多星测控调度效能评价指标体系研究

分析了建立多星测控调度问题评价指标体系的工程需求.从测控网管理方的角度,定义了反映测控系统服务能力、利用效率和资源配置合理性的指标;针对航天器拥有方的需求,建立了反映接受测控服务情况和反映航天任务满足程度的指标.通过两大部分指标构建了多星测控调度问题的指标体系.通过算例说明了利用该指标体系进行多星测控系统效能分析的可行性.     

多星联合鲁棒性调度模型研究

在多星联合调度过程中,存在着很多不确定性因素,其中云层覆盖变化是主要的不确定性来源.本文针对考虑云层覆盖变化的多星联合调度问题,借鉴了连续函数的鲁棒性优化思想,提出了一种基于邻域的鲁棒性指标.在此基础上,建立了多星联合鲁棒性调度模型,给出了观测任务优先级的计算方法.实例研究表明,本文建立的模型能够在保证调度方案性能的基础上,获得鲁棒性强的调度方案.     

多因素指派模型全局优化问题研究

基于多因素资源优化分配问题的不确定性,建立基于区间数型下的不确定多因素指派模型,给出模型建立的理论依据与全局优化算法,拓展区间数型多因素指派模型,解决了不确定条件下多因素资源优化分配问题.考虑多因素影响,基于任务完成效率,以5类任务多因素分配问题为例,获得了指派模型全局优化的解.为不确定条件下资源优化分配问题的研究拓宽了决策途径.     

属性具有线性优先级的区间数多属性决策方法

针对属性具有线性优先级、属性值为区间数的多属性决策问题进行了决策方法的研究.在属性权重的确定上,采用低级属性的权重值由所有比其高级属性效用值的乘积,并引入决策者的风险态度因子将区间数映射成点值,再经过规范化后得到.接着,给出了属性具有线性优先级的、属性值为区间数的加权算术平均算子,讨论了该算子的相关性质.最后,利用算子对具有线性优先级的区间数多属性决策问题进行算例分析,结果说明方法是有效的.     

基于不同优先级的油田增产多目标规划

为了衡量目标优先级不同对油田增产决策的影响,建立了基于不同优先级的多目标规划模型,并对增油量和措施费用两个目标不同优先级下的模型进行了求解,结果表明,目标优先级不同,油田增产措施的配置结果不同,但主要集中在对有效增产措施的配置上,对于效果较差的增产措施并无差异;同时,目标优先级不同导致目标的最优解相差较大,反映了决策者偏好对油田中后期增产决策的重要性.     

移动众包平台的任务酬金定价模型及调控策略研究——以珠三角地区为例

以珠三角地区移动众包平台为研究对象,实证分析了移动众包平台中的任务酬金定价模型和调控策略问题.首先使用GIS软件将移动众包平台中的用户和任务点的位置信息投射到真实地图,定性分析了影响任务点定价模型的交通、商圈等因素.然后建立以任务完成率最大和任务发布者收益最大的多目标任务酬金定价模型,使用熵权法确定模型中交通、商圈等因素的权值.最后借助贪心算法对上述模型进行求解,在分析部分任务未完成原因的基础上,提出了一种基于缓冲区打包的定价调控策略,通过实验分析,这种方法有助于提高任务的完成率和增加任务发布方的收益.     

多投入多产出效率测量与影响因素判断

多投入多产出效率的测量是极为重要的,而更为重要的是判断究竟是什么因素影响了效率,因为后者能为科学决策提供有力依据。本文提出按效率与影响因素相关系数最大化来确定投入与产出的权重,从而计算效率的方法,并同时识别了影响因素。本文还用该方法给出了一个算例,其结论颇有启发。结果表明,该方法对于多投入、多产出的效率问题的测量与多影响因素的判断相当有效。     

工程建设企业多项目人力资源配置研究——基于胜任力和两阶段优化视角

主要研究工程建设企业多项目人力资源优化配置问题。以项目经理这一关键性人力资源为对象,构建了基于胜任力模型的工程建设企业项目经理能力定级指标体系,给出了具体的能级评价方法,解决了待分配人员的定级和排序问题;在项目优先级和人员能级界定的基础上,以多项目工期均衡和人工总成本最低为目标建立了两阶段优化模型,给出了基于贪婪算法和线性规划的模型求解算法,设计了MATLAB程序对模型进行求解;最后通过实例验证了优化配置模型和求解算法的有效性。     

