support Earth-orbiting Spacecraft (SC). Prior to integrating OMP-26 into the 26M scheduling arena, the text-based, labor-intensive task lacked the sufficient means to support the 1992 mission support increase. Therefore, JPL developed the OMP-26 interactive scheduling tool to automate the time consuming tasks of the existing, manual scheduling process. OMP-26 has addressed the scheduling dilemma by assisting schedulers to identify and resolve conflicts more quickly through its graphical display features and constraint-checking editing capability. Although OMP-26 has reduced the scheduler's existing workload by easing the identification and resolution of conflicts, it will not meet the needs of another mission support increase to occur by 1995. Therefore, future plans for OMP-26 involve transitioning from a solely interactive scheduling tool to one that is highly automated and more powerful.
Rosas, Jody L.↗
A Demand Access Paradigm for NASA’s Deep Space Network
NASA’s Deep Space Network (DSN) is the primary resource for communications and navigation for interplanetary space missions, for both NASA and partner agencies. As part of an investigation into improved efficiency and responsiveness, we have been exploring and prototyping the infusion of a ”demand access” model into the DSN scheduling process. Today, DSN is fully pre-scheduled in advance, and many users rely on a stable schedule to plan their own spacecraft activities, weeks in advance of execution. However, a new class of missions is emerging that may not be scheduled as far in advance, and may be event-driven in coming across science targets at unpredictable times. These users could take advantage of an on-demand mechanism to download data. Simulations have shown that such a mechanism could improve latency (time from data collection to download) by 2x, as well as more efficiently utilize the available DSN antennas. In this paper, we describe a prototype of a demand access process and how it addresses the challenges of co-existing with a staticallyscheduled body of missions, while providing the benefits of lower latency science data return.
Lucena, Girly↗
Gain Scheduling for the Orion Launch Abort Vehicle Controller
One of NASAs challenges for the Orion vehicle is the control system design for the Launch Abort Vehicle (LAV), which is required to abort safely at any time during the atmospheric ascent portion of ight. The focus of this paper is the gain design and scheduling process for a controller that covers the wide range of vehicle configurations and flight conditions experienced during the full envelope of potential abort trajectories from the pad to exo-atmospheric flight. Several factors are taken into account in the automation process for tuning the gains including the abort effectors, the environmental changes and the autopilot modes. Gain scheduling is accomplished using a linear quadratic regulator (LQR) approach for the decoupled, simplified linear model throughout the operational envelope in time, altitude and Mach number. The derived gains are then implemented into the full linear model for controller requirement validation. Finally, the gains are tested and evaluated in a non-linear simulation using the vehicles ight software to ensure performance requirements are met. An overview of the LAV controller design and a description of the linear plant models are presented. Examples of the most significant challenges with the automation of the gain tuning process are then discussed. In conclusion, the paper will consider the lessons learned through out the process, especially in regards to automation, and examine the usefulness of the gain scheduling tool and process developed as applicable to non-Orion vehicles.
McNamara, Sara J.↗
A System for Planning Ahead
A software system that uses artificial intelligence techniques to help with complex Space Shuttle scheduling at Kennedy Space Center is commercially available. Stottler Henke Associates, Inc.(SHAI), is marketing its automatic scheduling system, the Automated Manifest Planner (AMP), to industries that must plan and project changes many different times before the tasks are executed. The system creates optimal schedules while reducing manpower costs. Using information entered into the system by expert planners, the system automatically makes scheduling decisions based upon resource limitations and other constraints. It provides a constraint authoring system for adding other constraints to the scheduling process as needed. AMP is adaptable to assist with a variety of complex scheduling problems in manufacturing, transportation, business, architecture, and construction. AMP can benefit vehicle assembly plants, batch processing plants, semiconductor manufacturing, printing and textiles, surface and underground mining operations, and maintenance shops. For most of SHAI's commercial sales, the company obtains a service contract to customize AMP to a specific domain and then issues the customer a user license.
