集中控制混合网络中基于流的资源分配算法

集中控制混合网络中,异构化网络内部的流量具有一定的规律和特性,如果使用原有的单一的离散式最大极值和无状态的网络资源调度算法,忽略了异构网络规律,会造成网络利用率较低、易震荡、部分网络流延迟等问题。通过分析由集中控制网络和普通网络组成的混合网络的拓扑结构,对混合网络结构中常见问题如流闪现、不能估计的流、路径堵塞或连接震荡场景进行分析,并提出基于期望和状态的流量评价资源规划算法POS和POS-FME。算法考虑混合网络的运行状态,对系统可用资源进行评估,为系统中各种流匹配对应可用资源,并具有一定的预测作用,从而避免混合网络出现运行效率低下的场景。通过实验,POS算法和POS-FME算法相对传统算法,利用率提高了10%～30%,并降低了震荡和平均延迟。     

Modeling and analysis of multiobjective lot splitting for N‐product M‐machine flowshop lines

Lot splitting is a new approach for improving productivity by dividing production lots into sublots. This approach enables accelerating production flow, reducing lead‐time and increasing the utilization of organization resources. Most of the lot splitting models in the literature have addressed a single objective problem, usually the makespan or flowtime objectives. Simultaneous minimization of these two objectives has rarely been addressed in the literature despite of its high relevancy to most industrial environments. This work aims at solving a multiobjective lot splitting problem for multiple products in a flowshop environment. Tight mixed‐integer linear programming (MILP) formulations for minimizing the makespan and flowtime are presented. Then, the MinMax solution, which takes both objectives into consideration, is defined and suggested as an alternative objective. By solving the MILP model, it was found that minimizing one objective results in an average loss of about 15% in the other objective. The MinMax solution, on the other hand, results in an average loss of 4.6% from the furthest objective and 2.5% from the closest objective. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

A note on combined job selection and sequencing problems

We investigate the solvability of two single‐machine scheduling problems when the objective is to identify among all job subsets with cardinality k,1≤k≤n, the one that has the minimum objective function value. For the single‐machine minimum maximum lateness problem, we conclude that the problem is solvable in O(n²) time using the proposed REMOVE algorithm. This algorithm can also be used as an alternative to Moore's algorithm to solve the minimum number of tardy jobs problem by actually solving the hierarchical problem in which the objective is to minimize the maximum lateness subject to the minimum number of tardy jobs. We then show that the REMOVE algorithm cannot be used to solve the general case of the single‐machine total‐weighted completion time problem; we derive sufficient conditions among the job parameters so that the total weighted completion time problem becomes solvable in O(n²) time. © 2013 Wiley Periodicals, Inc. Naval Research Logistics 60: 449–453, 2013     

非周期不完全检测下的维修优化

针对gamma退化型单部件系统,基于更新和半更新过程理论建立了以期望费用率最小为目标的非周期不完全检测下的维修优化模型。利用检测规划函数使系统的检测间隔期可以依据系统的状态进行调整。为更加贴近实际,模型考虑了测量误差对维修优化的影响。通过计算可以得到最优预防性维修阈值和检测方案。给出了模型的应用算例,通过对退化速率和费用参数的灵敏度分析说明了模型的有效性。与不考虑检测误差的优化结果进行对比,证明了考虑检测误差的重要性。     

空域窗射击毁歼概率解析计算模型

为提高未来空域窗射击技术对空中机动目标的毁伤概率,需要根据目标机动幅度对未来空域窗的大小进行动态设定,故提出“自适应射击窗”的概念.空域窗大小设定的依据是使毁歼概率最优.可以采用解析法来在线计算该毁歼概率值.在分析空域窗射击误差的基础上,演绎了均匀分布法与高斯和法两种毁歼概率解析计算方法的公式.以毁歼概率蒙特卡罗法仿真数据为基准值,对两种解析法的计算精度进行了对比分析.仿真结果表明,高斯和法的计算精度显著优于均匀分布法,且满足实时计算要求,适于在线评估空域窗的射击效能.     

面向反导预警作战的雷达交接班时机选择方法

针对反导预警作战过程中远程预警相控阵雷达(early-warning radar,EWR)和多功能地基相控阵雷达(ground-based radar,GBR)探测跟踪弹道导弹的交接班问题,提出了一种可行的雷达交接班时机选择方法.该方法通过卡尔曼滤波对弹道目标进行滤波定轨,基于目标瞬时运动状态估算目标剩余飞行距离,并依据GBR对目标的探测定位精度(geometrical dilution of precision,GDOP)进行接班时机优选,以支持反导预警作战雷达交接班决策.最后,通过一个实例验证了该方法的可行性和有效性.     

多阶段任务系统任务持续能力数学评价模型研究

分析了多阶段任务系统(PMS)及任务可靠度、可信度和任务效能等任务持续能力评价参数。结合实际装备系统大部分属于可用马尔可夫过程进行描述的可修复系统的特点,为简化模型复杂程度提出了一些合理的假设条件。针对两状态PMS,通过分析其状态转移关系,从阶段任务成功概率和阶段任务间转换概率的概念出发,分析给出其计算方法。结合对可信度和任务效能等参数的分析结果,建立其计算模型,从而建立了多阶段任务系统任务持续能力数学评价模型。最后结合常见的“靶场打靶”任务,通过对比仿真结果进行了模型的实例验证。     

Interval scheduling: A survey

In interval scheduling, not only the processing times of the jobs but also their starting times are given. This article surveys the area of interval scheduling and presents proofs of results that have been known within the community for some time. We first review the complexity and approximability of different variants of interval scheduling problems. Next, we motivate the relevance of interval scheduling problems by providing an overview of applications that have appeared in literature. Finally, we focus on algorithmic results for two important variants of interval scheduling problems. In one variant we deal with nonidentical machines: instead of each machine being continuously available, there is a given interval for each machine in which it is available. In another variant, the machines are continuously available but they are ordered, and each job has a given “maximal” machine on which it can be processed. We investigate the complexity of these problems and describe algorithms for their solution. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

Integrated production scheduling and preventive maintenance planning for a single machine under a cumulative damage failure process

This paper finds the optimal integrated production schedule and preventive maintenance plan for a single machine exposed under a cumulative damage process, and investigates how the optimal preventive maintenance plan interacts with the optimal production schedule. The goal is to minimize the total tardiness. The optimal policy possesses the following properties: Under arbitrary maintenance plan when jobs have common processing time, and different due dates, the optimal production schedule is to order the jobs by earliest due date first rule; and when jobs have common due date and different processing times, the optimal production schedule is shortest processing time first. The optimal maintenance plan is of control limit type under any arbitrary production schedule when machine is exposed under a cumulative damage failure process. Numerical studies on the optimal maintenance control limit of the maintenance plan indicate that as the number of jobs to be scheduled increases, the effect of jobs due dates on the optimal maintenance control limit diminishes. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

A m‐parallel crane scheduling problem with a non‐crossing constraint

In this paper, we study a m‐parallel machine scheduling problem with a non‐crossing constraint motivated by crane scheduling in ports. We decompose the problem to allow time allocations to be determined once crane assignments are known and construct a backtracking search scheme that manipulates domain reduction and pruning strategies. Simple approximation heuristics are developed, one of which guarantees solutions to be at most two times the optimum. For large‐scale problems, a simulated annealing heuristic that uses random neighborhood generation is provided. Computational experiments are conducted to test the algorithms. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007.