相控阵雷达协同探测任务调度研究北大核心

针对相控阵雷达协同探测资源调度问题,提出了一种协同探测任务调度算法。首先介绍了相控阵雷达协同探测相关内容;接着给出了协同探测调度模型。针对多节点协同探测,提出一种优化模型,权衡时间偏移量和时间利用率2个因素,在不显著增大时间偏移量的前提下,提高时间利用率,提升探测性能。最后通过仿真验证了算法的有效性。     

相控阵雷达协同探测任务调度研究

针对相控阵雷达协同探测资源调度问题,提出了一种协同探测任务调度算法。首先介绍了相控阵雷达协同探测相关内容;接着给出了协同探测调度模型。针对多节点协同探测,提出一种优化模型,权衡时间偏移量和时间利用率2个因素,在不显著增大时间偏移量的前提下,提高时间利用率,提升探测性能。最后通过仿真验证了算法的有效性。     

面向多星观测调度的分层迭代算法

提出一种基于分治策略的多星观测分层调度框架,在该框架下,用蚁群优化算法把任务分配至各轨道圈次上,并利用自适应模拟退火算法求解各轨道圈次的调度问题。根据各轨道圈次调度结果的反馈情况,再调整任务分配方案,重复上述过程直到达到算法终止条件。为了提高算法的性能,在设计蚁群算法的启发式信息模型时,应充分考虑卫星调度问题的领域知识;在模拟退火算法中设计两个邻域结构,采用动态选择策略在优化过程中确定最佳邻域搜索结构。仿真实验表明,该方法有效地降低了问题求解的复杂度,尤其在求解大规模多星观测调度问题时表现出优异的性能。     

舰载多传感器协同探测资源调度模型构建

先对多传感器协同探测的概念、技术基础和优势进行了分析,构建了舰载多传感器协同探测体系结构,给出了传感器选配依据和协同策略规划,然后结合线性规划资源调度算法,建立了多传感器协同探测资源调度模型。通过仿真计算验证了模型的合理性和适用性,为舰载多传感器系统充分发挥协同探测的体系作战能力提供了一种理论和技术参考。     

装备维修任务调度研究综述

分析了装备维修任务调度需求及意义,综述了装备维修任务调度理论研究现状,围绕旅行商问题(Traveling Salesman Problem,TSP)、车辆路径问题(Vehicle Routing Problem,VRP)、项目调度问题(Project Scheduling Problem,PSP)和车间调度问题(Shop Scheduling Problem,SSP),综述了装备维修任务调度的典型模型及相应模型的求解算法,最后,从装备维修任务调度理论、调度策略、调度模型和调度算法4个方面提出了下一步装备维修任务调度研究的方向。     

嵌入式操作系统多策略调度模型

分析了目前嵌入式操作系统调度策略的现状,指出了传统调度方法的不足之处,给出了多策略调度模型,该模型根据进程的属性参数决定采用哪种调度算法。多策略调度模型采用两级调度方案,即在原传统调度方法的基础上增加一级调度。一级调度确定多个调度算法的优先顺序;二级调度确定同一种调度算法中,的进程优先顺序。该模型使进程调度更加灵活和高效,应用范围更广。     

军事虚拟仓库应急保障仿真分析

在分析应急物流研究成果基础上,探索应用虚拟仓库理论和仿真技术研究应急物流中的协同库存问题.为此,构建了军事虚拟仓库系统及其协同控制系统动力学仿真模型,并针对军民、军军仓库间的应急物流协同保障策略进行仿真分析,结果表明这种方法可以在应急状态下合理调度和管理各类仓库资源,改进应急物流条件下仓库保障能力.     

基于协同进化的多平台联合对地观测优化调度

在分析了卫星与无人机在执行观测与资源调度上的特性差异基础上,建立了多平台联合对地观测调度问题的数学模型,提出了多平台协同进化调度算法(MPCCPSA)进行求解。MPCCPSA采用分层式协同进化架构解决了不同类型观测方案统一调度生成问题。根据不同类型平台使用特性以及观测目标集合特点,采用分治-合作策略将其分解分配到各平台,顶层的交叉、变异操作保证各种群的多样性,底层的分治、合作算子保证卫星与无人机之间保持观测能力动态互补,在确保可行解的前提下加快收敛速度。仿真实验表明该方法能够有效解决空-天基多类型平台联合观测优化调度问题。     