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Scheduling with chronology-directed search
The intelligent use of scheduling heuristics can enable the production of effective schedules in spite of the inherent intractability of scheduling in complex, real-world domains. In order to effectively use these heuristics, information is needed on the current state of the evolving schedule. One method to obtain this information is to use chronologies - limited histories of the scheduling process. Chronology-directed search is an important component of the heuristic approach to automated scheduling.
Biefeld, Eric W.↗
Scheduling System Assessment, and Development and Enhancement of Re-engineered Version of GPSS
The objective of this project is two-fold. First to provide an evaluation of a commercially developed version of the ground processing scheduling system (GPSS) for its applicability to the Kennedy Space Center (KSC) ground processing problem. Second, to work with the KSC GPSS development team and provide enhancement to the existing software. Systems reengineering is required to provide a sustainable system for the users and the software maintenance group. Using the LISP profile prototype code developed by the GPSS reverse reengineering groups as a building block, we have implemented the resource deconfliction portion of GPSS in common LISP using its object oriented features. The prototype corrects and extends some of the deficiencies of the current production version, plus it uses and builds on the classes from the development team's profile prototype.
Loganantharaj, Rasiah↗
Enabling Affordable Communications for the Burgeoning Deep Space Cubesat Fleet
The low costs of development and launch, coupled with new propulsive technologies, have made CubeSats increasingly popular for use in science investigations beyond geosynchronous orbit. As this deep space CubeSat fleet grows in size, the challenge of trying to provide affordable communications for it grows commensurately. The mass, power, and volume constraints inherent to CubeSats limit the antenna size and transmit power that they can use to close the deep space link. As a consequence, CubeSats need to rely more heavily on ground antennas that are characterized by large aperture, low noise temperatures, and relatively high-power transmitters. Such antennas are not in great abundance, nor are they inexpensive to build. For this reason, NASA’s Deep Space Network has been advocating a three-pronged approach to meeting anticipated CubeSat demand: development of simultaneous, shared-beam multi-spacecraft communications capabilities, development of large-antenna cross-support arrangements with other agencies and universities, and development of less uplink-intensive navigation techniques. This paper focuses on the pursuit of simultaneous, shared-beam multi-spacecraft communications capabilities. While the Multiple Spacecraft per Antenna (MSPA) technique has existed for over a decade, it has generally been limited to supporting downlink for just two in-beam spacecraft at a time. This limitation has largely been a function of the number and cost of available receivers. A relatively new technique that potentially overcomes this limitation is Opportunistic MSPA (OMSPA). Instead of relying on additional receivers, OMSPA makes use of a digital recorder at each ground station that is capable of capturing the intermediate frequency (IF) signals from every spacecraft in the antenna beam within the frequency bands of interest. When CubeSat projects see one or more opportunities for their CubeSat(s) to intercept the traditionally scheduled antenna beam of a “host” spacecraft, they can arrange for the CubeSat(s) to transmit open loop during those opportunities. Via a secure Internet site, the CubeSat mission operators can then retrieve the time- and frequency-relevant portions of the digital recording for subsequent demodulation and decoding, or subscribe to a service that does it for them. This “opportunistic” use of a host spacecraft’s ground antenna beam potentially enables CubeSat projects to make use of large ground antennas for downlink without having to compete with bigger, better-funded missions for antenna time in the formal scheduling process. In so doing, it also potentially enables CubeSat projects to avoid the aperture fees associated with formally scheduled downlink time - fees that factor into the “bottom-line” of competitively-bid NASA missions and that actually get charged to non-NASA missions. Taking advantage of these potential OMSPA benefits, however, will require CubeSat projects to pursue mission designs that ensure at least periodic in-beam operations relative to a “host” spacecraft. In the case of a constellation of CubeSats with inter-spacecraft distances that do not extend outside of the beam-width of the desired ground antenna at the given range, one CubeSat can serve as the “host” and have a formally scheduled downlink while the rest of the CubeSats can downlink essentially for “free” via OMSPA. Deep space CubeSats, of course, will need uplink in addition to downlink. Beyond commanding, this need is driven by the use of two-way ranging and Doppler for navigation. While OMSPA may not directly facilitate uplink, it does have the potential to free up antennas for those spacecraft that periodically require formally scheduled links for commanding and two-way radio metrics. NASA is also exploring the physical feasibility of an in-beam, simultaneous multi-spacecraft uplink technique. As with OMSPA, if successful, it will require little new equipment, further enabling affordable deep space CubeSat communications.
