Probabilistic resource pooling games

We study a setting with a single type of resource and with several players, each associated with a single resource (of this type). Unavailability of these resources comes unexpectedly and with player‐specific costs. Players can cooperate by reallocating the available resources to the ones that need the resources most and let those who suffer the least absorb all the costs. We address the cost savings allocation problem with concepts of cooperative game theory. In particular, we formulate a probabilistic resource pooling game and study them on various properties. We show that these games are not necessarily convex, do have non‐empty cores, and are totally balanced. The latter two are shown via an interesting relationship with Böhm‐Bawerk horse market games. Next, we present an intuitive class of allocation rules for which the resulting allocations are core members and study an allocation rule within this class of allocation rules with an appealing fairness property. Finally, we show that our results can be applied to a spare parts pooling situation.     

战术通信网组网方案评估优选方法

在当前信息化的战场环境下,战场环境瞬息万变,战术通信网作为信息化战争的神经中枢,起到了至关重要的作用,然而组网方案的好坏直接决定着通信网性能的优劣,因此,如何在战场环境下快速、自动、客观地评估战术通信网组网方案是一个亟待解决的问题。通过分析战术通信网的特性和要求,提出了战术通信网组网方案评估指标体系,并采用熵权灰关联的方法对组网方案进行评估优选,该方法方便快捷并且可以编程实现,不仅能够在战场环境下实时、自动地对组网方案进行评估优选,而且评估结果也符合实际情况,为指挥员提供了决策依据。     

美国联合非致命武器项目及其十年科技战略规划

美国联合非致命武器理事会在成立20周年之际发布了《联合非致命武器项目科技战略规划2016-2025》,对未来十年相关领域的科技发展做出了部署安排。本文介绍美国联合非致命武器项目的使命职能、组织机构、运行模型、代表性成果以及十年科技战略规划,旨在为技术创新和管理创新提供参考和借鉴。     

一种基于部分搜索的GNSS模糊度解算方法

为解决LAMBDA算法搜索策略效率不高的问题,提出了一种基于部分搜索的模糊度解算新方法。该方法的解算成功率接近LAMBDA算法,且只需对最后3维模糊度元素采取搜索策略。同时,通过合理设定搜索椭球的大小和搜索策略;大大提高了搜索效率。通过实验对该新算法与Bootraping算法、LAMBDA算法进行了比较分析,进一步验证了该新算法的有效性。     

基于频谱的缺陷定位技术:逆向切片优于执行切片

一般情况下,缺陷定位技术的应用前提是必须存在测试预言,以判断测试用例的运行结果是失败或者成功,否则将无法使用。然而,在许多实际情况下,测试预言不存在或者很难定义,这就是著名的"预言家难题"。为了应对"预言家难题",利用蜕变测试,扩充了采用逆向切片基于频谱的缺陷定位技术的方法学,实现了其在无测试预言下的有效应用。基于上述研究,进一步对比了SFL技术中执行切片与逆向切片的缺陷定位能力。实验表明,在有测试预言和无测试预言两种情况下,逆向切片比执行切片具有更强的缺陷定位能力,并且公式GP19和ER1’相比于其他公式,更有可能达到最优结果。     

Single batch machine scheduling with deliveries

We consider the problem of scheduling a set of n jobs on a single batch machine, where several jobs can be processed simultaneously. Each job j has a processing time p_j and a size s_j. All jobs are available for processing at time 0. The batch machine has a capacity D. Several jobs can be batched together and processed simultaneously, provided that the total size of the jobs in the batch does not exceed D. The processing time of a batch is the largest processing time among all jobs in the batch. There is a single vehicle available for delivery of the finished products to the customer, and the vehicle has capacity K. We assume that K = rD, where and r is an integer. The travel time of the vehicle is T; that is, T is the time from the manufacturer to the customer. Our goal is to find a schedule of the jobs and a delivery plan so that the service span is minimized, where the service span is the time that the last job is delivered to the customer. We show that if the jobs have identical sizes, then we can find a schedule and delivery plan in time such that the service span is minimum. If the jobs have identical processing times, then we can find a schedule and delivery plan in time such that the service span is asymptotically at most 11/9 times the optimal service span. When the jobs have arbitrary processing times and arbitrary sizes, then we can find a schedule and delivery plan in time such that the service span is asymptotically at most twice the optimal service span. We also derive upper bounds of the absolute worst‐case ratios in both cases. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 470–482, 2015     

