用于动平衡测试的MDAC窄带跟踪滤波器

为了从强干扰噪声中提取出与转速同频的振动分量,且适应转速波动时的情形,利用互相关原理导出了跟踪滤波方法,并采用乘法数模转换器(MDAC)设计并实现了窄带跟踪滤波器。实验结果表明,其增益相对误差优于0.2%,相位绝对误差优于0.4°,当转速变化率不超过9000r/min2时,滤波器的输出能够跟踪转速的变化。该滤波器能较好地满足动平衡测试的需要。     

Scheduling stochastic jobs with asymmetric earliness and tardiness penalties

We consider a stochastic counterpart of the well-known earliness-tardiness scheduling problem with a common due date, in which n stochastic jobs are to be processed on a single machine. The processing times of the jobs are independent and normally distributed random variables with known means and known variances that are proportional to the means. The due dates of the jobs are random variables following a common probability distribution. The objective is to minimize the expectation of a weighted combination of the earliness penalty, the tardiness penalty, and the flow-time penalty. One of our main results is that an optimal sequence for the problem must be V-shaped with respect to the mean processing times. Other characterizations of the optimal solution are also established. Two algorithms are proposed, which can generate optimal or near-optimal solutions in pseudopolynomial time. The proposed algorithms are also extended to problems where processing times do not satisfy the assumption in the model above, and are evaluated when processing times follow different probability distributions, including general normal (without the proportional relation between variances and means), uniform, Laplace, and exponential. © 1997 John Wiley & Sons, Inc. Naval Research Logistics 44, 531–557, 1997.     

分布式虚拟环境中环境信息动态更新研究

环境信息动态更新是分布式虚拟环境的一个主要特点,如何使环境信息更新更活跃、更反应实际情况,一直是分布式虚拟环境研究的重点。在典型的分布式交互环境中,环境信息动态更新是基于协议数据单元(PDU),它不提供数据可靠性传输保证。如何保证分布式环境中各结点环境信息一致性,本文提出了一种信息校验和协议,它通过极少的信息传输就可保证各结点环境信息高度一致。文中定义了保证分布式环境信息动态一致性更新的协议数据单元,并在试验环境中证明了协议的可靠性。     

空陆攻防博弈的动态武器目标分配

大规模作战具有高动态、非完全信息和不确定性,在分析归纳目前解决动态武器目标分配问题的一系列方法的基础上,尝试构建基于双方动态博弈的攻防对抗综合数学模型,并利用纳什均衡和帕累托最优算法进行分阶段求解。结果表明,该数学模型和博弈论方法结合能够有效解决武器目标动态分配问题。     

备件保障效能动态评估方法

针对目前效能评估方法多重视效能指标的静态观测值,对时序状态数据所蕴含的趋势信息关注较少的缺点,提出基于灰色聚类-粗糙集和集对分析的备件保障效能动态评估方法。针对主客观赋权方法各自的优缺点,引入依赖度和重要度的概念,建立灰色聚类-粗糙集组合赋权模型;将指标权重引入集对理论,提出集对同势、均势和反势的定义,描述备件保障效能的变化规律,构建基于马尔可夫链的集对分析动态模型。实例分析结果表明,该方法可以有效反映备件保障效能的动态变化特征,为决策者制定备件保障长期计划提供科学依据。     

Shortest path interdiction problem with arc improvement recourse: A multiobjective approach

We consider the shortest path interdiction problem involving two agents, a leader and a follower, playing a Stackelberg game. The leader seeks to maximize the follower's minimum costs by interdicting certain arcs, thus increasing the travel time of those arcs. The follower may improve the network after the interdiction by lowering the costs of some arcs, subject to a cardinality budget restriction on arc improvements. The leader and the follower are both aware of all problem data, with the exception that the leader is unaware of the follower's improvement budget. The effectiveness of an interdiction action is given by the length of a shortest path after arc costs are adjusted by both the interdiction and improvement. We propose a multiobjective optimization model for this problem, with each objective corresponding to a different possible improvement budget value. We provide mathematical optimization techniques to generate a complete set of strategies that are Pareto‐optimal. Additionally, for the special case of series‐parallel graphs, we provide a dynamic‐programming algorithm for generating all Pareto‐optimal solutions.     

