Dynamic coordination of production planning and sales admission control in the presence of a spot market

We consider the decision‐making problem of dynamically scheduling the production of a single make‐to stock (MTS) product in connection with the product's concurrent sales in a spot market and a long‐term supply channel. The spot market is run by a business to business (B2B) online exchange, whereas the long‐term channel is established by a structured contract. The product's price in the spot market is exogenous, evolves as a continuous time Markov chain, and affects demand, which arrives sequentially as a Markov‐modulated Poisson process (MMPP). The manufacturer is obliged to fulfill demand in the long‐term channel, but is able to rein in sales in the spot market. This is a significant strategic decision for a manufacturer in entering a favorable contract. The profitability of the contract must be evaluated by optimal performance. The current problem, therefore, arises as a prerequisite to exploring contracting strategies. We reveal that the optimal strategy of coordinating production and sales is structured by the spot price dependent on the base stock and sell‐down thresholds. Moreover, we can exploit the structural properties of the optimal strategy to conceive an efficient algorithm. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

基于状态的检查与修理综合决策模型

分析了单部件系统退化过程的特点，建立了基于状态的检查与修理决策模型。该模型根据系统的当前状态来决定检查与修理，通过分析计算系统在一个更新周期内平均检查次数、预防性维修及修复性故障的概率，建立维修费用与检查问隔及预防性维修阈值的关系，以平均维修费用最小为目标，优化检查间隔及预防性维修阈值。最后运用Matlab对模型进行数值计算，结果表明，模型能有效地降低维修费用。     

Optimal control of a production‐inventory system with both backorders and lost sales

We consider the optimal control of a production inventory‐system with a single product and two customer classes where items are produced one unit at a time. Upon arrival, customer orders can be fulfilled from existing inventory, if there is any, backordered, or rejected. The two classes are differentiated by their backorder and lost sales costs. At each decision epoch, we must determine whether or not to produce an item and if so, whether to use this item to increase inventory or to reduce backlog. At each decision epoch, we must also determine whether or not to satisfy demand from a particular class (should one arise), backorder it, or reject it. In doing so, we must balance inventory holding costs against the costs of backordering and lost sales. We formulate the problem as a Markov decision process and use it to characterize the structure of the optimal policy. We show that the optimal policy can be described by three state‐dependent thresholds: a production base‐stock level and two order‐admission levels, one for each class. The production base‐stock level determines when production takes place and how to allocate items that are produced. This base‐stock level also determines when orders from the class with the lower shortage costs (Class 2) are backordered and not fulfilled from inventory. The order‐admission levels determine when orders should be rejected. We show that the threshold levels are monotonic (either nonincreasing or nondecreasing) in the backorder level of Class 2. We also characterize analytically the sensitivity of these thresholds to the various cost parameters. Using numerical results, we compare the performance of the optimal policy against several heuristics and show that those that do not allow for the possibility of both backordering and rejecting orders can perform poorly.© 2010 Wiley Periodicals, Inc. Naval Research Logistics 2010     

Minimizing the false alarm rate in systems with transient abnormality

We consider a stochastic partially observable system that can switch between a normal state and a transient abnormal state before entering a persistent abnormal state. Only the persistent abnormal state requires alarms. The transient and persistent abnormal states may be similar in appearance, which can result in excess false alarms. We propose a partially observable Markov decision process model to minimize the false alarm rate, subject to a given upper bound on the expected alarm delay time. The cost parameter is treated as the Lagrange multiplier, which can be estimated from the bound of the alarm delay. We show that the optimal policy has a control‐limit structure on the probability of persistent abnormality, and derive closed‐form bounds for the control limit and present an algorithm to specify the Lagrange multiplier. We also study a specialized model where the transient and persistent abnormal states have the same observation distribution, in which case an intuitive “watchful‐waiting” policy is optimal. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 320–334, 2016     

基于可用度的电子装备预防维修周期研究

探讨了修复型预防维修的一种仿真模型，基于可用度的定义，给出了基于最大可用度的修复型预防维修周期决策模型的仿真计算方法，结合数据采集板给出了仿真运算结果。     

基于熵权多目标决策的雷达网战损装备抢修排序方法

分析了熵权的理论基础，给出了一种基于熵权多目标决策的雷达网战损装备抢修排序方法。其中，采用熵权与决策者的主观权重相结合的方法确定综合权重，使权重的确定更加合理。最后通过实例对该方法的可行性和实用性进行了说明。     

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。     

基于不确定语言多属性决策的目标威胁度排序

针对影响不同类目标威胁度的因素建立了目标威胁度指标体系,并在此基础上,利用不确定语言多属性决策模型对不同类目标威胁度进行排序。该方法不仅充分体现了不同类目标威胁度的影响因素,还充分利用了专家和决策人员的经验,能较好地反映现代战场中目标威胁度的实际情况,利于提高智能化程度;模型适应性较强,计算量小,符合军事系统实时性的特殊要求,使评估结果更具有可信性和实用性。     

基于0-1规划的弹种决策与发射时序优化

对于身管发射武器系统,同一发射单元使用不同弹种完成作战任务时,需要确立合适的弹种决策与最佳的发射时机.提出用O-1变量来描述弹种决策变量.通过时弹种发射机制及武器系统控制特点进行分析,构造多种0-1变量用以描述武器系统作战时序之间的约束关系,建立了弹种发射时序的0-1规划模型,并用LINGO软件实现了0-1规划的求解.给出了典型应用实例,进行了仿真计算,结果表明,该方法简单可行.