Bayesian selling problem with partial information

We introduce an optimal stopping problem for selling an asset when the fixed but unknown distribution of successive offers is from one of n possible distributions. The initial probabilities as to which is the true distribution are given and updated in a Bayesian manner as the successive offers are observed. After receiving an offer, the seller has to decide whether to accept the offer or continue to observe the next offer. Each time an offer is observed a fixed cost is incurred. We consider both the cases where recalling a past offer is allowed and where it is not allowed. For each case, a dynamic programming model and some heuristic policies are presented. Using simulation, the performances of the heuristic methods are evaluated and upper bounds on the optimal expected return are obtained. © 2013 Wiley Periodicals, Inc. Naval Research Logistics, 2013     

Does Military Spending Nonlinearly Affect Economic Growth in South Africa?

Using annual data collected from 1988 to 2015, this study provides evidence of a non-linear relationship between military spending, economic growth and other growth determinants for the South African economy. The empirical study is based on estimates of a logistic smooth transition regression (LSTR) model and our empirical results point to an inverted U-shaped relationship between military spending and economic growth for the data. Furthermore, our empirical results suggest that the current levels of military spending, as a component of total government expenditure, are too high in the South African economy and need to be transferred towards more productive non-military expenditure in order to improve the performance of economic growth and other growth determinants.     

基于能量均衡的无线传感器网络生命周期延长策略

为了从能量均衡角度出发解决无线传感器网络生命周期延长问题,从而延长整个物联网生命周期,针对无线传感器网络节点遭遇"路由空洞"的时候出现路由中断和生命周期缩短现象,分析能量消耗原理,构建最优跳数模型和能量均衡协议,并提出采用能量均衡的思路减少节点能量消耗。实验仿真发现,应用能量均衡协议使无线传感器网络节点能量分布更加均匀,节点能量消耗时间明显变长,而个别节点能量消耗速度明显大于其他节点的情况得以减少,达到延长无线传感器网络生命周期的目的。仿真结果验证了该方法的可行性。     

UUV编队协同搜索静止目标的准最优方法

考虑到操作的简单性且实际执行搜索任务时搜索力不是无限可分,将连续空间的搜索问题转换为离散空间的最优搜索问题。通过划分网格,将连续的目标位置分布离散化。根据最优搜索理论,提出了单个无人水下航行器的准最优随机搜索方法,较好地逼近理论最优值。并以此为基础,分析了无人水下航行器编队的3种协同搜索方法:集中最优搜索、分散最大概率搜索和准最优搜索。最后通过实例仿真,得出了编队准最优搜索策略的有效性、优越性和可操作性。此方法将对无人水下航行器编队的战法研究具有参考借鉴意义。     

Optimal inventory control policy for periodic‐review inventory systems with inventory‐level‐dependent demand

We consider a setting in which inventory plays both promotional and service roles; that is, higher inventories not only improve service levels but also stimulate demand by serving as a promotional tool (e.g., as the result of advertising effect by the enhanced product visibility). Specifically, we study the periodic‐review inventory systems in which the demand in each period is uncertain but increases with the inventory level. We investigate the multiperiod model with normal and expediting orders in each period, that is, any shortage will be met through emergency replenishment. Such a model takes the lost sales model as a special case. For the cases without and with fixed order costs, the optimal inventory replenishment policy is shown to be of the base‐stock type and of the (s,S) type, respectively. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012     

改进的二进制粒子群算法的传感器优化配置

传感器组合优化问题是设备状态检测系统设计的重要问题.在研究传感器与故障有向图的基础上,提出了传感器优化配置模型;根据传感器组合优化自身特点,从位改变率、惯性权重两个方面对BPSO算法进行了参数分析.结合免疫算法的“亲和度”思想以及非线性惯性权重递减公式,提出了改进的二进制粒子群算法.案例显示,改进的二进制PSO算法提高了算法在整个解空间的搜索能力,加快了收敛速度,能够很好地用于解决传感器优化问题.     

导弹对抗离散数学模型

针对现代非接触战争的特点,用数学方法研究导弹对抗模型问题,针对作战的不同目的和要求,建立两个导弹对抗的非线性规划数学模型,对模型求解设计了近似方法,该方法把上述非线性规划模型化为多个线性规划模型进行求解.对一类较简单的特殊情况进行了仔细分析,得到了分析解.最后指出该方法如何应用于动态实时优化决策.本文的方法可用来预测和评估导弹对抗的效果,为作战实时优化决策提供参考.     

Optimal pricing and inventory control policy in periodic‐review systems with fixed ordering cost and lost sales

This paper studies a periodic‐review pricing and inventory control problem for a retailer, which faces stochastic price‐sensitive demand, under quite general modeling assumptions. Any unsatisfied demand is lost, and any leftover inventory at the end of the finite selling horizon has a salvage value. The cost component for the retailer includes holding, shortage, and both variable and fixed ordering costs. The retailer's objective is to maximize its discounted expected profit over the selling horizon by dynamically deciding on the optimal pricing and replenishment policy for each period. We show that, under a mild assumption on the additive demand function, at the beginning of each period an (s,S) policy is optimal for replenishment, and the value of the optimal price depends on the inventory level after the replenishment decision has been done. Our numerical study also suggests that for a sufficiently long selling horizon, the optimal policy is almost stationary. Furthermore, the fixed ordering cost (K) plays a significant role in our modeling framework. Specifically, any increase in K results in lower s and higher S. On the other hand, the profit impact of dynamically changing the retail price, contrasted with a single fixed price throughout the selling horizon, also increases with K. We demonstrate that using the optimal policy values from a model with backordering of unmet demands as approximations in our model might result in significant profit penalty. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2006     

Doubly Stochastic Sequential Assignment Problem

This article introduces the Doubly Stochastic Sequential Assignment Problem (DSSAP), an extension of the Sequential Stochastic Assignment Problem (SSAP), where sequentially arriving tasks are assigned to workers with random success rates. A given number of tasks arrive sequentially, each with a random value coming from a known distribution. On a task arrival, it must be assigned to one of the available workers, each with a random success rate coming from a known distribution. Optimal assignment policies are proposed for DSSAP under various assumptions on the random success rates. The optimal assignment algorithm for the general case of DSSAP, where workers have distinct success rate distribution, has an exponential running time. An approximation algorithm that achieves a fraction of the maximum total expected reward in a polynomial time is proposed. The results are illustrated by several numerical experiments. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 124–137, 2016     

Capacity expansion and cost efficiency improvement in the warehouse problem

The warehouse problem with deterministic production cost, selling prices, and demand was introduced in the 1950s and there is a renewed interest recently due to its applications in energy storage and arbitrage. In this paper, we consider two extensions of the warehouse problem and develop efficient computational algorithms for finding their optimal solutions. First, we consider a model where the firm can invest in capacity expansion projects for the warehouse while simultaneously making production and sales decisions in each period. We show that this problem can be solved with a computational complexity that is linear in the product of the length of the planning horizon and the number of capacity expansion projects. We then consider a problem in which the firm can invest to improve production cost efficiency while simultaneously making production and sales decisions in each period. The resulting optimization problem is non‐convex with integer decision variables. We show that, under some mild conditions on the cost data, the problem can be solved in linear computational time. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 367–373, 2016