Abraham, Douglas S.↗
Anchorage Arrival Scheduling Under Off-Nominal Weather Conditions
Weather can cause flight diversions, passenger delays, additional fuel consumption and schedule disruptions at any high volume airport. The impacts are particularly acute at the Ted Stevens Anchorage International Airport in Anchorage, Alaska due to its importance as a major international portal. To minimize the impacts due to weather, a multi-stage scheduling process is employed that is iteratively executed, as updated aircraft demand and/or airport capacity data become available. The strategic scheduling algorithm assigns speed adjustments for flights that originate outside of Anchorage Center to achieve the proper demand and capacity balance. Similarly, an internal departure-scheduling algorithm assigns ground holds for pre-departure flights that originate from within Anchorage Center. Tactical flight controls in the form of airborne holding are employed to reactively account for system uncertainties. Real-world scenarios that were derived from the January 16, 2012 Anchorage visibility observations and the January 12, 2012 Anchorage arrival schedule were used to test the initial implementation of the scheduling algorithm in fast-time simulation experiments. Although over 90% of the flights in the scenarios arrived at Anchorage without requiring any delay, pre-departure scheduling was the dominant form of control for Anchorage arrivals. Additionally, tactical scheduling was used extensively in conjunction with the pre-departure scheduling to reactively compensate for uncertainties in the arrival demand. For long-haul flights, the strategic scheduling algorithm performed best when the scheduling horizon was greater than 1,000 nmi. With these long scheduling horizons, it was possible to absorb between ten and 12 minutes of delay through speed control alone. Unfortunately, the use of tactical scheduling, which resulted in airborne holding, was found to increase as the strategic scheduling horizon increased because of the additional uncertainty in the arrival times of the aircraft. Findings from these initial experiments indicate that it is possible to schedule arrivals into Anchorage with minimal delays under low-visibility conditions with less disruption to high-cost, international flights.
Grabbe, Shon↗
Iterative refinement scheduling
We present a heuristics-based approach to deep space mission scheduling which is modeled on the approach used by expert human schedulers in producing schedules for planetary encounters. New chronological evaluation techniques are used to focus the search by using information gained during the scheduling process to locate, classify, and resolve regions of conflict. Our approach is based on the assumption that during the construction of a schedule there exist several disjunct temporal regions where the demand for one resource type or a single temporal constraint dominates (bottleneck regions). If the scheduler can identify these regions and classify them based on their dominant constraint, then the scheduler can select the scheduling heuristic.
