| 张俊新,胡梅,钟文安,孙乐园,胡鹏,叶欣,晏政.基于遗传算法的航天发射场多任务并行规划方法[J].电子测量与仪器学报,2025,39(5):117-124 |
| 基于遗传算法的航天发射场多任务并行规划方法 |
| Multi task parallel planning method for space launchsites based on genetic algorithm |
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| DOI： |
| 中文关键词: 多任务并行规划 航天发射任务规划 航班化发射 高效发射 遗传算法 |
| 英文关键词:multi task parallel planning space launch mission planning airline-flight-mode efficient launch genetic algorithm |
| 基金项目: |
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| Author | Institution |
| Zhang Junxin | 1.School of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China;
2.Xichang Satellite Launch Center, Haikou 571100, China |
| Hu Mei | School of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China |
| Zhong Wenan | Xichang Satellite Launch Center, Haikou 571100, China |
| Sun Leyuan | Xichang Satellite Launch Center, Haikou 571100, China |
| Hu Peng | Xichang Satellite Launch Center, Haikou 571100, China |
| Ye Xin | Xichang Satellite Launch Center, Haikou 571100, China |
| Yan Zheng | Xichang Satellite Launch Center, Haikou 571100, China |
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| 中文摘要: |
| 当前，航天发射建设规模不断扩大，建设多个测试发射的设备设施后，发射场面临多枚运载火箭并行测试的任务规划问题。运载火箭进场、测试、总装、转运和加注发射分别在不同的测试区域完成，由于型号差异，某些测试区域可以共用，某些不能共用，且同一测试区域能够容纳的运载火箭有限（通常仅能容纳1枚），在这些约束条件下，如何在尽可能短的时间内完成多任务并行的计划安排是必须解决的重要问题。通过对国内外相关问题研究的分析，梳理了2000年以来国内航天发射场测试发射工艺流程设计和优化的方法，现行的“双代号网络计划图”难以适应多任务并行规划需要，关键路径法、价值链分析法等缺乏定量分析能力。结合国内航天发射场规划问题的难点，采用遗传算法，通过双层编码方式，根据并行任务数量确定种群规模和迭代次数，以航天发射场任务规划的目标函数作为算法适应度计算函数。经过算例验证，可以得到可供工程应用的多任务并行规划较为优化的方案，求解5枚火箭任务并行规划方案用时<1 min，较传统手工绘制双代号网络计划图的方式效率大幅提升。方法具有一定的通用性和扩展性，可以根据不同火箭任务的流程对编码方法进行设置和细化，从而提高算法的实用性。 |
| 英文摘要: |
| With the rapid expansion of space launch infrastructure, modern launch sites face the critical challenge of parallel task scheduling for multiple launch vehicles (LVs) undergoing concurrent testing. The process involves sequential phases—LV arrival, testing, final assembly, transfer, and propellant loading/launch—each requiring dedicated or shared test facilities. Due to variations in LV configurations, certain test areas are mutually exclusive, while others have limited capacity (typically accommodating only one LV at a time). Under these constraints, achieving efficient multi mission parallel scheduling to minimize total completion time has become an urgent operational requirement. Analysis of domestic and international research since 2000 reveals that traditional methods, such as dual-code network diagrams, are inadequate for parallel mission planning. Conventional approaches like the critical path method (CPM) and value chain analysis lack robust quantitative capabilities for handling complex resource conflicts. To address these limitations in China’s space launch scheduling, this study proposes a genetic algorithm (GA)-based framework with a dual-layer encoding scheme. The algorithm dynamically adjusts population size and iteration counts based on the number of parallel missions, while the fitness function directly corresponds to the scheduling objective: minimizing mission duration under facility constraints. Case studies demonstrate the method’s efficacy. For a scenario involving five LVs, the algorithm generates optimized parallel schedules in under one minute, significantly outperforming manual dual-code network diagram construction. The proposed approach exhibits notable universality and extensibility: the encoding scheme can be customized to accommodate diverse LV workflows, enhancing practical applicability. |
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