不确定环境下多自主体系统的分布式估计与控制

多自主体系统是当前系统控制界研究的热点问题. 在实际中, 自主体系统通常并不是在理想的环境下执行任务, 而是面临多源头、多层次和多变化的各类不确定性因素的影响. 它们通过在微观层面上影响各自主体决策的正确性, 从而在宏观上对自主体系统的整体行为产生显著影响. 不确定性因素和多自主体系统分布式信息架构交互耦合, 给系统的设计与分析带来本质性困难. 本文围绕分布式估计与分布式控制问题, 研究在随机通信噪声、数据丢失、量化和系统未知结构参数等不确定因素影响下, 如何为各自主体设计更加鲁棒、更加有效的分布式估计算法及分布式控制律, 以实现全局估计与控制目标, 并对闭环系统性能进行系统分析.     

基于灰色系统理论多资源网络计划分配的优化方法

本文在分析了现有的网络资源分配的几个优化准则和在工序排序过程中各种因素对工期产生影响的基础上，基于灰色系统理论，提出了以关联度为工序优先级的多资源网络计划分配问题的优化准则，并通过算例和与其它优化准则对比分析，说明依据该准则可使网络计划满足多资源约束条件，并使工期延长最少     

Multi-objective allocation of multi-function workers with lower bounded capacity

This paper deals with the assignment of a type of task to each member of a multi-function staff (each worker is able to perform a given subset of types of tasks, possibly with a priority index associated to each element of the subset). The resulting number of workers for each type of task must be not less than a given lower bound and as close as possible to another given value. The objectives are to minimize a function of the slacks and the surpluses of capacity, to distribute the slacks and the surpluses homogeneously among the types of task and to maximize the sum of priority indexes of the assignments. The problem is modelled as a nonlinear mixed integer program and is transformed and solved as a minimum cost flow problem.     

动态连续蚁群系统及其在天基预警中的应用

存在监控冲突的天基中段预警传感器调度优化是一个动态、高维、复杂多约束的非线性优化问题,其解空间的高维度与状态复杂性直接制约了智能优化算法的运用.本文以任务分解与任务复合优先权计算为基础,通过二级分离机制将解空间维度与状态复杂性降低至适于连续蚁群(continuous ant-colony optimization,CACO)处理的全局优化形态,构建出相应的优化子路径集.在此基础上,针对监控冲突导致的状态变化特性,从局部搜索递进与募集的角度提出适于传感器调度优化的MG-DCACO(double direction continuous ant-colony optimization based mass recruitment and group recruitment)算法,成功将智能优化算法应用于基于低轨星座的天基中段预警中.最后对算法的收敛性进行论证,并通过与已有规则调度算法的对比得出MG-DCACO算法可获得优于规则调度算法的全局最优解.     

A new heuristic for the multi-mode resource investment problem

This paper considers the problem of minimizing resource investment required to execute the tasks in a project network by a given project due date. A project consists of non-pre-emptive tasks executed in a known and required precedence order. Each task is completed in one of its feasible modes, which may differ not only in task duration but also in consumption of renewable resources. A priority rule heuristic with polynomial computational complexity is presented for this computationally intractable problem. This heuristic simultaneously considers due date constraints and resource usage to select and schedule tasks with one decision rule. This differs from prior multi-mode priority rule scheduling heuristics that apply two consecutive decision rules to schedule tasks. Extensive computational testing indicates promising results.     

基于遗传算法的浸染生产排缸策略

针对目前印染企业在浸染生产过程中产品种类和加工设备多、调度复杂的特性，建立了一种用于浸染生产调度的数学模型，并应用遗传算法进行排缸调度求解。以生产任务的分配优先级顺序作为染色体的编码来求解多个生产任务在多个染缸上的调度分配命题，从而得出了最优排缸策略，适用于快速、高效地解决实际生产中大量生产任务调度问题。仿真结果表明了该策略的有效性和实用性。     