Biefeld, Eric↗
Cruise-Efficient Short Takeoff and Landing (CESTOL): Potential Impact on Air Traffic Operations
The National Aeronautics and Space Administration (NASA) is investigating technological and operational concepts for introducing Cruise-Efficient Short Takeoff and Landing (CESTOL) aircraft into a future US National Airspace System (NAS) civil aviation environment. CESTOL is an aircraft design concept for future use to increase capacity and reduce emissions. CESTOL provides very flexible takeoff, climb, descent and landing performance capabilities and a high-speed cruise capability. In support of NASA, this study is a preliminary examination of the potential operational impact of CESTOL on airport and airspace capacity and delay. The study examines operational impacts at a subject site, Newark Liberty Intemational Airport (KEWR), New Jersey. The study extends these KEWR results to estimate potential impacts on NAS-wide network traffic operations due to the introduction of CESTOL at selected major airports. These are the 34 domestic airports identified in the Federal Aviation Administration's Operational Evolution Plan (OEP). The analysis process uses two fast-time simulation tools to separately model local and NAS-wide air traffic operations using predicted flight schedules for a 24-hour study period in 2016. These tools are the Sen sis AvTerminal model and NASA's Airspace Concept Evaluation System (ACES). We use both to simulate conventional-aircraft-only and CESTOL-mixed-with-conventional-aircraft operations. Both tools apply 4-dimension trajectory modeling to simulate individual flight movement. The study applies AvTerminal to model traffic operations and procedures for en route and terminal arrival and departures to and from KEWR. These AvTerminal applications model existing arrival and departure routes and profiles and runway use configurations, with the assumption jet-powered, large-sized civil CESTOL aircraft use a short runway and standard turboprop arrival and departure procedures. With these rules, the conventional jet and CESTOL aircraft are procedurally separated from each other geographically and in altitude during tenninal airspace approach and departure operations, and each use a different arrival runway. AvTeminal implements its unique Focal-point Scheduling Process to sequence, space and delay aircraft to resolve spacing and overtake conflicts among flights in the airspace and airport system serving KEWR. This Process effectively models integrated arrival and departure operations. AvTerminal assesses acceptance rates and delay magnitude and causality at selected locations, including en route outer boundary fixes, tenninal airspace arrival and departure boundary fixes, terminal airspace arrival merge and departure diverge fixes, and runway landing and takeoff runways. The analysis compares the resulting capacity impacts, flight delays and delay sources between CESTOL and conventional KEWR operations. AvTerminal quantitative results showed that CESTOL has significant capability to increase airport arrival acceptance rates (35-40% at KEWR) by taking advantage of otherwise underused airspace and runways where available. The study extrapolates the AvTerminal-derived KEWR peak arrival and departure acceptance rates to estimate capacity parameter values for each of the OEP airports in the ACES modeling of traffic through the entire NAS network. The extrapolations of acceptance rates allow full, partial or no achievement of CESTOL capacity gains at an OEP airport as determined by assessments of the degree to which local procedures allow leveraging of CESTOL capabilities. These assessments consider each OEP airport's runway geometries, runway system configurations, airport and airspace operations, and potential CESTOL traffic loadings. The ACES modeling, simulates airport and airspace spacing constraints imposed by airport runway system, terminal and en route air traffic control and traffic flow management operations using airport acceptance rates representing conventional-aircraft-only and CESTOL-mixed operations. CEOL aircraft are assumed to have Mach 0.8, and alternatively Mach 0.7, cruise speeds to examine compatibility with conventional aircraft operations in common airspace. The ACES results provides estimates of CESTOL delay impact NAS-wide and at OEP airports due to changes in OEP airport acceptance rates and changes in en route airspace potential conflict rates. Preliminary results show meaningful nationwide delay reductions (20%) due to CESTOL operations at 34 major domestic airports.
Couluris, G. J.↗
Expert system for on-board satellite scheduling and control
An Expert System is described which Rockwell Satellite and Space Electronics Division (S&SED) is developing to dynamically schedule the allocation of on-board satellite resources and activities. This expert system is the Satellite Controller. The resources to be scheduled include power, propellant and recording tape. The activities controlled include scheduling satellite functions such as sensor checkout and operation. The scheduling of these resources and activities is presently a labor intensive and time consuming ground operations task. Developing a schedule requires extensive knowledge of the system and subsystems operations, operational constraints, and satellite design and configuration. This scheduling process requires highly trained experts anywhere from several hours to several weeks to accomplish. The process is done through brute force, that is examining cryptic mnemonic data off line to interpret the health and status of the satellite. Then schedules are formulated either as the result of practical operator experience or heuristics - that is rules of thumb. Orbital operations must become more productive in the future to reduce life cycle costs and decrease dependence on ground control. This reduction is required to increase autonomy and survivability of future systems. The design of future satellites require that the scheduling function be transferred from ground to on board systems.
