基于变分不等式的多商品物流网络优化设计

为优化设计多级多商品流的物流网络,按网络状态把物流网络划分为静态网络和动态网络,分析了静态网络的基础设施建设和动态网络的物流活动问题,构建了可描述不同网络阶段的运营成本和建设成本函数,并且考虑了运营过程带来的环境污染问题,构建了治理费用函数。基于以上函数,建立以供给能力为约束条件,以总成本最小为目标的网络设计和重新设计模型,然后将模型转换为变分不等式问题,证明了所设计模型与变分不等式等价。最后通过算例,运用修正投影算法对模型进行数值演算和验证,得到了最优成本下的设施建设方案和物流组织方案。     

多级多商品流物流网络设计的优化模型与组合模拟退火算法*

以最小化系统内的建设费用、库存费用和运输费用为优化目标,建立可描述一般性带能力约束的多级多商品流物流网络设计问题的优化模型,并根据物流网络设计问题的特点,基于模拟退火算法的思想,设计组合模拟退火算法对模型进行求解。算例的计算结果表明,该优化方法运算快捷,结果正确合理,能为此类物流网络设计问题提供科学的决策依据;通过分析发现,存储费用和运输费用都会影响物流网络系统的总费用,但运输费用对系统总费用的影响程度相对要大得多。     

基于二阶段随机规划的回收物流网络优化设计研究

针对含有连续分布随机参数的回收物流网络优化设计问题,结合抽样理论,建立了由样本数量决定求解效率的二阶段随机规划模型.指出适量小样本对应的模型最优值必然是实际最优值的下界,提出了基于大样本分析的物流网络稳健性评价方法以及实际最优值上界的确定方法.给出模型求解的混合遗传算法,并总结了物流网络的优化设计步骤.通过具体算例说明了模型及其算法在设计决策中的应用.     

多状态网络可靠度的d-最小割（路）集转换算法*

为寻求计算多状态网络系统可靠度更为简明的方法,提出了一种d-最小割、路集转换算法。该算法在已知d-最小割（路）集的基础上,基于逻辑代数理论,通过展开和之积表达式获得d-最小路（割）集,再基于两者中数量较少的一个运用容斥原理,得到网络可靠度。同时,分别利用容量未取最大和不为0的边及对应取值组成的集合对表示d-最小割(路),基于集合之间的隶属关系及将集合运算中正常的先取逆再合并的运算顺序变为先合并再取逆的思想,提出相关引理,简化算法。通过复杂度分析,证明算法有效。算例证明了算法的有效性和适用性。     

物流配送网络环境中移动代理路径寻优

将蚁群算法的基本原理用到物流配送网最短路径搜索中,对降低配送成本有重要意义。针对物流配送网带约束的路由选择问题,分析了自然界中蚂蚁卓越的选路行为,提出了一种基于ant-like移动代理的路径选择算法（ORA）。该算法利用ant-like移动代理采集物流配送网络中各节点的详细信息,以网络延迟和费用为QoS参数,建立了一个满足延迟约束达到费用最小化模型;同时采用多个ant-like代理选径策略来求路径最优解,以缩短路由重构的时间,延长了网络的生存时间。分析和仿真实验结果都证明该算法是有效的。     

易腐品多周期闭环物流网络鲁棒优化模型

针对不同周期的易腐品需求与退货不确定性问题,构建了易腐品多周期闭环物流网络,并设计了对应的混合整数线性规划（MILP）模型,以实现最低系统总成本、最佳设施选址以及最优配送车辆运输路径的决策。为有效规避不确定参数的影响,采用基约束鲁棒方法,将模型中的部分清晰约束转换为鲁棒对应式。以上海市果蔬农产品企业为实例,通过遗传算法对模型进行求解。结果表明,相对单周期而言,多周期系统具有动态性、系统成本更低的优点,同时通过不确定预算参数的变化分析,验证了鲁棒模型的可行性与有效性,进而为不确定环境下构建多周期闭环物流网络及降低系统成本提供了借鉴。     

一种基于元数据的众包图片效用最优选择算法

随着互联网的高速发展,移动终端设备产生的众包图片可以用在许多重要应用场景当中以获得有效的信息。例如地震后现场区域的修复、重大事故的处理。但是,这些应用场景往往都会有资源限制的问题,如带宽、终端的存储与处理能力等等,这就限制了形成众包图片的数量。因此,如何在资源有限的情况下,从众包图片中实现目标的最佳还原是一个巨大挑战。通过采集与处理图片的地理和几何数据,形成图片的元数组,在限制计算资源的条件下,提出了一种以元数据为输入的众包图片效用最优选择算法,以实现目标的最佳还原。算法的输入是元数据而非像素,所以在资源有限的应用场景中能够高效地分析众包。采用图片的效用来衡量目标区域被覆盖的程度,并提出了图片效用计算方法。最后设计了仿真实验,实验结果验证了算法的有效性与优越性。     

