Realignment in the National Football League: Did they do it right?

The National Football League (NFL) in the United States expanded to 32 teams in 2002 with the addition of a team in Houston. At that point, the league was realigned into eight divisions, each containing four teams. We describe a branch‐and‐cut algorithm for minimizing the sum of intradivisional travel distances. We consider first the case where any team can be assigned to any division. We also consider imposing restrictions, such as aligning the AFC (American Football Conference) and the NFC (National Football Conference) separately or maintaining traditional rivalries. We show that the alignment chosen by the NFL does not minimize the sum of intradivisional travel distances, but that it is close to optimal for an alignment that aligns the NFC and AFC separately and imposes some additional restrictions. © 2003 Wiley Periodicals, Inc. Naval Research Logistics 50: 683–701, 2003.     

脉冲等离子体推力器工作过程一维磁流体动力学数值模拟

通过耦合脉冲等离子体推力器(PPT)工作过程中电场、磁场、流场之间的相互作用,描述了电容器放电、推进剂烧蚀、等离子体加速流动等过程,建立了基于磁流体动力学(MHD)的一维非定常数学模型,对PPT的工作过程进行数值模拟。对数值模拟结果作了相应分析,并将部分模拟结果与实验数据进行了比较。结果表明,模型正确反映了推力器放电过程、推进剂烧蚀质量与表面温度的变化趋势。     

面向工序能力的制造装备重构

为提高制造系统的快速响应能力,分析了面向可重构的制造机床模块化设计方法,提出了面向工序能力的制造机床重构的总体思路,重点从能力相似度计算、相关性分析、缺少能力参数的模块重构、冗余能力参数模块重构、能力比较、模块替换、能力验证等方面研究了基于模块的机床重构算法。通过加工中心的配置与重构对算法进行了验证,结果表明,该算法是可行和有效的。     

时域磁场积分方程迭代求解

利用时间步进算法(MOT)求解时域电场积分方程解决导体目标瞬态散射问题时,其计算结果不稳定,会发生后期震荡现象。通过对时域磁场积分方程显式和隐式方案的分析,推导出一种隐式的迭代方案,它不但易于实现,而且数值结果表明这种时域磁场积分方程MOT迭代方案比时域电场积分方程MOT具有更好的稳定性,延缓了后期震荡效应。     

印度外空技术试验受到的压制、影响及其对策研究

印度开展外空技术试验遭受了来自国际舆论的谴责、强国遏制与外空法约束等多个方面的压力.当今世界航天大国都在竞相开展外空技术试验.当各国面临相似的问题时,应尽快开展研究,找出如何以合理的理由、灵活的手段开展试验,同时集结优势力量开展国际合作.本文深入分析主要航天国家对印度开展外空试验的主要压制手段,以此剖析印度外空技术试验...     

考虑非线性退化与随机失效阈值的剩余寿命预测

针对广泛存在的非线性退化设备,现有方法尚未考虑随机失效阈值对剩余寿命预测结果的影响。因此,通过对设备性能退化过程进行分析,提出了一种综合考虑非线性退化与随机失效阈值的剩余寿命预测方法。基于Wiener过程构建了考虑个体差异与测量误差的非线性退化模型;基于卡尔曼滤波算法建立状态空间模型以实现对退化状态的在线更新;基于极大似然法估计失效阈值分布系数估计方法,得到随机失效阈值的概率分布;基于随机失效阈值推导出剩余寿命的概率分布,实现对剩余寿命的在线预测。算例研究表明,所提方法可以有效地提升剩余寿命预测的准确性,具备一定工程应用价值。     

Optimal production ramp-up in the smartphone manufacturing industry

Motivated by challenges in the smartphone manufacturing industry, we develop a dynamic production ramp-up model that can be applied to economically satisfy nonstationary demand for short-life-cycle products by high-tech companies. Due to shorter life cycles and more rapid evolution of smartphones, production ramp-up has been increasingly critical to the success of a new smartphone. In the production ramp-up, the key challenge is to match the increasing capacity to nonstationary demand. The high-tech smartphone manufacturers are urged to jointly consider the effect of increasing capacity and decreasing demand. We study the production planning problem using a high-dimensional Markov decision process (MDP) model to characterize the production ramp-up. To address the curse of dimensionality, we refine Monte Carlo tree search (MCTS) algorithm and theoretically analyze its convergence and computational complexity. In a real case study, we find that the MDP model achieves revenue improvement by stopping producing the existing product earlier than the benchmark policy. In synthetic instances, we validate that the proposed MCTS algorithm saves computation time without loss of solution quality compared with traditional value iteration algorithm. As part of the Lenovo production solution, our MDP model enables high-tech smartphone manufacturers to better plan the production ramp-up.     

Revenue maximization in two-station tandem queueing systems

We study optimal pricing for tandem queueing systems with finite buffers. The service provider dynamically quotes prices to incoming price sensitive customers to maximize the long-run average revenue. We present a Markov decision process model for the optimization problem. For systems with two stations, general-sized buffers, and two or more prices, we describe the structure of the optimal dynamic pricing policy and develop tailored policy iteration algorithms to find an optimal pricing policy. For systems with two stations but no intermediate buffer, we characterize conditions under which quoting either a high or a low price to all customers is optimal and provide an easy-to-implement algorithm to solve the problem. Numerical experiments are conducted to compare the developed algorithms with the regular policy iteration algorithm. The work also discusses possible extensions of the obtained results to both three-station systems and two-station systems with price and congestion sensitive customers using numerical analysis.     

电磁发射系统改进FAHP-神经网络健康评估方法

准确定量评估电磁发射系统发射前的健康状态意义重大。针对模糊层次分析方法(Fuzzy Analytic Hierarchy Process, FAHP)在评估电磁发射系统串行结构同级元素健康值时存在较大偏差,且无法适用系统非线性变权重需求的缺陷,提出改进FAHP-神经网络方法。通过在计算同级元素健康指数时构造能够满足串行结构健康评估的非线性函数进行计算,并在数学上证明该方法的有效性;通过引入神经网络系统,在已有的系统先验信息和测量数据的基础上训练模型解决系统健康评估非线性变权重需求。基于电磁发射系统脉冲成形网络系统建立健康评估模型,开展评估试验。结果表明,提出的方法健康评估精度较高,在各种系统健康状态下,评估结果均符合系统实际的健康状况;对比传统的FAHP,提出的改进方法评估精度大幅提升,且在评估试验中没有出现故障误报和漏报的情况,从而验证了提出方法的可行性和工程实用价值。     

核反应堆非能动系统物理过程敏感参数识别的迭代法

核反应堆非能动系统参数敏感性分析是系统可靠性不确定性分析的重要组成部分。相关系数在刻画系统物理过程的成功准则参数敏感性方面存在缺陷,针对此问题提出迭代法,其综合了输入参数与成功准则的相关系数、偏相关系数以及输入参数之间的相关系数三方面信息,通过实例验证方法的正确性和实用性。迭代法适合分析参数对成功准则的偏相关性,能识别出更为精确的敏感参数,为进一步提高非能动系统物理过程的可靠性提供参考依据。