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This article examines a problem faced by a firm procuring a material input or good from a set of suppliers. The cost to procure the material from any given supplier is concave in the amount ordered from the supplier, up to a supplier‐specific capacity limit. This NP‐hard problem is further complicated by the observation that capacities are often uncertain in practice, due for instance to production shortages at the suppliers, or competition from other firms. We accommodate this uncertainty in a worst‐case (robust) fashion by modeling an adversarial entity (which we call the “follower”) with a limited procurement budget. The follower reduces supplier capacity to maximize the minimum cost required for our firm to procure its required goods. To guard against uncertainty, the firm can “protect” any supplier at a cost (e.g., by signing a contract with the supplier that guarantees supply availability, or investing in machine upgrades that guarantee the supplier's ability to produce goods at a desired level), ensuring that the anticipated capacity of that supplier will indeed be available. The problem we consider is thus a three‐stage game in which the firm first chooses which suppliers' capacities to protect, the follower acts next to reduce capacity from unprotected suppliers, and the firm then satisfies its demand using the remaining capacity. We formulate a three‐stage mixed‐integer program that is well‐suited to decomposition techniques and develop an effective cutting‐plane algorithm for its solution. The corresponding algorithmic approach solves a sequence of scaled and relaxed problem instances, which enables solving problems having much larger data values when compared to standard techniques. © 2013 Wiley Periodicals, Inc. Naval Research Logistics, 2013 相似文献
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In a rendezvous search problem, two players are placed in a network and must try to meet each other in the least possible expected time. We look at rendezvous search on a discrete interval in which the players are initially placed using independent draws (usually assumed to be from the same distribution). Some optimal solutions are known if this distribution is uniform, and also for certain other special types of distribution. In this article, we present two new results. First, we characterize the complete set of solutions for the uniform case, showing that all optimal strategies must have two specific properties (namely, of being swept and strictly geodesic). Second, we relate search strategies on the interval to proper binary trees, and use this correspondence to derive a recurrence relation for solutions to the symmetric rendezvous problem for any initial distribution. This relation allows us to solve any such problem computationally by dynamic programming. Finally, some ideas for future research are discussed. © Wiley Periodicals, Inc. Naval Research Logistics 60: 454–467, 2013 相似文献
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针对现代战争条件下装备保障资源需求变化快,保障资源预测困难的问题,首先分析了影响装备保障资源需求的因素,根据实际情况选取了平均维修间隔时间(MTBM)、平均修复时间(MTBR)等8项影响装备保障资源需求的关键指标,然后将基于遗传算法(GA)优化的反向传播(BP)神经网络应用于保障资源需求预测中,构建了基于遗传神经网络的需求预测模型,最后利用1980年~2010年实际保障资源需求数据对模型进行了验证.验证结果表明,基于GA优化的BP神经网络预测模型有较快的收敛速度、较强的适应性和较高的预测精度,适用于装备保障资源需求预测. 相似文献
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科学信息的快速发展给高校心理健康教育提出了新的挑战,网络多媒体相结合案例教学的教育模式已成为高校教育模式发展的趋势。文章阐述了在《心理健康教育》教学中运用网络多媒体技术结合案例教学的心得体会,分析了网络多媒体结合案例教学的优势。 相似文献
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本文从业务量损失方面来考虑通信网链路的重要性,提出了一种用于评估通信网链路重要性的方法一业务量损失法。该方法根据不同链路故障造成业务量损失率的不同,据此判断链路的重要性。在不同的路由选择方式下,业务量损失法的评估结果也不同。本文考虑了两种路由选择方式,并举例说明了业务量损失法的运用。 相似文献
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针对多个虚拟网络同时映射时资源统一优化分配的问题,提出了一种基于多目标微粒群优化的虚拟网络映射方法(MSC-VNE),提高底层网络资源利用率及全局负载均衡性能。建立了虚拟网络映射的多目标优化模型,将单个虚拟网络映射作为一个子群,并采用多子群协作优化的方法在子群映射时通过相互信息交换进行协同进化,最终达到全局资源的优化分配。仿真结果表明,与典型成果相比,提出的方法有效地提高了底层网络资源利用率和虚拟网络构建成功率。 相似文献