Bayesian enhanced decision making for deteriorating repairable systems with preventive maintenance

Since a system and its components usually deteriorate with age, preventive maintenance (PM) is often performed to restore or keep the function of a system in a good state. Furthermore, PM is capable of improving the health condition of the system and thus prolongs its effective age. There has been a vast amount of research to find optimal PM policies for deteriorating repairable systems. However, such decisions involve numerous uncertainties and the analyses are typically difficult to perform because of the scarcity of data. It is therefore important to make use of all information in an efficient way. In this article, a Bayesian decision model is developed to determine the optimal number of PM actions for systems which are maintained according to a periodic PM policy. A non‐homogeneous Poisson process with a power law failure intensity is used to describe the deteriorating behavior of the repairable system. It is assumed that the status of the system after a PM is somewhere between as good as new for a perfect repair and as good as old for a minimal repair, and for failures between two preventive maintenances, the system undergoes minimal repairs. Finally, a numerical example is given and the results of the proposed approach are discussed after performing sensitivity analysis. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

基于ARIS的战时修理业务流程仿真研究

利用ARIS建模方法与工具,对战时装备修理业务流程进行了仿真研究。以陆军某机步师某型装备为例,给出了战时修理业务的主流程模型以及各分支模型,通过仿真运行及结果分析,得出了制约该流程的瓶颈和需要进一步优化的对象,为装备维修保障过程仿真提供了一个很好的思路和方法。该方法对于开发战时维修保障效能仿真评估系统,实现维修保障系统的动态仿真和全要素评估具有重要的支撑作用。     

陆军武器装备需求生成程序研究

借鉴美军"联合能力集成与开发系统"的分析流程,以"面向任务、基于能力"的需求牵引观为指导,研究提出我陆军武器装备需求生成程序,确定了每一环节的具体任务和要求,对完善陆军武器装备需求生成机制和需求的统一论证具有一定的现实和理论意义。     

基于层次分析法的人为电磁环境复杂程度评估

针对战场电磁环境复杂多变,定性、定量描述难的特点,运用层次分析法(AHP)探索人为电磁环境复杂程度评估问题。建立了评估人为电磁环境的递阶层次结构,细化了评估指标,明确了评价标准,并通过对组合权重的计算,最终将对人为电磁环境复杂程度的评估转换为13个底层参量的检测数据,4个分值标准和5个评定层次。该方法对评估电磁环境的一般复杂性和特定复杂性具有一定的指导意义,较好地满足了战场电磁环境复杂程度评估的需求。     

Analysis of artillery shoot‐and‐scoot tactics

Firing multiple artillery rounds from the same location has two main benefits: a high rate of fire at the enemy and improved accuracy as the shooter's aim adjusts to previous rounds. However, firing repeatedly from the same location carries significant risk that the enemy will detect the artillery's location. Therefore, the shooter may periodically move locations to avoid counter‐battery fire. This maneuver is known as the shoot‐and‐scoot tactic. This article analyzes the shoot‐and‐scoot tactic for a time‐critical mission using Markov models. We compute optimal move policies and develop heuristics for more complex and realistic settings. Spending a reasonable amount of time firing multiple shots from the same location is often preferable to moving immediately after firing an initial salvo. Moving frequently reduces risk to the artillery, but also limits the artillery's ability to inflict damage on the enemy.     

Aggregating courier deliveries

We consider the problem of efficiently scheduling deliveries by an uncapacitated courier from a central location under online arrivals. We consider both adversary‐controlled and Poisson arrival processes. In the adversarial setting we provide a randomized (3βΔ/2δ ? 1) ‐competitive algorithm, where β is the approximation ratio of the traveling salesman problem, δ is the minimum distance between the central location and any customer, and Δ is the length of the optimal traveling salesman tour overall customer locations and the central location. We provide instances showing that this analysis is tight. We also prove a 1 + 0.271Δ/δ lower‐bound on the competitive ratio of any algorithm in this setting. In the Poisson setting, we relax our assumption of deterministic travel times by assuming that travel times are distributed with a mean equal to the excursion length. We prove that optimal policies in this setting follow a threshold structure and describe this structure. For the half‐line metric space we bound the performance of the randomized algorithm in the Poisson setting, and show through numerical experiments that the performance of the algorithm is often much better than this bound.     

Algorithms for computing waiting time distributions under different queue disciplines for the D-BMAP/PH/1

A queueing system characterized by the discrete batch Markovian arrival process (D-BMAP) and a probability of phase type distribution for the service time is one that arises frequently in the area of telecommunications. Under this arrival process and service time distribution we derive the waiting time distribution for three queue disciplines: first in first out (FIFO), last in first out (LIFO), and service in random order (SIRO). We also outline efficient algorithmic procedures for computing the waiting time distributions under each discipline. © 1997 John Wiley & Sons, Inc. Naval Research Logistics 44: 559–576, 1997     

基于分布式排队网络的防空战术协同模型

分析了多个防空火力单元的作战过程,将战术BM/C3的控制协调决策抽象为决策策略。建立了多个火力单元协同作战的排队网络模型,并运用SPN理论对排队网络的运行机制进行了强有力的描述,有效地体现了防空作战过程中的威胁评估、目标分配等战术决策过程。这将为建立大型复杂排队网络,模拟仿真战役层面防空作战过程奠定基础。     

GSPN的分析方法及其应用

随着计算机和信息技术的发展,广义随机Petri网(G SPN)作为一种图形化的建模工具,不仅可以对系统进行形式化的描述和快速原型开发,而且由于其具有坚实的数学理论基础,可以对系统进行正确性验证和性能评价,因此在系统的设计过程中,得到了广泛的应用。基于结构分析方法、可达图分析和数值分析方法讨论分析了G SPN,并给出了具体的算例,最后讨论了G SPN的应用领域。     

A model of supply-chain decisions for resource sharing with an application to ventilator allocation to combat COVID-19

We present a stochastic optimization model for allocating and sharing a critical resource in the case of a pandemic. The demand for different entities peaks at different times, and an initial inventory for a central agency are to be allocated. The entities (states) may share the critical resource with a different state under a risk-averse condition. The model is applied to study the allocation of ventilator inventory in the COVID-19 pandemic by FEMA to different U.S. states. Findings suggest that if less than 60% of the ventilator inventory is available for non-COVID-19 patients, FEMA's stockpile of 20 000 ventilators (as of March 23, 2020) would be nearly adequate to meet the projected needs in slightly above average demand scenarios. However, when more than 75% of the available ventilator inventory must be reserved for non-COVID-19 patients, various degrees of shortfall are expected. In a severe case, where the demand is concentrated in the top-most quartile of the forecast confidence interval and states are not willing to share their stockpile of ventilators, the total shortfall over the planning horizon (until May 31, 2020) is about 232 000 ventilator days, with a peak shortfall of 17 200 ventilators on April 19, 2020. Results are also reported for a worst-case where the demand is at the upper limit of the 95% confidence interval. An important finding of this study is that a central agency (FEMA) can act as a coordinator for sharing critical resources that are in short supply over time to add efficiency in the system. Moreover, through properly managing risk-aversion of different entities (states) additional efficiency can be gained. An additional implication is that ramping up production early in the planning cycle allows to reduce shortfall significantly. An optimal timing of this production ramp-up consideration can be based on a cost-benefit analysis.