基于混合概率解发掘算法的第四方物流弹复性网络设计

为了使第四方物流系统能够安全、有效地运作,研究基于弹复性的第四方物流网络设计问题。建立以网络构建成本为约束、弹复性为目标的优化模型,针对问题模型设计基于最优计算预算分配的混合概率解发掘算法。通过随机产生的不同规模问题,对混合概率解发掘算法进行性能评估,并将实验结果与传统的概率解发掘算法进行比较。实验结果表明了模型与算法的有效性。     

物流中心选址的神经网络集成模型研究

针对在建立物流中心选址模型中,单个人工神经网络模型难以确定参数、容易产生“过拟合”等问题,提出一种神经网络二次集成模型,利用Bootstrap可重复采样技术得到不同的训练集来训练产生不同的个体神经网络,采用粒子群优化算法结合个体输出获得神经网络集成,并在此基础上将集成视为个体再次结合。实验结果表明,该模型易于设计且能够提高泛化能力。     

动力线载波传感控制网络的权值分簇组网

为提高动力线载波传感控制网络拓扑结构的可靠性和稳定性,讨论并提出一种改进的基于加权的交叠分簇算法。该算法集合节点的节点度、信号衰减强度两种因素计算各个节点的权值。通过比较入簇节点的权值大小,选择退出或者加入簇来对网络进行动态分簇。实验结果表明,该算法减小了网络的路由开销,缩短了组网总时间,提高了负载均衡度,在动力线载波传感控制网络组网上具有一定的有效性。     

基于遗传神经网络的汽车回收逆向物流综合评价*

目前面向汽车回收的逆向物流工作实施效果的好坏难以衡量,针对这一问题,构建了汽车回收逆向物流综合评价指标体系。采用可以获得专家知识经验的层次分析法计算指标体系的权重作为神经网络的输入,实现了定性分析和定量分析的有效结合;提出了用遗传算法优化神经网络的遗传神经网络评价方法,结合实例验证了该评价方法更加稳定迅速并具有时效性。实验结果表明提出的评价方法可以为汽车制造企业的逆向物流管理提供决策依据。     

基于云环境下新产品开发团队优选研究

针对云环境下新产品开发团队优选问题,充分考虑团队的研发能力、协调能力和服务质量,建立了包含知识相似度评价模型、协同效应评价模型和服务质量评价模型的综合评价模型;对算法的适应度函数和搜索方式加以改进,提出了改进的人工蜂群算法,对所建立的模型进行求解,为服务需求方选择出最优团队组合;最后,以自动引导运输车（AGV）的新产品开发为例,通过模型求解和算法对比,验证了所提方法的可行性和有效性。     

A stochastic node-failure network with individual tolerable error rate at multiple sinks

Many enterprises consider several criteria during data transmission such as availability, delay, loss, and out-of-order packets from the service level agreements (SLAs) point of view. Hence internet service providers and customers are gradually focusing on tolerable error rate in transmission process. The internet service provider should provide the specific demand and keep a certain transmission error rate by their SLAs to each customer. This paper is mainly to evaluate the system reliability that the demand can be fulfilled under the tolerable error rate at all sinks by addressing a stochastic node-failure network (SNFN), in which each component (edge or node) has several capacities and a transmission error rate. An efficient algorithm is first proposed to generate all lower boundary points, the minimal capacity vectors satisfying demand and tolerable error rate for all sinks. Then the system reliability can be computed in terms of such points by applying recursive sum of disjoint products. A benchmark network and a practical network in the United States are demonstrated to illustrate the utility of the proposed algorithm. The computational complexity of the proposed algorithm is also analyzed.     

System reliability maximization for a computer network by finding the optimal two-class allocation subject to budget

In the real world, a computer/communication system is usually modeled as a capacitated-flow network since each transmission line (resp. facility) denoted by an edge (resp. node) has multiple capacities. System reliability is thus defined to be a probability that d units of data are transmitted successfully from a source node to a sink node. From the perspective of quality management, system reliability is a critical performance indicator of the computer network. This paper focuses on maximizing system reliability for the computer network by finding the optimal two-class allocation subject to a budget, in which the two-class allocation is to allocate exactly one transmission line (resp. facility) to each edge (resp. node). In addition, allocating transmission lines and facilities to the computer network involves an allocation cost where the cost for allocating a transmission line depends on its length. For solving the addressed problem, a genetic algorithm based method is proposed, in which system reliability is evaluated in terms of minimal paths and state-space decomposition. Several experimental results demonstrate that the proposed algorithm can be executed in a reasonable time and has better computational efficiency than several popular soft computing algorithms.     