Waiting time distributions in the accumulating priority queue

We are interested in queues in which customers of different classes arrive to a service facility, and where performance targets are specified for each class. The manager of such a queue has the task of implementing a queueing discipline that results in the performance targets for all classes being met simultaneously. For the case where the performance targets are specified in terms of ratios of mean waiting times, as long ago as the 1960s, Kleinrock suggested a queueing discipline to ensure that the targets are achieved. He proposed that customers accumulate priority as a linear function of their time in the queue: the higher the urgency of the customer’s class, the greater the rate at which that customer accumulates priority. When the server becomes free, the customer (if any) with the highest accumulated priority at that time point is the one that is selected for service. Kleinrock called such a queue a time-dependent priority queue, but we shall refer to it as the accumulating priority queue. Recognising that the performance of many queues, particularly in the healthcare and human services sectors, is specified in terms of tails of waiting time distributions for customers of different classes, we revisit the accumulating priority queue to derive its waiting time distributions, rather than just the mean waiting times. We believe that some elements of our analysis, particularly the process that we call the maximum priority process, are of mathematical interest in their own right.     

实时系统中单处理器调度算法的优化设计研究

研究实时系统的建模与调度问题是运筹与控制领域研究的热点问题, 对实时系统中的单处理器的调度算法进行了分析与研究, 特别是对其中的单调速率算法和最早时间限优先算法进行了深入的研究, 指出单调速率算法是一种典型的静态调度算法, 并且证明了单调速率算法是单处理器最优的静态优先级调度算法, 同时还指出最早时间限优先算法是一种典型的动态优先级调度算法,证明了最早时间限优先算法是单处理器的最优的动态优先级调度算法. 最后, 为了更好地进行实时系统的建模与调度, 引入了一种新的对任务执行行为进行抽象的方法--T-LET平面方法, 利用这种方法建立了单处理器流调度模型和BLREF调度算法, 并指出这种模型和算法都具有很强的几何背景.     

A hybrid adaptively genetic algorithm for task scheduling problem in the phased array radar

A phased array radar (PAR) is used to detect new targets and update the information of those detected targets. Generally, a large number of tasks need to be performed by a single PAR in a finite time horizon. In order to utilize the limited time and the energy resources, it is necessary to provide an efficient task scheduling algorithm. However, the existing radar task scheduling algorithms can't be utilized to release the full potential of the PAR, because of those disadvantages such as full PAR task structure ignored, only good performance in one aspect considered and just heuristic or the meta-heuristic method utilized. Aiming at above issues, an optimization model for the PAR task scheduling and a hybrid adaptively genetic (HAGA) algorithm are proposed. The model considers the full PAR task structure and integrates multiple principles of task scheduling, so that multi-aspect performance can be guaranteed. The HAGA incorporates the improved GA to explore better solutions while using the heuristic task interleaving algorithm to utilize wait intervals to interleave subtasks and calculate fitness values of individuals in efficient manners. Furthermore, the efficiency and the effectiveness of the HAGA are both improved by adopting chaotic sequences for the population initialization, the elite reservation and the mixed ranking selection, as well as designing the adaptive crossover and the adaptive mutation operators. The simulation results demonstrate that the HAGA possesses merits of global exploration, faster convergence, and robustness compared with three state-of-art algorithms—adaptive GA, hybrid GA and highest priority and earliest deadline first heuristic (HPEDF) algorithm.     

On search by address computation

We study the effect of data distribution on address computation data structures for searching, as typified by the priority queue problem. We compare several techniques showing that, in contrast to sorting, neither one nor multilevel bucket methods are uniformly efficient for this task. However, an enhancement of order preserving extendible hashing is shown to behave asymptotically independently of the amount of data and its distribution. Also conclusions regarding multiattribute file structures are presented.     

Simple heuristics for the assembly line worker assignment and balancing problem

We propose simple heuristics for the assembly line worker assignment and balancing problem. This problem typically occurs in assembly lines in sheltered work centers for the disabled. Different from the well-known simple assembly line balancing problem, the task execution times vary according to the assigned worker. We develop a constructive heuristic framework based on task and worker priority rules defining the order in which the tasks and workers should be assigned to the workstations. We present a number of such rules and compare their performance across three possible uses: as a stand-alone method, as an initial solution generator for meta-heuristics, and as a decoder for a hybrid genetic algorithm. Our results show that the heuristics are fast, they obtain good results as a stand-alone method and are efficient when used as a initial solution generator or as a solution decoder within more elaborate approaches.