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.     

Information theory for ranking and selection

We study the classical ranking and selection problem, where the ultimate goal is to find the unknown best alternative in terms of the probability of correct selection or expected opportunity cost. However, this paper adopts an alternative sampling approach to achieve this goal, where sampling decisions are made with the objective of maximizing information about the unknown best alternative, or equivalently, minimizing its Shannon entropy. This adaptive learning is formulated via a Bayesian stochastic dynamic programming problem, by which several properties of the learning problem are presented, including the monotonicity of the optimal value function in an information-seeking setting. Since the state space of the stochastic dynamic program is unbounded in the Gaussian setting, a one-step look-ahead approach is used to develop a policy. The proposed policy seeks to maximize the one-step information gain about the unknown best alternative, and therefore, it is called information gradient (IG). It is also proved that the IG policy is consistent, that is, as the sampling budget grows to infinity, the IG policy finds the true best alternative almost surely. Later, a computationally efficient estimate of the proposed policy, called approximated information gradient (AIG), is introduced and in the numerical experiments its performance is tested against recent benchmarks alongside several sensitivity analyses. Results show that AIG performs competitively against other algorithms from the literature.     

陆军无人装备作战指挥信息系统

本文对陆战场无人化、智能化作战需求及其对指挥信息系统的能力要求进行分析。以美军为典型样例对无人装备的指挥信息系统发展现状进行分析,对无人装备作战指挥信息系统体系架构进行构想,并提出未来需重点突破的关键技术,最后对军队无人装备指挥信息体系的发展提出了几点建议。     

【将军论坛】重要经济目标与矢量防护工程

重要经济目标是信息化战争首选的打击对象,这类目标绝大多数位于地面以上,特点突出、规模庞大、难以保密、易损性强,一旦遭受破坏后果将损失惨重,而依靠建造硬壳抵御弹药毁伤的传统防护工程对其难有大的作为。本文从新形势下重要经济目标防护陷于困局的实际出发,系统分析和梳理了传统防护工程和防护技术的特点及其应用的固有薄弱特征,提出矢量防护的创造性解决方案,为重要经济设施和预警设施、新型作战装备等位于地面的易损性目标提供了理论基础。矢量防护工程通过改变弹药的运动方向,避免弹药与目标交汇,将防护工程由“拳击式”硬顶变为“太极式”破解,为未来我国重要经济目标提供了新质防护技术。     

Arbitrary-oriented target detection in large scene sar images

Target detection in the field of synthetic aperture radar (SAR) has attracted considerable attention of researchers in national defense technology worldwide, owing to its unique advantages like high resolution and large scene image acquisition capabilities of SAR. However, due to strong speckle noise and low signal-to-noise ratio, it is difficult to extract representative features of target from SAR images, which greatly inhibits the effectiveness of traditional methods. In order to address the above problems, a framework called contextual rotation region-based convolutional neural network (RCNN) with multilayer fusion is proposed in this paper. Specifically, aimed to enable RCNN to perform target detection in large scene SAR images efficiently, maximum sliding strategy is applied to crop the large scene image into a series of sub-images before RCNN. Instead of using the highest-layer output for proposal generation and target detection, fusion feature maps with high resolution and rich semantic information are constructed by multilayer fusion strategy. Then, we put forwards rotation anchors to predict the minimum circumscribed rectangle of targets to reduce redundant detection region. Furthermore, shadow areas serve as contextual features to provide extraneous information for the detector identify and locate targets accurately. Experimental results on the simulated large scene SAR image dataset show that the proposed method achieves a satisfactory performance in large scene SAR target detection.     

Impacts of sequential acquisition,market competition mode,and confidentiality on information flow

We investigate information flow in a setting in which 2 retailers order from a supplier and sell to a market with uncertain demand. Each retailer has access to a signal. The retailers can disclose signals to each other (horizontal information sharing), while the supplier can solicit signals by offering retailers differential payments as incentives for signal disclosure (vertical information acquisition). In the base setting, market competition is in quantity, and a retailer can fully infer the signal that the other retailer discloses to the supplier. We show that the supplier prefers to sequentialize the procedure for information acquisition. Moreover, vertical information acquisition by the supplier is a strategic complement to horizontal information sharing between the retailers to establish information flow. In the equilibrium, the retailers have no incentive to exchange signals, but system wide information transparency can be realized through a combination of information acquisition and inference. We further study the signaling effect, whereby the supplier utilizes wholesale pricing as an instrument to affect the retailers' inference of the shared signals, and price competition to explore their impacts on the supplier's preference for sequential acquisition and the sustainability of information flow.     

基于军民融合的军事信息资源效能转化

军事信息资源效能转化的强弱,事关军队信息化建设和军队战斗力生成,需要引起高度重视。本文阐述军民融合对军事信息效能转化工作的巨大推动力,并通过分析当前制约军民融合军事信息资源效能转化的内因和外因,提出完善军民融合军事信息资源效能转化工作的措施建议。     

用不确定性度量作战效能的评估方法

把作战过程分解成多个节点,用不确定性自信息量度量各节点完成任务的程度,并改造了信息熵函数,得出一种适合求解作战效能的评估方法.最后,针对一实例进行效能评估建模、验模和灵敏度分析.     

军事电子信息装备发展与思考

主要叙述了美、英、法进行未来陆军转型的简要过程与概况,以及他们的发展方针与模式,简述了中国军队的军事改革情况,从而提出了我国军事电子信息化发展的顶层设计、转型路线、军事电子信息装备的综合集成等相关方面的分析思考和应做的努力,希望通过抛砖引玉的方式,引起和获得同仁们的帮助以及对火控、指控装备发展的重视.     

基于自校正卡尔曼滤波器的信息融合算法

设计了一种新的基于ARMA模型自校正卡尔曼滤波器及其信息融合的方法,从而避免了经典卡尔曼滤波器需要精确知道系统的模型参数和噪声统计特性的缺点.仿真结果表明,在未知部分模型参数和噪声统计特性的情况下,自校正卡尔曼滤波器的滤波性能非常接近稳态最优卡尔曼滤波器;基于自校正卡尔曼滤波器的信息融合滤波性能接近基于稳态最优卡尔曼滤波器的信息融合滤波性能.