基于组合优化的停机位分配模型研究

停机位分配（ASA）是机场生产调度的核心，也是一类典型的组合优化问题。针对以往研究在优化建模方面存在的问题，较全面地考察了实际ASA过程的约束条件，以旅客在机场所移动距离总和最小化作为目标函数，建立了ASA的整数规划模型，并对该模型的求解和验证技术进行了讨论。     

Analysis and modeling of information flow and distributed expertise in space-related operations

Evolving space operations requirements and mission planning for long-duration expeditions require detailed examinations and evaluations of information flow dynamics, knowledge-sharing processes, and information technology use in distributed expert networks. This paper describes the work conducted with flight controllers in the Mission Control Center (MCC) of NASA's Johnson Space Center. This MCC work describes the behavior of experts in a distributed supervisory coordination framework, which extends supervisory control/command and control models of human task performance. Findings from this work are helping to develop analysis techniques, information architectures, and system simulation capabilities for knowledge sharing in an expert community. These findings are being applied to improve knowledge-sharing processes applied to a research program in advanced life support for long-duration space flight. Additional simulation work is being developed to create interoperating modules of information flow and novice/expert behavior patterns.     

基于图染色理论的并行测试任务调度算法

目前的自动测试系统大多数采用串行测试的工作方式,测试效率很低.针对这个问题,建立了基于图论的测试任务关系模型,用"图"来描述测试任务占用仪器资源的情况,将测试任务调度的工程问题转化为图论中的数学问题.在测试任务关系模型的基础上,提出了两个任务调度算法:CTG算法和CTG-T算法.对于多个测试任务,利用这两个算法可以得到并行度最大或者测试时间最短的任务分组方案,能有效地实现并行测试.这两个算法是基于图的染色理论得到的,对其正确性进行了理论分析和实例仿真.两个算法已经在实际系统中得到了实验验证,结果表明能够大大提高自动测试系统的测试效率.      

人工智能在卫星任务规划中的应用

智能规划与调度是实现卫星自主规划其飞行任务的关键。将人工智能方法应用于卫星的任务规划,以执行对地观测任务为例,将任务规划问题归于组合优化问题,建立了相应的数学模型,并应用Hopfield神经网络算法进行求解,结果表明,该方法可用于单个仪器的任务规划。     

Multi-UAV reconnaissance task allocation for heterogeneous targets using an opposition-based genetic algorithm with double-chromosome encoding

This paper presents a novel multiple Unmanned Aerial Vehicles (UAVs) reconnaissance task allocation model for heterogeneous targets and an effective genetic algorithm to optimize UAVs’ task sequence. Heterogeneous targets are classified into point targets, line targets and area targets according to features of target geometry and sensor’s field of view. Each UAV is regarded as a Dubins vehicle to consider the kinematic constraints. And the objective of task allocation is to minimize the task execution time and UAVs’ total consumptions. Then, multi-UAV reconnaissance task allocation is formulated as an extended Multiple Dubins Travelling Salesmen Problem (MDTSP), where visit paths to the heterogeneous targets must meet specific constraints due to the targets’ feature. As a complex combinatorial optimization problem, the dimensions of MDTSP are further increased due to the heterogeneity of targets. To efficiently solve this computationally expensive problem, the Opposition-based Genetic Algorithm using Double-chromosomes Encoding and Multiple Mutation Operators (OGA-DEMMO) is developed to improve the population variety for enhancing the global exploration capability. The simulation results demonstrate that OGA-DEMMO outperforms the ordinary genetic algorithm, ant colony optimization and random search in terms of optimality of the allocation results, especially for large scale reconnaissance task allocation problems.     

面向数字化装配生产过程的动态调度方法

针对数字化装配实际生产过程中动态扰动因素造成静态调度失效问题,建立面向数字化装配生产过程的动态调度模型。模型考虑了机器故障、人员流失和不合格产品等动态扰动对装配过程的影响,建立飞机大部件数字化装配工艺流程描述模型。在此基础上计算预调度方案,扰动产生时刻重新计算调整预调度方案。将资源利用率、平均生产率和作业平衡系数等指标作为优化目标,结合多目标优化遗传算法求解模型的Pareto最优解,得到多目标优化的装配作业调度方案。     

Limit protection design in turbofan engine acceleration control based on scheduling command governor

A new limit protection method based on Scheduling Command Governor (SCG) is proposed for imposing multiple constraints on a turbofan engine during acceleration process. A Gain Scheduling Controller (GSC) is designed for the transient state control and its stability proof is developed using Linear Matrix Inequalities (LMIs). The SCG is an add-on control scheme which manages engine limits effectively based on reference trajectory optimization. Unlike the traditional min–max architecture with switching logic, the SCG method utilizes the Linear Parameter Varying (LPV) closed-loop model to form a prediction of future constraint violation and per instant solves a constraint-admissible reference within an approximate Maximal Output Admissible Set (MOAS). The influence of the variation of engine dynamic characteristics and equilibrium points during transient state control is handled by the design of contractive sets. Simulation results on a turbofan engine component-level model show the applicability and effectiveness of the SCG method. Compared to the traditional min–max method, the SCG method has less conservativeness. In addition, the design of contractive sets makes conservativeness tunable.     

Comparative analysis of global optical observability of satellites in LEO

With a growing number of resident space objects (RSOs), the facilities for near-Earth space surveillance have to cope with increasing workload. It also applies to low-cost small optical surveillance facilities which may present regional, national and global networks. Improved methods of planning and scheduling optical telescopes are required to use these instruments efficiently. Today, optical observations are only feasible if the following quite stringent requirements are met: the object should be illuminated by sunlight, and it should be above while the Sun is below the observer’s horizon. For different orbits, these preconditions result in varying degrees of the space object observability at various ground-based sites. Certainly, satellites in low Earth orbit (LEO) are particularly difficult to observe. This study aims at developing a new technique for assessing observability of a satellite in different types of orbits – namely, low, medium and high Earth orbits, imaging of the opportunity for its visibility in respective diagrams and their analysing for the existing near-Earth population of RSOs. Unlike other researches, wherein one or several observational stations have been chosen as target sites for in-depth analyses of visibility of all the satellites or just the selected ones, the present study focuses on examining the probability of optical surveillance of satellites in a certain orbit from any locations worldwide. It offers considerable scope for automation of surveillance planning and scheduling optical surveillance networks.     

空间目标光电巡天策略的优化

随着空间目标与碎片数量的激增,传统的基于目标跟踪的空间目标光电监视手段已不能满足需求,近年出现了面向空域的空间目标巡天观测。针对空间目标巡天观测的特点,同时考虑空间目标观测的目标个数和数据量,提出了一种空间目标巡天观测策略,并建立了相应的空间目标光电巡天优化数学模型。模型中通过0-1变量的使用,实现了目标函数和约束条件的线性化,十分有利于大规模问题的求解,能够更好地满足实际需求。仿真计算表明:策略简单,易于实现,并且有很好的效果;通过优化,只需要少量望远镜即能实现大批量空间目标的监视,并保持一定的观测数据量,为空间目标光电监视策略提供了新的思路。     

基于多分区操作系统的多核确定性调度方法设计

为提高多分区操作系统环境下多核处理器的应用带来的操作系统任务调度的确定性,研究了多分环境下多核操作系统结构,从处理器核心和分区的资源分配和分区调度表的时间同步角度出发,采用了静态配置、处理器绑定和时间窗口调度方法,实现了处理器资源的确定性分配,解决了调度表之间的时间同步问题。测试结果表明,方法有效提高了分区环境下多核操作系统任务调度的确定性。