不确定条件下多无人机侦察调度问题

结合动态目标的不确定性,构建了动态环境下多无人机协同搜索问题模型,并基于半随机式搜索策略的人工蜂群算法求解该模型。利用双重进化的特点,改进了插入点算子和逆转序列算子,在需要进行两点操作的搜索过程中,随机选取一点,另一点通过遍历可行解来确定最优解的位置。最后在某海域岛礁间距离之和的解空间维度上进行交叉搜索,并应用到局部搜索过程中构成双重进化,实验结果验证了所提出算法的有效性以及解决多无人机调度问题的可行性。     

基于多Agent无人机协同侦察任务规划模型

建立了多无人机协同侦察任务规划模型，在考虑侦察时间间隔约束和目标载荷需求基础上，给出了位于不同基地的无人机优化部署和调度策略，并提出了基于多Agent的优化搜索仿真算法，利用Matlab实现了无人机优化配置和任务规划模型，得出了无人机优化配置和任务规划方案，最后分析了模型算法存在的某些不足，提出了模型改进的方向。     

The resource constrained project scheduling problem with multiple crashable modes: An exact solution method

We introduce a formulation and an exact solution method for a nonpreemptive resource constrained project scheduling problem in which the duration/cost of an activity is determined by the mode selection and the duration reduction (crashing) within the mode. This problem is a natural combination of the time/cost tradeoff problem and the resource constrained project scheduling problem. It involves the determination, for each activity, of its resource requirements, the extent of crashing, and its start time so that the total project cost is minimized. We present a branch and bound procedure and report computational results with a set of 160 problems. Computational results demonstrate the effectiveness of our procedure. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 107–127, 2001     

A competitive genetic algorithm for resource-constrained project scheduling

In this paper we consider the resource-constrained project scheduling problem (RCPSP) with makespan minimization as objective. We propose a new genetic algorithm approach to solve this problem. Subsequently, we compare it to two genetic algorithm concepts from the literature. While our approach makes use of a permutation based genetic encoding that contains problem-specific knowledge, the other two procedures employ a priority value based and a priority rule based representation, respectively. Then we present the results of our thorough computational study for which standard sets of project instances have been used. The outcome reveals that our procedure is the most promising genetic algorithm to solve the RCPSP. Finally, we show that our genetic algorithm yields better results than several heuristic procedures presented in the literature. © 1998 John Wiley & Sons, Inc. Naval Research Logistics 45: 733–750, 1998     

Client‐contractor bargaining on net present value in project scheduling with limited resources

The client‐contractor bargaining problem addressed here is in the context of a multi‐mode resource constrained project scheduling problem with discounted cash flows, which is formulated as a progress payments model. In this model, the contractor receives payments from the client at predetermined regular time intervals. The last payment is paid at the first predetermined payment point right after project completion. The second payment model considered in this paper is the one with payments at activity completions. The project is represented on an Activity‐on‐Node (AON) project network. Activity durations are assumed to be deterministic. The project duration is bounded from above by a deadline imposed by the client, which constitutes a hard constraint. The bargaining objective is to maximize the bargaining objective function comprised of the objectives of both the client and the contractor. The bargaining objective function is expected to reflect the two‐party nature of the problem environment and seeks a compromise between the client and the contractor. The bargaining power concept is introduced into the problem by the bargaining power weights used in the bargaining objective function. Simulated annealing algorithm and genetic algorithm approaches are proposed as solution procedures. The proposed solution methods are tested with respect to solution quality and solution times. Sensitivity analyses are conducted among different parameters used in the model, namely the profit margin, the discount rate, and the bargaining power weights. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009     

A self‐adapting genetic algorithm for project scheduling under resource constraints

This papers deals with the classical resource‐constrained project scheduling problem (RCPSP). There, the activities of a project have to be scheduled subject to precedence and resource constraints. The objective is to minimize the makespan of the project. We propose a new heuristic called self‐adapting genetic algorithm to solve the RCPSP. The heuristic employs the well‐known activity list representation and considers two different decoding procedures. An additional gene in the representation determines which of the two decoding procedures is actually used to compute a schedule for an individual. This allows the genetic algorithm to adapt itself to the problem instance actually solved. That is, the genetic algorithm learns which of the alternative decoding procedures is the more successful one for this instance. In other words, not only the solution for the problem, but also the algorithm itself is subject to genetic optimization. Computational experiments show that the mechanism of self‐adaptation is capable to exploit the benefits of both decoding procedures. Moreover, the tests show that the proposed heuristic is among the best ones currently available for the RCPSP. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 433–448, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/nav.10029     