基于节点重要度的复杂网络可靠性研究

为研究复杂网络的节点重要度及其可靠性,在随机网络、小世界网络和无标度网络模型下,理论分析度值中心性、半局部中心性、介数中心性和PageRank算法在节点重要度评估上的准确性,通过仿真模拟方法,对网络理论模型和实际复杂网络分别进行研究,采用逐步移除节点和重复计算的方式,考察网络最大连通子图和节点移除比例的关系,同时对数值仿真结果进行合理分析。研究结果说明基于网络全局信息的介数中心性和PageRank算法对节点重要度评估的效果更好,同时说明网络的可靠性与网络拓扑结构有关,实际复杂网络一般同时具备多种拓扑特性。     

无监督特征优选高分辨率影像变化检测新方法

通过分析面向对象高分辨率影像变化检测面临的问题,从影像对象多特征选择与利用入手来提高其变化检测的性能。提出了一种面向对象的非监督特征优选的变化检测方法,首先利用面向对象的分割方法对原始影像进行分割得到各影像对象并提取特征形成特征影像;然后按尽量消除数据冗余原则提取各单特征影像的变化检测结果;最后利用直方图相交的方法与基准影像中的变化强度信息作比较,并以此为依据进行特征优选。在此基础上利用马尔科夫随机场（Markov Random Fields,MRF）模型将优选特征的变化检测结果进行自动融合。新算法很好地实现了多特征的自动优选和综合利用,验证结果表明算法具有很好的变化检测准确性和鲁棒性性能。     

基于最优簇首数划分单元格的改进GAF算法

为构造良好的拓扑结构, 降低节点能耗, 延长网络生存期, 对传统的GAF算法进行了改进。改进算法中考虑到连通度和最优簇首数的问题, 提出了新的虚拟单元格划分方法。在簇首选择阶段, 不仅考虑到节点剩余能量及节点距基站的距离, 还考虑到节点吞吐率的问题, 并通过定义簇首选择函数来进行簇首选择。仿真结果显示, 改进的GAF算法有效地节省了节点能耗, 延长了网络生存期。该算法的各项性能均优于传统的GAF算法。     

A model of a decision support system based on case-based reasoning for third-party logistics evaluation

The social demands for third‐party logistics (3PL) are further developing the model of supply chain management. The analysis of an effective approach for 3PL service supplier evaluation is given in terms of its direct relation to the operational efficiency and benefit of the service‐demanding enterprise as well as its supply chain management. An analysis of the traditional academic theoretical results and practical methods for 3PL supplier selection indicates the deficiencies of this approach. In this paper, a case‐based reasoning model framework is postulated for a 3PL evaluation and selection system. This work further expands upon the theoretical basis of this system and its reasoning process by discussing the advantages and practical value of this framework.     

Using minimal cuts to optimize network reliability for a stochastic computer network subject to assignment budget

In our modern society, information and data are usually transmitted through a computer network. Since the computer network's reliability has a great impact on the quality of data transmission, many organizations devote to evaluating or improving network reliability, especially for network reliability optimization. This study focuses on such a confronted problem that is to find the optimal transmission line assignment to the computer network such that network reliability is maximized subject to the budget constraint. Each transmission line owns several states due to failure, maintenance, etc., and thus the computer network associated with any transmission line assignment is called a stochastic computer network. Network reliability is the probability that the computer network can transmit the specified units of data successfully. Because the discussed problem is NP-hard, an optimization algorithm that integrates the genetic algorithm, minimal cuts and Recursive Sum of Disjoint Products is proposed. Experimental results illustrate the solution procedure and show that the proposed algorithm can be executed in a reasonable time.     

A scheduling algorithm for maximum throughput based on the link condition in heterogeneous network

Nowadays, a lot of wireless interfaces can be used by mobile users to access the Internet, such as WLAN, WiMAX, WlFI and even 3G. If a mobile terminal is equipped with multiple interfaces, it can use them simultaneously to improve the performance at the hot point where different RANs （Radio Access Networks） overlap. This paper proposes a scheduling algorithm based on the link condition that ensures the whole network has the maximum throughput. Simulation is also done to show the improvement of throughput with this scheduling algorithm.