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991.
Algorithm to solve a chance‐constrained network capacity design problem with stochastic demands and finite support 下载免费PDF全文
Kathryn M. Schumacher Richard Li‐Yang Chen Amy E.M. Cohn Jeremy Castaing 《海军后勤学研究》2016,63(3):236-246
We consider the problem of determining the capacity to assign to each arc in a given network, subject to uncertainty in the supply and/or demand of each node. This design problem underlies many real‐world applications, such as the design of power transmission and telecommunications networks. We first consider the case where a set of supply/demand scenarios are provided, and we must determine the minimum‐cost set of arc capacities such that a feasible flow exists for each scenario. We briefly review existing theoretical approaches to solving this problem and explore implementation strategies to reduce run times. With this as a foundation, our primary focus is on a chance‐constrained version of the problem in which α% of the scenarios must be feasible under the chosen capacity, where α is a user‐defined parameter and the specific scenarios to be satisfied are not predetermined. We describe an algorithm which utilizes a separation routine for identifying violated cut‐sets which can solve the problem to optimality, and we present computational results. We also present a novel greedy algorithm, our primary contribution, which can be used to solve for a high quality heuristic solution. We present computational analysis to evaluate the performance of our proposed approaches. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 236–246, 2016 相似文献
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In this article, we define two different workforce leveling objectives for serial transfer lines. Each job is to be processed on each transfer station for c time periods (e.g., hours). We assume that the number of workers needed to complete each operation of a job in precisely c periods is given. Jobs transfer forward synchronously after every production cycle (i.e., c periods). We study two leveling objectives: maximin workforce size () and min range (R). Leveling objectives produce schedules where the cumulative number of workers needed in all stations of a transfer line does not experience dramatic changes from one production cycle to the next. For and a two‐station system, we develop a fast polynomial algorithm. The range problem is known to be NP‐complete. For the two‐station system, we develop a very fast optimal algorithm that uses a tight lower bound and an efficient procedure for finding complementary Hamiltonian cycles in bipartite graphs. Via a computational experiment, we demonstrate that range schedules are superior because not only do they limit the workforce fluctuations from one production cycle to the next, but they also do so with a minor increase in the total workforce size. We extend our results to the m‐station system and develop heuristic algorithms. We find that these heuristics work poorly for min range (R), which indicates that special structural properties of the m‐station problem need to be identified before we can develop efficient algorithms. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 577–590, 2016 相似文献
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基于3D-IC技术实现的3D SRAM,其电路中使用了大量的TSV。目前TSV制造工艺尚未成熟,使得TSV容易出现开路或短路故障,从而给3D SRAM的测试带来新的挑战。现有的2D BIST测试方式能够探测到3D SRAM中存在的故障,但并不能判定是TSV故障还是存储器本身故障;TSV专用测试电路虽然能够探测出TSV的故障,但需要特定的测试电路来实现,这就增加了额外的面积开销,同时加大了电路设计复杂度。基于此,本文提出了一种使用测试算法来探测TSV开路故障的方法,在不使用TSV专用测试电路且不增加额外面积开销的情况下通过BIST电路解决3D SRAM中TSV的开路故障检测问题。结果显示该TSV测试算法功能正确,能够准确探测到TSV的开路故障,并快速定位TSV的开路位置。 相似文献
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针对某种冗余机械臂逆运动学求解的问题,提出了一种基于改进量子粒子群神经网络的求解算法。以冗余机械臂末端位姿为输入,经神经网络求得其逆解;针对神经网络输出结果误差较大的问题,把神经网络求初值加入初始化的粒子群中,通过基于Metropolis准则改进量子粒子群算法,避免了量子粒子群算法的早熟现象;以关节坐标经正向运动学求得的末端位姿和期望位姿的误差为适应度函数,对机械臂关节坐标迭代寻优。仿真结果表明该方法结合了神经网络算法的快速性和改进量子粒子群算法的精确性,满足求冗余机械臂逆运动学问题的速度和精度要求。 相似文献
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针对传统修正恒模算法(MCMA)收敛速度慢、剩余码间干扰大的缺点,在双模式修正恒模算法(MCMA+DD-LMS)的基础上引入局部信息量和变步长的思想,提出了一种基于修正多模算法(MMMA)的双模式变步长修正多模算法(VS-MMMA+DD-LMS)。经过理论分析和实验仿真,证明改进后的算法较传统算法具有更好的均衡效果。 相似文献
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