基于统计性QoS保证的数据包延时界调度算法

讨论了调度算法的性能指标,对目前基于业务流的调度算法的技术特点与性能优劣进行了分析和比较.重点研究了基于时延和丢包率的算法,并提出了一种基于数据包延时界(PDB)排列的调度策略,与WFQ及传统EDF算法进行了比较,证明业务端到端超时概率随网络带宽利用率的变化性能优于传统EDF算法.     

Maintenance and flight scheduling of low observable aircraft

In this article, we focus on relatively new maintenance and operational scheduling challenges that are faced by the United States Air Force concerning low‐observable (LO) or stealth aircraft. The LO capabilities of an aircraft degrade stochastically as it flies, making it difficult to make maintenance scheduling decisions. Maintainers can address these damages, but must decide, which aircraft should be put into maintenance, and for how long. Using data obtained from an active duty Air Force F‐22 wing and interviews with Air Force maintainers and program specialists, we model this problem as a generalization of the well‐known restless multiarmed bandit superprocess. Specifically, we use an extension of the traditional model to allow for actions that require varying lengths of time, and generate two separate index policies from a single model; one for maintenance actions and one for the flying action. These index policies allow maintenance schedulers to intuitively, quickly, and effectively rank a fleet of aircraft based on each aircraft's LO status and decide, which aircraft should enter into LO maintenance and for how long, and which aircraft should be used to satisfy daily sortie requirements. Finally, we present extensive data‐driven, detailed simulation results, where we compare the performance of the index policies against policies currently used by the Air Force, as well as some other possible more naive heuristics. The results indicate that the index policies significantly outperform existing policies in terms of fully mission capable (FMC) rates. In particular, the experiments highlight the importance of coordinated maintenance and flying decisions. © 2015 Wiley Periodicals, Inc. 62:60–80, 2015     

考虑航线交叉的救援直升机起飞时序规划方法

为解决多架救援直升机的起飞时序规划问题,以最小化最后一架救援直升机的起飞时间为优化目标,建立多直升机多起降点的数学规划模型。设计了基于任务优先级的快速启发式算法,提出航线交叉点的处理方案,给出起飞时间求解算法。以云南鲁甸6.8级地震的灾后救援为背景,设计了包含24架直升机和12个起飞点的起飞时序规划案例,对模型和算法进行了仿真验证,并对航线交叉的影响与处理措施进行了深入讨论。实验结果表明该模型和方法能有效解决多架救援直升机的起飞时序规划问题。     

基于改进遗传算法的舰载机出库调度优化方法

舰载机出库最优调度方案是合理编排一批次舰载机出库顺序,使目标舰载机在最短的时间内调出机库。提出舰载机出库调度方案4步单目标优化方法。第1步实现舰载机数量分配功能;第2步依靠遗传算法,实现舰载机选择功能,寻找最优出库组合方案;第3步根据优先级规则对舰载机出库作业进行排序,从而得到最优出库方案的舰载机调度序列;第4步找出需要倒机的舰载机并根据一定规则进行整理后,得到出库作业序列。最后,建立模拟机库验证调度方案的优化方法可行、高效。     

面向网络化无人机集群的计算换通信分布式任务调度方法

针对网络化无人机集群任务自主协同问题以及市场竞拍法的优缺点,提出“计算换通信”思想及其相应的分布式任务调度方法。通过对显式和隐式冲突任务的分析,建立任务相关智能体集合。提出基于任务抑制的局部优化方法,用于提前消解部分任务冲突,以减少算法迭代次数。设计基于历史竞标信息的智能体位置推断法,为局部优化提供必要的信息输入。基于组网仿真平台与集群救援场景开展蒙特卡罗仿真实验,结果表明,相比于市场竞拍法中具有代表性的基于共识的捆绑算法和性能影响算法,所提方法能够获得更少的迭代次数、更短的收敛时间以及更优的调度性能。     

面向计算流体力学的图形处理器资源管理

针对求解计算流体力学过程中图形处理器资源利用率低的问题,提出面向计算流体力学的图形处理器资源优化管理方案。基于计算流体力学的算法特性和同时运行任务的执行特点,设计合理的调度方案。通过动态改变不同任务的启动规模和启动时间,在减少资源竞争的同时提高图形处理器资源的有效使用。实验结果表明:本文提出的资源管理方案相比基线方法在不同任务规模下的平均加速比达到 1.64,对图形处理器的硬件资源使用也有了显著的提升。     

Efficient algorithms for flexible job shop scheduling with parallel machines

Job shop scheduling with a bank of machines in parallel is important from both theoretical and practical points of view. Herein we focus on the scheduling problem of minimizing the makespan in a flexible two-center job shop. The first center consists of one machine and the second has k parallel machines. An easy-to-perform approximate algorithm for minimizing the makespan with one-unit-time operations in the first center and k-unit-time operations in the second center is proposed. The algorithm has the absolute worst-case error bound of k − 1 , and thus for k = 1 it is optimal. Importantly, it runs in linear time and its error bound is independent of the number of jobs to be processed. Moreover, the algorithm can be modified to give an optimal schedule for k = 2 .     

雷达装备战场抢修支援分队调度决策研究

针对雷达装备战场抢修支援分队支援抢修多个现场的损伤雷达装备,以及抢修过程中可能出现其它现场损伤雷达装备请求支援抢修的调度问题,以实现每次支援抢修损伤雷达装备都获得最大的战斗力恢复率为目标,提出了一种类似于计算机中断过程的调度决策方法。调度决策中的抢修中断优先级判定方法,也可作为抢修支援分队确定支援抢修多个现场损伤雷达装备初始次序的依据。最后,通过实例应用验证了调度决策方法的实用性和可行性。     

电子侦察卫星联合侦察的混合调度算法

针对电子侦察卫星的使用约束,及不同任务的调度需求,建立了电子侦察卫星联合侦察的多目标混合整数规划模型.利用进化算法的全局搜索能力和变邻域搜索的局部优化能力,提出了一种多目标进化算法和变邻域搜索相结合两阶段混合调度算法MOEA VNS.针对问题多时间窗组合优化特点,设计了进化算子与邻域移动算子,在确保解多样性的同时使算法...     

Bounded flexibility in days‐on and days‐off scheduling

This article presents a flexible days‐on and days‐off scheduling problem and develops an exact branch and price (B&P) algorithm to find solutions. The main objective is to minimize the size of the total workforce required to cover time‐varying demand over a planning horizon that may extend up to 12 weeks. A new aspect of the problem is the general restriction that the number of consecutive days on and the number of consecutive days off must each fall within a predefined range. Moreover, the total assignment of working days in the planning horizon cannot exceed some maximum value. In the B&P framework, the master problem is stated as a set covering‐type problem whose columns are generated iteratively by solving one of three different subproblems. The first is an implicit model, the second is a resource constrained shortest path problem, and the third is a dynamic program. Computational experiments using both real‐word and randomly generated data show that workforce reductions up to 66% are possible with highly flexible days‐on and days‐off patterns. When evaluating the performance of the three subproblems, it was found that each yielded equivalent solutions but the dynamic program proved to be significantly more efficient. © 2013 Wiley Periodicals, Inc. Naval Research Logistics 60: 678–701, 2013