Model parameter estimation and residual life prediction for a partially observable failing system

We consider a partially observable degrading system subject to condition monitoring and random failure. The system's condition is categorized into one of three states: a healthy state, a warning state, and a failure state. Only the failure state is observable. While the system is operational, vector data that is stochastically related to the system state is obtained through condition monitoring at regular sampling epochs. The state process evolution follows a hidden semi‐Markov model (HSMM) and Erlang distribution is used for modeling the system's sojourn time in each of its operational states. The Expectation‐maximization (EM) algorithm is applied to estimate the state and observation parameters of the HSMM. Explicit formulas for several important quantities for the system residual life estimation such as the conditional reliability function and the mean residual life are derived in terms of the posterior probability that the system is in the warning state. Numerical examples are presented to demonstrate the applicability of the estimation procedure and failure prediction method. A comparison results with hidden Markov modeling are provided to illustrate the effectiveness of the proposed model. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 190–205, 2015     

一种快速确定GPS整周模糊度的方法

GPS初始整周模糊度的求解是利用载波相位进行测量时的关键问题,采用了对系数矩阵进行QR分解的方法,用以降低矩阵的维数。模糊度搜索时,针对Z变换可能会引入多余误差,采用了对称三角分解法对协方差矩阵进行去相关处理。实验与仿真结果表明,定位误差在0.5 cm以内,方位角和仰角误差在0.1°以内。     

Single machine just‐in‐time scheduling problems with two competing agents

In scheduling problems with two competing agents, each one of the agents has his own set of jobs to be processed and his own objective function, and both share a common processor. In the single‐machine problem studied in this article, the goal is to find a joint schedule that minimizes the total deviation of the job completion times of the first agent from a common due‐date, subject to an upper bound on the maximum deviation of job completion times of the second agent. The problem is shown to be NP‐hard even for a nonrestrictive due‐date, and a pseudopolynomial dynamic program is introduced and tested numerically. For the case of a restrictive due‐date (a sufficiently small due‐date that may restrict the number of early jobs), a faster pseudopolynomial dynamic program is presented. We also study the multiagent case, which is proved to be strongly NP‐hard. A simple heuristic for this case is introduced, which is tested numerically against a lower bound, obtained by extending the dynamic programming algorithm. © 2013 Wiley Periodicals, Inc. Naval Research Logistics 61: 1–16, 2014     

Minimum violations and predictive meta‐rankings for college football

This article presents two meta‐ranking models that minimize or nearly minimize violations of past game results while predicting future game winners as well as or better than leading current systems—a combination never before offered for college football. Key to both is the development and integration of a highly predictive ensemble probability model generated from the analysis of 36 existing college football ranking systems. This ensemble model is used to determine a target ranking that is used in two versions of a hierarchical multiobjective mixed binary integer linear program (MOMBILP). When compared to 75 other systems out‐of‐sample, one MOMBILP was the leading predictive system while getting within 0.64% of the retrodictive optimum; the other MOMBILP minimized violations while achieving a prediction total that was 2.55% lower than the best mark. For bowls, prediction sums were not statistically significantly different from the leading value, while achieving optimum or near‐optimum violation counts. This performance points to these models as potential means of reconciling the contrasting perspectives of predictiveness versus the matching of past performance when it comes to ranking fairness in college football. © 2013 Wiley Periodicals, Inc. Naval Research Logistics 61: 17–33, 2014