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     

层次分析法在装甲师作战能力评估中的应用

装甲师作战能力的评估，对装甲部队建设有重要的推动作用，但影响装甲师作战能力的因素很多。以往沿用的人数统计评估法、指数评估法等存在一定的不确定性，因此，采用层次分析法对部队的作战能力，给出一种定量和定性相结合的评估方法。     

基于可调光纤F-P滤波器解调的分布式光纤光栅传感系统

提出并实现了一种可满足工程实用要求的波分复用光纤光栅传感网络解决方案,系统通过可调谐光纤F-P滤波器的连续扫描实现波长信号的解调.该传感系统扫描带宽50 nm,单点工作带宽5 nm,对一般应用系统每根单纤可设20-30个点,系统最高扫描频率200 Hz,分辨率5 pm(约5με或0.5℃).     

A mathematical formulation and efficient heuristics for the dynamic container relocation problem

The container relocation problem (CRP) is concerned with emptying a single yard‐bay which contains J containers each following a given pickup order so as to minimize the total number of relocations made during their retrieval process. The CRP can be modeled as a binary integer programming (IP) problem and is known to be NP‐hard. In this work, we focus on an extension of the CRP to the case where containers are both received and retrieved from a single yard‐bay, and call it the dynamic container relocation problem. The arrival (departure) sequences of containers to (from) the yard‐bay is assumed to be known a priori. A binary IP formulation is presented for the problem. Then, we propose three types of heuristic methods: index based heuristics, heuristics using the binary IP formulation, and a beam search heuristic. Computational experiments are performed on an extensive set of randomly generated test instances. Our results show that beam search heuristic is very efficient and performs better than the other heuristic methods.Copyright © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 101–118, 2014     

高倍率脉冲放电锂电池的功率输出特性

由于电池内阻和极化现象的存在,锂电池在放电的瞬间会出现较大的电压跌落,高倍率脉冲放电锂电池更是如此。为了研究高倍率脉冲放电锂电池的功率输出特性,探讨温度、荷电状态和老化等因素对电池功率性能的影响规律,定义了锂电池的功率特性曲线,搭建了高倍率电池测试平台,并从温度、荷电状态和老化3个阻抗敏感因素开展实验研究。研究方法和结论对后期开展锂电池系统的峰值功率评估和功率曲线预测有一定的指导意义。     

ABC分析法在不平衡报价评审中的应用

针对不平衡报价在施工招标中呈愈演愈烈之势,严重损害招标人利益,在分析不平衡报价含义与表现形式的基础上,利用ABC分析法基本原理与思想,以某建设工程为例,探析ABC分析法在不平衡报价评审中的应用和实施,并运用ABC分析法对工程实践数据进行了筛选分析,筛选结果与工程实践相符.研究结果为解决工程建设评标中定量评审不平衡报价方...     

基于OFD-LFM信号的MIMO雷达微多普勒效应分析

多输入多输出(MIMO)雷达系统利用空间分集技术,能够在不同视角接收目标回波信号,与传统单基雷达系统相比,可以提取更丰富的目标特征信息,因而大大提高了雷达的目标运动参数估计精度。基于MIMO雷达系统,详细推导了正交频分线性调频信号体制下目标部件旋转引入的微多普勒效应,通过取多组观测数据的数学期望对旋转半径进行了参数估计,最后的仿真实验验证了理论分析的结果,得到的旋转半径估值与真实值非常接近。     

使用三个数域的数域筛算法

大整数分解难题是RSA密码的数学安全基础.目前数域筛算法是分解365比特以上大整数的最有效方法,然而它的时间复杂度仍然是亚指数的.对于目前普遍使用的1024比特以上大整数,数域筛算法还不能分解,所以研究数域筛算法具有重要的意义.现有的一般数域筛算法普遍使用两个数域,对多个数域的研究极少.一般数域筛算法经过修改可以使用三个数域,即两个代数数域和一个有理数域.分析表明:修改后的数域筛算法与原来的一般数域筛算法在时间复杂度上处于同一量级.但修改后的数域筛算法有更多地方可以合并计算,所以计算速度更快了.通过两个实验也验证了这一结论.     

OFD-LFM MIMO雷达中目标部件振动的微多普勒效应分析

与传统单基雷达系统相比,多输入多输出(MIMO)雷达系统充分利用发射分集和接收分集的优势,能够在不同的视角接收目标回波信号,可以提取目标更精细的微多普勒特征,因而大大提高了雷达对目标的识别能力。基于MIMO雷达系统,详细分析推导了正交频分线性调频信号体制下目标部件振动的微多普勒效应,并采用时频分析工具对理论分析的结果进行了仿真验证。     

基于分合粒子群算法的多无人机任务重分配

多UAV在执行任务过程中,战场环境以及UAV编队状态的改变将导致原有的分配计划失效或效率降低,因此有必要重新分配任务。针对多无人机任务重分配问题,首先建立了相应的数学模型。其次运用分组基础上的任务重分配策略进行任务分配,提出了改进的K均值聚类算法进行初步分组,再在分组的基础下,提出了分合粒子群优化算法进行组内任务分配。最后进行实验仿真。实验结果与分析表明基于分合粒子群算法的任务重分配方法能有效地满足多变的战场环境要求。