Optimizing in Advance Shenzhou 8 Space Mission Scheduling Tryout Architecture Xing Jinjiang1, Li Jian2, Zhu Hua3
Zhou Hong4, Feng Yuncheng5
Beijing Aerospace Control Center Beijing, China 1
[email protected], 2
[email protected], 3
[email protected]
School of Economics and Management Beijing University of Aeronautics and Astronautics Beijing, China 4
[email protected], 5
[email protected]
Abstract—To provide best scheduling solution for the Shenzhou 8 space mission, the scheduling algorithms and configurations are determined before actual mission platform developing using a tryout architecture in Beijing Aerospace Control Center. The architecture is designed agile and extendable so that various processes can be implemented to validate and optimize the algorithms rapidly. With this architecture, the scheduling solution is designed credible before applied in the formal mission scheduling mainframes.
C. Crew schedule Crew schedule is a kind of integrated schedule that indicates how the mission crew activities should be arranged. The crews may comprise taikonauts, spaceship specialists, payload specialists, space medics, spaceship operators, mission commands and leaders, and even other collaborative control centers.
Index Terms—space mission, scheduling, tryout, optimizing, Shenzhou, spaceship
A tryout of scheduling algorithm presumes uncertain algorithm descriptions and results, and it is also uncertain when and at what degree the algorithm will be valid and optimized. So a tryout process is usually a loop process, during which the required algorithm can be gradually improved.
I.
INTRODUCTION
Among the coming space missions of China, Shenzhou 8 will be a landmark one, not only for the brand-new rendezvous and docking, RVD, machineries but also for the unprecedented complexity of flight control requirements. So revolutionary methodologies, of which the most extraordinary is the mission scheduling aspect, are applied in Beijing Aerospace Control Center, BACC.
II.
SCHEDULING TRYOUT ARCHITECTURE
Although different scheduling algorithms can all be given usable, only the optimized ones promise best goals with minimum costs. Some algorithms also need sound configurations to make them as effective as possible. Hence a tryout architecture is used for determining the new scheduling algorithms. The most important schedules in space missions are nominal dictate schedule, dynamic tracking, telemetry and command system, TT&C, schedule and crew schedule. A. Nominal dictate schedule The nominal dictate schedule is a kind of primal schedule, which can be regarded as a schedule template. It can be used to generate practical dictate schedules according to actual orbit elements of spacecrafts. There will be more than 100 series of nominal dictate schedules in the Shenzhou 8 mission, which indicates automatic nominal scheduling algorithms. B. TT&C schedule Because of multiple objects in the RVD mission, the ground tracking stations need to be scheduled unitedly and dynamically to make full use of the limited TT&C resources without conflicts.
Figure 1. The scheduling tryout architecture
As shown in Figure 1, usually three departments are involved in the tryout architecture. A. Schedule specialists The schedule specialists are responsible for the overall scheduling solutions of the Shenzhou 8 mission. They are supposed to provide the optimized scheduling algorithms of the
three kinds of schedules. In most circumstances, the specialists are not quite sure about the availability and efficiency of the algorithms at the beginning, and it is why a tryout process is needed. B. Tryout platform designers The tryout platform designers are supposed to build the platform to implement, testify and solve the algorithms and configurations as soon as possible. C. Mission platform team The mission platform team’s work is directly oriented to practical mission. They only take the best solutions decided by the schedule specialists and implement them in the mission scheduling mainframes. III.
THE TRYOUT PLATFORM
As the core of the tryout architecture, in order to meet the needs of unconventional tryout calculations, the tryout platform must be designed with corresponding features. A. Agile Designing One basic requirement of the tryout platform is that it must be built quickly, usually in days, even hours, to respond to the schedule specialists. For this purpose, the tryout platform is usually designed with the most advanced developing tools such as Visual Studio 2010 with .NET 4.0 features as IDE, RegexBuddy as text analyzer and Infragistics NetAdvantage as control library. Although the newest versions imply unknown developing risks, the new features such as Regular Expressions, Microsoft IntelliTrace and Parallel LINQ surely quicken the developing process. The tryout platform doesn’t have to be as mature as a final product, as long as it is usable for the tryouts.
commercial control libraries, all these data presenting method can be realized very rapidly. D. Tryout Process Controlling For flexibility considerations, the process of tryouts is defined in neither algorithms nor the tryout platform, but can be controlled by user data. The schedule specialists are allowed provide process definitions in the user forms. And then the tryout platform would call corresponding algorithms in certain sequence and for certain number of times. As a typical example, the number of population and generation, replication rate, mutation rate and cross rate are all suited to control a genetic process for solving a scheduling algorithm. E. Algorithm Tracing To help validating and evaluating, all the execution traces of the scheduling algorithms are logged in the source tracing fields of the schedule entries. Better than traditional console messages, grid based tracing is helpful. As illustrated in Figure 2, by clicking the text link in the Source Trace column of the crew schedules, the corresponding entry in the scheduling rule stack that generates this schedule entry will be highlighted and expanded to its detailed parameter list. In this way, the scheduling specialists can conveniently examine the interrelated results and sources.
