维修任务调度的多目标决策模型

通过对维修调度问题进行分析,考虑到战斗力相关的多种目标因素,针对目前研究的目标单一,又未考虑武器的先进程度、配置等缺陷,就修复后的加权作战时间和装备尽可能少的延误数量双重目标加以研究,并给出了调度算法。从而弥补了这些缺陷,使调度更加合理。最后,算例表明模型的求解方法简单,计算时间较短,可有效地实现维修任务调度。     

水下传感器网络部署优化研究

水下传感器网络具有广阔的应用前景,但也存在能量有限、延迟较长、部署区域不连通和通信能耗大等问题,理论和实践证明传感器节点的优化部署是解决已上问题的有效方法。为此综合考虑水下传感器网络的工作环境及传感器特性,建立了提高网络可靠性的网络部署模型,给出了网络部署的成本函数及约束条件,并使用遗传算法对传感器节点和中转节点的部署成本及网络生命周期内的能量消耗进行优化。通过仿真实验表明,该模型收敛速度快,部署节点结果合理,达到了较好的优化网络的目的。     

A branch‐and‐price algorithm for parallel machine scheduling with time windows and job priorities

This paper presents a branch‐and‐price algorithm for scheduling n jobs on m nonhomogeneous parallel machines with multiple time windows. An additional feature of the problem is that each job falls into one of ρ priority classes and may require two operations. The objective is to maximize the weighted number of jobs scheduled, where a job in a higher priority class has “infinitely” more weight or value than a job in a lower priority class. The methodology makes use of a greedy randomized adaptive search procedure (GRASP) to find feasible solutions during implicit enumeration and a two‐cycle elimination heuristic when solving the pricing subproblems. Extensive computational results are presented based on data from an application involving the use of communications relay satellites. Many 100‐job instances that were believed to be beyond the capability of exact methods, were solved within minutes. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2006     

联合作战规划中资源调度算法研究

以联合作战为例建立任务和作战平台资源模型,通过多维动态列表规划算法(Multidimensional Dynamic List Scheduling,MDLS)进行任务-平台分配,对平台选择提出了两种不同的优先权计算方法,通过对案例求解比较了不同方法的结果.     

基于多智能体技术的协同信息融合系统研究

为达到充分发掘信息组合潜能的目的,利用多智能体(multi-agent)技术构建了自协同信息融合系统。给出了自协同信息融合系统的体系结构,对该信息融合系统需要进行的协同内容进行了分析,总结了基于多智能体的协同信息融合的主要技术特点。该系统可以起到实现信息共享、统一协调传感器探测行为、提高信息融合系统综合效能的作用。     

传感器网络在网络中心战中的应用

介绍了传感器网络对比于分立式传感器的优势所在,指出传感器网络通过多点数据融合及任务指派的方法加强传感器网络在监视和跟踪空中目标、地面活动目标、以及特殊目标(如联合战区防空反导、目标战损估计)中的应用。提出了传感器网络应该解决的几个具体问题;包括节点选择与优化部署方面的组建原则、任务指派方面的注意事项、资源管理的目标要求、异构资源信息共享的四种解决方法、以及网络安全(关于网络本身的健壮性以及数据的安全性)等方面的问题和解决方法。     

A metaheuristic scheduling procedure for resource-constrained projects with cash flows

Resource-constrained project scheduling problems with cash flows (RCPSPCF) are complex, combinatorial optimization problems. Many heuristics have been reported in the literature that produce reasonable schedules in limited project environments. However, the lack of a heuristic that dominates under differing project conditions can lead to a suboptimal choice of an appropriate heuristic for scheduling any given project. This may result in poor schedules and monetary losses. This paper reports on the application of the tabu search metaheuristic procedure for the RCPSPCF. Strategies for neighborhood generation and candidate selection that exploit the special features of the problem are combined with a simple multiheuristic start procedure. Extensive experimentation, with multiple data sets and comparison with an upper bound, indicates a significant improvement, both in project Net Present Value (NPV) as well as the number of projects, where the metaheuristic outperforms the best known heuristics in the literature. More specifically, this procedure produces the best schedules in over 85% of the projects tested, in contrast to the best single-pass heuristics which have been shown to dominate in at most 20% of the same cases. This iterative, general purpose heuristic is able to adapt significantly better to the complex interactions of the many critical parameters of the RCPSPCF than single-pass heuristics that use more specific information about each project environment. © 1999 John Wiley & Sons, Inc. Naval Research Logistics 46: 912–927, 1999     

Minimizing makespan in a multimode multiprocessor shop scheduling problem

We study the problem of multimode scheduling tasks on dedicated processors, with the objective of minimizing the maximum completion time. Each task can be undertaken in one among a set of predefined alternative modes, where each mode specifies a required set of dedicated processors and a processing time. At any time each processor can be used by a single task at most. General precedence constraints exist among tasks, and task preemption is not allowed. The problem consists of assigning a mode and a starting time to each task, respecting processor and precedence constraints, to minimize the time required to complete all tasks. The problem is NP-hard in several particular cases. In previous works, we studied algorithms in which a solution was obtained by means of an iterative procedure that combines mode assignment and sequencing phases separately. In this paper, we present some new heuristics where the decision on the mode assignment is taken on the basis of a partial schedule. Then, for each task, the mode selection and the starting time are chosen simultaneously considering the current processor usage. Different lower bounds are derived from a mathematical formulation of the problem and from a graph representation of a particular relaxed version of the problem. Heuristic solutions and lower bounds are evaluated on randomly generated test problems. © 1999 John Wiley & Sons, Inc. Naval Research Logistics 46: 893–911, 1999     

Minimizing total completion time in two-processor task systems with prespecified processor allocations

We consider the problem of scheduling multiprocessor tasks with prespecified processor allocations to minimize the total completion time. The complexity of both preemptive and nonpreemptive cases of the two-processor problem are studied. We show that the preemptive case is solvable in O(n log n) time. In the nonpreemptive case, we prove that the problem is NP-hard in the strong sense, which answers an open question mentioned in Hoogeveen, van de Velde, and Veltman (1994). An efficient heuristic is also developed for this case. The relative error of this heuristic is at most 100%. © 1998 John Wiley & Sons, Inc. Naval Research Logistics 45: 231–242, 1998     

Scheduling deteriorating jobs to minimize makespan

We consider a single-machine problem of scheduling n independent jobs to minimize makespan, in which the processing time of job J_j grows by w_j with each time unit its start is delayed beyond a given common critical date d. This processing time is p_j if J_j starts by d. We show that this problem is NP-hard, give a pseudopolynomial algorithm that runs in time and O(nd) space, and develop a branch-and-bound algorithm that solves instances with up to 100 jobs in a reasonable amount of time. We also introduce the case of bounded deterioration, where the processing time of a job grows no further if the job starts after a common maximum deterioration date D > d. For this case, we give two pseudopolynomial time algorithms: one runs in O(n²d(D − d) time and O(nd(D − d)) space, the other runs in p_j)²) time and p_j) space. © 1998 John Wiley & Sons, Inc. Naval Research Logistics 45: 511–523, 1998