Cost allocation rules for elastic single‐attribute situations

Many cooperative games, especially ones stemming from resource pooling in queueing or inventory systems, are based on situations in which each player is associated with a single attribute (a real number representing, say, a demand) and in which the cost to optimally serve any sum of attributes is described by an elastic function (which means that the per‐demand cost is non‐increasing in the total demand served). For this class of situations, we introduce and analyze several cost allocation rules: the proportional rule, the serial cost sharing rule, the benefit‐proportional rule, and various Shapley‐esque rules. We study their appeal with regard to fairness criteria such as coalitional rationality, benefit ordering, and relaxations thereof. After showing the impossibility of combining coalitional rationality and benefit ordering, we show for each of the cost allocation rules which fairness criteria it satisfies. © 2017 Wiley Periodicals, Inc. Naval Research Logistics 64: 271–286, 2017     

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.     

航天装备大数据资源平台构建技术研究

航天装备研制涉及论证、研制、生产、试验、综合保障等各个环节,所产生的数据具有种类多、数据量大、异构性强等特点,且数据资源分散于各科研机构院所,形成一个个数据孤岛,严重制约了型号数据的统一管理和协同应用。为了建立“逻辑统一、物理分散”的航天装备统一大数据中心,本文提出一种航天装备大数据资源平台构建技术,该平台基于统一的数据编目规范,综合利用云计算、微服务、区块链等技术,可实现多维异构数据深度融合管控,实现基于型号科研生产活动下的数据逻辑统一管理和实体分布式存储,能够解决航天装备整个研制活动全要素全过程的数据采集和可控共享的现实需求,为各业务场景下的数据分析奠定基础。     

基于SDP松弛的干扰资源优化分配技术研究

提出一种基于semidefinite programming(简称SDP)松弛的干扰资源优化分配算法。在问题优化过程中首先对模型中非凸的约束条件进行松弛,变为凸约束,将原来的数学模型转化成SDP求解形式,利用内点算法对松弛后的模型求解。该算法利用解析的手段使得干扰资源优化分配问题中的NP难问题在多项式时间内得以解决,并且有较高的可靠性。仿真结果验证了算法的有效性。     

雷达组网探测资源可监控性分析

探测资源可监控性是实现雷达组网系统资源优化管理与控制的前提,与管控灵活性有着密切的关系。以雷达群组网系统为背景,综合考虑组网探测资源可监控性需求、技术可行性与经济性等因素,深入分析了雷达组网系统指控中心、组网雷达及附属设备等资源的可监视性与可控性,并提出了探测资源可监控性设计原则,这对组网管控系统及组网雷达的升级改造与研制具有一定的指导与参考价值。     

分布式海战场资源规划问题

分布式海战场资源规划是实现海上联合作战的关键。首先分析了海战场资源的分类及战场资源规划特点,建立了基于多智能体的分布式海战场资源规划系统,深入分析了该规划系统的组成和功能,并构造了相应的资源规划结构模型,为实现定性和定量相结合的海战场资源规划与管理提供了一条有效的途径。     

边消警文献信息资源保障体系建设构想

阐述了边消警文献信息资源保障体系建设的内涵和建设原则,从资源体系建设、服务体系建设、网络平台建设和保障体系建设等四方面对边消警文献信息资源保障体系建设的内容进行了探讨.     

信息化战场中基于智能体聚类的信息处理

研究了信息战条件下信息的处理问题,通过分析信息化战场条件下战场环境和人员的特点,提出了一种基于智能体聚类的信息处理方法,通过该方法来实现战场信息的通信、传输、交换从而协助指挥员决策。该方法的特点是可以根据战场的环境进行聚类,降低了信息的复杂度从而保证及时有效地传递消息。该方法通过智能体平台实现,具有一定智能性并减少了指挥员的决策难度,对指挥自动化而言是一个很好的实例。     

节点实时性能自适应的集群资源分配算法

由于配置和所运行作业的不同,集群各节点的实时性能差异较大。为提高集群性能,提出节点实时性能自适应的集群资源分配算法 (node real-time performance adaptive cluster resource scheduling algorithm,NPARSA)。节点实时性能用其配置(CPU核数及速度、内存容量、磁盘容量)和实时状态参数(CPU、内存和磁盘的剩余数量及磁盘读写速度)表示。NPARSA根据作业类型自主选择节点性能评价指标的权值,实现节点实时性能对于作业类型的自适应。实时性能最优的节点分配给作业。虚拟机实验和物理集群实验表明,与Spark默认资源分配算法、没有考虑作业类型与节点匹配的算法、使用作业和节点匹配差异程度作为资源分配依据的算法相比,NPARSA能更有效地缩短作业执行时间、提高集群性能。     

基于agent 的分布式集成环境

本文提出了一种基于多ａｇｅｎｔ的分布式集成环境ＤＩＥＭＡ的通用框架,描述了ＤＩＥＭＡ的工作原理;提出了服务ａｇｅｎｔ和请求ａｇｅｎｔ的实现框架及其设计与实现。