B. Extendable feature Since multiple scheduling algorithms need to be compared and more improved ones will be added, the tryout platform is designed with the flexibility to switch between multiple algorithm candidates. The core tryout platform is usually delivered to the schedule specialists soon and can be a steady system for long term use, even without any available scheduling algorithms at the beginning. All the tryout scheduling algorithms will be proposed by the schedule specialists and implemented by the tryout platform designers. As a kind of reinforcement, the algorithms are delivered as platform plugins, which are usually DLL function libraries and dynamic UI forms. The tryout platform can load the plugins dynamically at runtime, even without restarted. The core of the tryout platform is the root, of which the most essential function is integrating the plugins. While all the algorithms, UIs and statistics are branches that could be added to the extendable system. C. Data presenting As a basic tryout need, the data should be presented in the most intuitionistic way. Data grids are the essential presenting manner, besides which timelines and statistic charts can be used to help reading, understanding and analyzing. Using
Figure 2. Crew scheduling algorithm execution tracing
F. Errors and Conflics Marking It is quite hard to check all the generated schedules manually, so the tryout platform is required to be able to check and mark the specified data automatically. The schedule specialists define what and how to check, which will be implemented in grids of the tryout platform, to
help themselves locate errors and conflicts in the schedule results. In this way, all the error and conflict inspecting calculations will be performed by the tryout platform automatically.
process of loops, both the algorithm descriptions and the implemented codes evolve steadily. V.
TRYOUT CASE: SHENZHOU 8 TRACKING STATION FREQUENCY SCHEDULING
In the Shenzhou 8 mission, there will be two conventional frequencies, say F1 and F2, used in the TT&C tracking stations to send control dictates upward to the spaceship. The spaceship must not receive dictates from different stations with the same frequency at the same time to avoid conflicts that may cause damage to it. So a scheduling algorithm is needed to control the stations switching between F1 and F2. In 200 circles of flight, the less the switch, the better the schedule is, meanly the number of switches is the target value to optimize. Figure 3. Highlighted mark where errors and conflics found
In Figure 3, two i12 dictates are scheduled with no usable station, which can be considered as a failure of current scheduling algorithm. The grid marks these two cells with red background using Value Based Appearances feature so that the schedule specialists can easily find them instead of checking row by row. G. Data Importing and Exporting Usually, the tryout platform is provided with grids that can paste data from whatever other applications and documents so that the schedule specialists need not type the data more than once by hand. Some tryout schedule results can be directly used in mission documents or for execution. So the tryout system is also designed to export data according with mission platform interfaces, usually in XML and spreadsheet files. IV.
In the beginning, the schedule specialists proposed two algorithms, of which one is the By-last-circle principle and the other Fixed-station. To make clear which is better, the specialists requested a tryout system. The system was built within three days with the calculation results showing that the target value of the By-last-circle algorithm reached 114 and Fixed-station 97. Later, the Fixed-station algorithm was enhanced by the schedule specialists according to earlier results and proposed as By-priority algorithm, which was added to the system as a plugin within 4 hours. This new algorithm was different from the former two, for it needed a station priority list as scheduling guidance. The specialists gave an initial priority list, with which the By-priority algorithm achieved 93. But the specialist was not sure whether it was good enough. So a genetic process was added to find the possible best priority list. The By-priority algorithm with the optimized priority list defined in Table I achieved 50 in the end, meanly once every 4 circles and no more than 0.14 switches per station per circle. It was quite an improvement.
ALGORITHM TRYOUT PROCESS
A tryout implies validation and optimization. An algorithm can hardly be identified correct or efficient when proposed for the first time by the schedule specialists. Only by implemented in codes, the internal problems can be revealed by the tryout platform designers and reported to the schedule specialists for revising. If an algorithm is correct, it is then capable to generate valid schedules. The schedule specialists evaluate the implemented algorithms with the aid of the tryout system by checking the generated schedules and statistics in both digits and charts. In most circumstances, an optimized algorithm can be confirmed just by generating schedules with better target value than others. There are nevertheless situations that the algorithms output the schedules and target values randomly according to the variable orbit elements and TT&C resource occupations. And then the evaluation of algorithms comes down to a batch of testing according to Monte-Carlo principle. By inspecting original algorithms, new and improved ones are proposed, along which the plugins are renewed. In this
TABLE I. Priority 1 2 3 4 5 6 8 10 11 12 13 14 15 16 17 18
OPTIMIZED PRIORITY LIST
Station Inland B Inland A Inland C Inland E Inland F Inland D Ship A Ship C Overseas G Ship B Overseas A Overseas F Overseas B Overseas C Overseas D Overseas E
Preferred frequency F2 F2 F2 F1 F1 F1 F2 F1 F1 F1 F2 F1 F1 F2 F1 F1
Before decisions made, another algorithm called Lessswitching was tested, with a result of 165, which was the worst eventually.
an engineering process which minimize mission risks by testifying the methods before mainframe developing. The optimized algorithms and configurations ensure best mission goals and minimum mission costs, as far as the schedule specialists together with the tryout process can provide. ACKNOWLEDGMENT This project is funded by China Postdoctoral Science Foundation. REFERENCES [1] [2] Figure 4. Comparison of number of switch [3]
The By-priority algorithm together with the optimized priority list was delivered in both descriptions and source codes to the mission platform team as a part of the final tracking station frequency scheduling solution. This tryout case is introduced in detail in [6]. VI.
[4]
[5]
CONCLUSION
The tryout architecture introduced in this paper is essential for the coming new model of space missions. With it, all the uncertain aspects in scheduling solution design is discovered and solved beforehand. It is an academic process that bring the system engineering methodologies into actual mission, and also
[6]
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