Barry, John M.↗
Stochastic Modeling of Airlines' Scheduled Services Revenue
Airlines' revenue generated from scheduled services account for the major share in the total revenue. As such, predicting airlines' total scheduled services revenue is of great importance both to the governments (in case of national airlines) and private airlines. This importance stems from the need to formulate future airline strategic management policies, determine government subsidy levels, and formulate governmental air transportation policies. The prediction of the airlines' total scheduled services revenue is dealt with in this paper. Four key components of airline's scheduled services are considered. These include revenues generated from passenger, cargo, mail, and excess baggage. By addressing the revenue generated from each schedule service separately, air transportation planners and designers are able to enhance their ability to formulate specific strategies for each component. Estimation results clearly indicate that the four stochastic processes (scheduled services components) are represented by different Box-Jenkins ARIMA models. The results demonstrate the appropriateness of the developed models and their ability to provide air transportation planners with future information vital to the planning and design processes.
Hamed, M. M.↗
Stochastic Modeling of Airlines' Scheduled Services Revenue
Airlines' revenue generated from scheduled services account for the major share in the total revenue. As such, predicting airlines' total scheduled services revenue is of great importance both to the governments (in case of national airlines) and private airlines. This importance stems from the need to formulate future airline strategic management policies, determine government subsidy levels, and formulate governmental air transportation policies. The prediction of the airlines' total scheduled services revenue is dealt with in this paper. Four key components of airline's scheduled services are considered. These include revenues generated from passenger, cargo, mail, and excess baggage. By addressing the revenue generated from each schedule service separately, air transportation planners and designers arc able to enhance their ability to formulate specific strategies for each component. Estimation results clearly indicate that the four stochastic processes (scheduled services components) are represented by different Box-Jenkins ARIMA models. The results demonstrate the appropriateness of the developed models and their ability to provide air transportation planners with future information vital to the planning and design processes.
Hamed, M. M.↗
Precision Departure Release Capability (PDRC) Overview and Results: NASA to FAA Research Transition
NASA researchers developed the Precision Departure Release Capability (PDRC) concept to improve the tactical departure scheduling process. The PDRC system is comprised of: 1) a surface automation system that computes ready time predictions and departure runway assignments, 2) an en route scheduling automation tool that uses this information to estimate ascent trajectories to the merge point and computes release times and, 3) an interface that provides two-way communication between the two systems. To minimize technology transfer issues and facilitate its adoption by TMCs and Frontline Managers (FLM), NASA developed the PDRC prototype using the Surface Decision Support System (SDSS) for the Tower surface automation tool, a research version of the FAA TMA (RTMA) for en route automation tool and a digital interface between the two DSTs to facilitate coordination.
PDRC↗
Automated Long - Term Scheduling for the SOFIA Airborne Observatory
The NASA Stratospheric Observatory for Infrared Astronomy (SOFIA) is a joint US/German project to develop and operate a gyro-stabilized 2.5-meter telescope in a Boeing 747SP. SOFIA's first science observations were made in December 2010. During 2011, SOFIA accomplished 30 flights in the "Early Science" program as well as a deployment to Germany. The new observing period, known as Cycle 1, is scheduled to begin in 2012. It includes 46 science flights grouped in four multi-week observing campaigns spread through a 13-month span. Automation of the flight scheduling process offers a major challenge to the SOFIA mission operations. First because it is needed to mitigate its relatively high cost per unit observing time compared to space-borne missions. Second because automated scheduling techniques available for ground-based and space-based telescopes are inappropriate for an airborne observatory. Although serious attempts have been made in the past to solve part of the problem, until recently mission operations staff was still manually scheduling flights. We present in this paper a new automated solution for generating SOFIA long-term schedules that will be used in operations from the Cycle 1 observing period. We describe the constraints that should be satisfied to solve the SOFIA scheduling problem in the context of real operations. We establish key formulas required to efficiently calculate the aircraft course over ground when evaluating flight schedules. We describe the foundations of the SOFIA long-term scheduler, the constraint representation, and the random search based algorithm that generates observation and instrument schedules. Finally, we report on how the new long-term scheduler has been used in operations to date.
Civeit, Thomas↗