Maximizing project cash availability

Consider a project during the life cycle of which there are cash payouts and in‐flows. To better meet his financial commitments, the project owner would like to meet all deadlines without running out of cash. We show that the cash availability objective is similar to the total weighted flowtime used to measure work‐in‐progress performance in the scheduling and inventory control literatures. In this article we provide several specialized solution methods for the problem of minimizing total weighted flowtime in an arbitrary acyclic project network, subject to activity release times and due dates, where the activity weights may be positive or negative and represent cash in‐ and out‐flows. We describe the structure of an optimal solution and provide several efficient algorithms and their complexity based on mincost and maxflow formulations. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006     

Local search methods for the discrete time/resource trade-off problem in project networks

In this paper we consider the discrete time/resource trade-off problem in project networks. Given a project network consisting of nodes (activities) and arcs (technological precedence relations), in which the duration of the activities is a discrete, nonincreasing function of the amount of a single renewable resource committed to it, the discrete time/resource trade-off problem minimizes the project makespan subject to precedence constraints and a single renewable resource constraint. For each activity, a work content is specified such that all execution modes (duration/resource requirement pairs) for performing the activity are allowed as long as the product of the duration and the resource requirement is at least as large as the specified work content. We present a tabu search procedure which is based on a decomposition of the problem into a mode assignment phase and a resource-constrained project scheduling phase with fixed mode assignments. Extensive computational experience, including a comparison with other local search methods, is reported. © 1998 John Wiley & Sons, Inc. Naval Research Logistics 45: 553–578, 1998     

Case‐based reasoning and improved adaptive search for project scheduling

Most scheduling problems are notoriously intractable, so the majority of algorithms for them are heuristic in nature. Priority rule‐based methods still constitute the most important class of these heuristics. Of these, in turn, parametrized biased random sampling methods have attracted particular interest, due to the fact that they outperform all other priority rule‐based methods known. Yet, even the “best” such algorithms are unable to relate to the full range of instances of a problem: Usually there will exist instances on which other algorithms do better. We maintain that asking for the one best algorithm for a problem may be asking too much. The recently proposed concept of control schemes, which refers to algorithmic schemes allowing to steer parametrized algorithms, opens up ways to refine existing algorithms in this regard and improve their effectiveness considerably. We extend this approach by integrating heuristics and case‐based reasoning (CBR), an approach that has been successfully used in artificial intelligence applications. Using the resource‐constrained project scheduling problem as a vehicle, we describe how to devise such a CBR system, systematically analyzing the effect of several criteria on algorithmic performance. Extensive computational results validate the efficacy of our approach and reveal a performance similar or close to state‐of‐the‐art heuristics. In addition, the analysis undertaken provides new insight into the behaviour of a wide class of scheduling heuristics. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 201–222, 2000     

A genetic algorithm with neighborhood search for the resource‐constrained project scheduling problem

The resource‐constrained project scheduling problem (RCPSP) consists of a set of non‐preemptive activities that follow precedence relationship and consume resources. Under the limited amount of the resources, the objective of RCPSP is to find a schedule of the activities to minimize the project makespan. This article presents a new genetic algorithm (GA) by incorporating a local search strategy in GA operators. The local search strategy improves the efficiency of searching the solution space while keeping the randomness of the GA approach. Extensive numerical experiments show that the proposed GA with neighborhood search works well regarding solution quality and computational time compared with existing algorithms in the RCPSP literature, especially for the instances with a large number of activities. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

型号项目工期风险管理决策模型与自适应遗传算法

针对型号项目的过程特点,建立了型号项目工期风险的管理决策模型。模型考虑了型号项目中活动重叠、活动迭代、活动执行时间的不确定性和可更新资源总量等重要的工期风险影响因素。从模型的特点出发,给出了问题求解的基于自适应遗传算法的仿真优化方法。算例显示,该算法能较好地求解本文的工期风险管理决策问题。     

Sensitivity curves for effective project management

A key problem in project management is to decide which activities are the most important to manage and how best to manage them. A considerable amount of literature has been devoted to assigning “importance” measures to activities to help with this important task. When activity times are modeled as random variables, these activity importance measures are more complex and difficult to use. A key problem with all existing measures is that they summarize the importance in a single number. The result is that it is difficult for managers to determine a range of times for an activity that might be acceptable or unacceptable. In this paper, we develop sensitivity curves that display the most useful measures of project performance (in terms of schedule) as a function of an activity's time. The structure of the networks allows us to efficiently estimate these curves for all desired activities, all desired time ranges, and all desired measures in a single set of simulation runs. The resulting curves provide insights that are not available when considering summarized measures alone. Chief among these insights is the ability to identify an acceptable range of times for an activity that will not lead to negative scheduling consequences. © 2003 Wiley Periodicals, Inc. Naval Research Logistics 50: 481–497, 2003