吸气式高超声速飞行器助推分离过程数值仿真

针对吸气式高超声速冲压发动机验证性试验特殊的飞行环境和助推分离条件,以某轴对称吸气式高超声速飞行器级间分离问题为具体研究对象,采用非结构网格局部网格重构技术和非定常问题非定常六自由度问题仿真方法,对该复杂构型飞行器助推分离过程进行数值计算。研究得到弱干扰冷态分离状态下飞行器及助推器的运动参数和气动力参数在分离过程中的发展规律。对0.3 s内助推器的位移轨迹进行分析,判断分离方案的可行性,并给出最佳的分离工况条件。     

舰炮身管的模态分析与多目标优化

舰炮的炮口扰动是影响舰炮射击精度的重要因素。为了减少舰炮的炮口扰动,优化身管结构,建立了柔性身管的ABAQUS有限元模型,将有限元模型的模态计算值与模态试验值进行比较,发现建立的柔性身管有限元模型是合理可行的。提出了混沌量子粒子群算法与动力学联合优化的方法,进行身管和炮口的多目标优化。优化结果表明,优化后炮口中心的线速度、角速度和角位移与优化前相比显著减小,身管质量有所降低,结构更加合理,该优化方法有效可行,为下一步全炮总体优化设计提供了一定的借鉴。     

Stochastic production capacity: A bane or a boon for quick response supply chains?

The quick response (QR) system that can cope with demand volatility by shortening lead time has been well studied in the literature. Much of the existing literature assumes implicitly or explicitly that the manufacturers under QR can always meet the demand because the production capacity is always sufficient. However, when the order comes with a short lead time under QR, availability of the manufacturer's production capacity is not guaranteed. This motivates us to explore QR in supply chains with stochastic production capacity. Specifically, we study QR in a two-echelon supply chain with Bayesian demand information updating. We consider the situation where the manufacturer's production capacity under QR is uncertain. We first explore how stochastic production capacity affects supply chain decisions and QR implementation. We then incorporate the manufacturer's ability to expand capacity into the model. We explore how the manufacturer determines the optimal capacity expansion decision, and the value of such an ability to the supply chain and its agents. Finally, we extend the model to the two-stage two-ordering case and derive the optimal ordering policy by dynamic programming. We compare the single-ordering and two-ordering cases to generate additional managerial insights about how ordering flexibility affects QR when production capacity is stochastic. We also explore the transparent supply chain and find that our main results still hold.     

Synchronizing pricing and replenishment to serve forward-looking customers

We incorporate strategic customer waiting behavior in the classical economic order quantity (EOQ) setting. The seller determines not only the timing and quantities of the inventory replenishment, but also the selling prices over time. While similar ideas of market segmentation and intertemporal price discrimination can be carried over from the travel industries to other industries, inventory replenishment considerations common to retail outlets and supermarkets introduce additional features to the optimal pricing scheme. Specifically, our study provides concrete managerial recommendations that are against the conventional wisdom on “everyday low price” (EDLP) versus “high-low pricing” (Hi-Lo). We show that in the presence of inventory costs and strategic customers, Hi-Lo instead of EDLP is optimal when customers have homogeneous valuations. This result suggests that because of strategic customer behavior, the seller obtains a new source of flexibility—the ability to induce customers to wait—which always leads to a strictly positive increase of the seller's profit. Moreover, the optimal inventory policy may feature a dry period with zero inventory, but this period does not necessarily result in a loss of sales as customers strategically wait for the upcoming promotion. Furthermore, we derive the solution approach for the optimal policy under heterogeneous customer valuation setting. Under the optimal policy, the replenishments and price promotions are synchronized, and the seller adopts high selling prices when the inventory level is low and plans a discontinuous price discount at the replenishment point when inventory is the highest.