用线性规划方法求网络计划的最低成本日程

网络计划优化的一个重要内容是研究项目的最低成本日程。本文假定工序在可压缩范围内,直接成本呈线性变化的前提下,以线性规范为工具,提出了一种最低成本日程的优化方法。当借助计算机求解时,该方法更加简单实用。     

基于蒙特卡洛方法的网络计划资源优化

蒙特卡洛方法由计算机产生伪随机数而生成试验点,根据约束条件找到优化解,据此编制C语言程序,可以成功求解网络计划的工期固定-资源均衡优化、资源有限-工期最短优化和资源有限-工期最短-资源均衡优化,并给出蒙特卡洛方法得到最优解的概率.算例结果表明:相同条件下,基于蒙特卡洛方法的工期固定-资源均衡优化方案资源方差较粒子群算法小,基于蒙特卡洛方法的资源有限-工期最短优化方案工期较遗传算法短,基于蒙特卡洛方法的资源有限-工期最短-资源均衡优化方案工期较遗传算法短.     

压缩感知中信号重构的极大熵方法(英文)

本文针对压缩感知理论中BP算法的l1最优化问题,构造了一种新的信号重构的极大熵方法.极大熵方法克服了l1最优化问题的非光滑性,同时根据同伦方法构造极大熵函数的最优解序列来逼近全局最优稀疏解.数值实验表明极大熵方法是十分有效的信号重构方法.     

跨区域生产供给的决策模型及其求解

跨区域生产经营是现代企业集团发展的一种趋势,追求高效益低成本是生产经营者所考虑的首要问题.我们对这类问题进行了描述和定量分析,并在是否允许产品交叉生产两种情形下建立了优化数学模型,经过技术分析,分别将这两种模型转化为运输问题和最优平衡指派问题来处理,从而为这类经济决策问题提供了一种科学的决策依据和可行的决策方法.     

基于特征值分析的多尺度结构优化设计方法

基于特征值分析，提出了多尺度结构优化设计方法.该方法被用于分析宏观结构上作用有最不利荷载时，使宏观结构刚度最大的宏观拓扑结构和微观材料分布.引入约束条件为最不利荷载的Euclid范数等于1，根据Rayleigh-Ritz定理，可以将结构的柔顺度转换为一个与局部荷载向量维数相同的对称矩阵，这样就将作用有最不利荷载的柔顺度最小问题转换为求解对称矩阵的最大特征值最小问题，同时最不利荷载可以通过最大特征值矩阵的特征向量求得.最后通过算例验证所提多尺度结构优化设计方法的有效性，并说明宏观拓扑结构和微观材料分布的合理性.所提出的多尺度优化方法具有迭代稳定、收敛迅速等特点.该文拓扑优化中密度函数的更新是基于灵敏度分析和移动渐近线方法（method of moving asymptotes，MMA）.     

考虑退货情形下的闭环供应链双源库存控制策略

假设再制造闭环供应链由单个制造商和单个零售商组成,考虑制造商产品回收率、退货率和废旧产品再制造率的不确定性,建立再制造环节废旧产品的最优检测时间、购买时间和闭环供应链最低成本的数学模型.利用回收率、退货率和再制造率计算废旧产品的最优检测和购买的时间间隔,分析再制造环节中双源库存的成本最小化问题.研究表明:再制造环节可降低闭环供应链的双源库存成本,但随着回收率的增加,总成本先下降后增加;在回收率、退货率和再制造率三者共同作用下,最优检测和购买时间间隔的设置可降低双源库存成本,优化资源配置.     

试论数学课堂教学方法的优化

1 优化选择课堂教学方法是教学过程最优化的重要组成部分。选择最优的教学方法就是要最优地配合使用各种教学方法,由于不同的教学方法具有不同的作用和功能,于是现行的教学过程优化理论多半从教学方法的性能上来选择最优的教学方法。但问题在于按照一定的教育、教养和发展的任务优化得到的最有效的教学方法往往不是唯一的,因此这样并不能从根本上解决最优地配合使用教学方法的问题。因为它还必须配合使用各种最有效的教学方法.然而,     

通胀压力：纾解与应对

想必朝野对于＂通胀猛于虎＂的逻辑都有深入的共识,政府甚至将抗击通胀列入2011年工作重点日程。     

基于ISM和AHP法的物流业低成本发展影响因素研究

随着物流需求的快速增长,物流企业得到迅速发展,使行业间的竞争变得更加激烈.在世界经济一体化发展的背景下,标准化、专业化和网络化的企业物流随之出现.国民经济受物流的影响越来越大,物流成本已经成为了各个企业竞争的关键.因此,影响物流业低成本发展的因素一直受到专家和学者的重视和研究.本文通过文献研究方法选取12个影响因素,运用解释结构模型(ISM)构建影响物流业低成本发展因素的结构模型.应用层次分析法(AHP)对所建模型中因素指标的影响力进行排序,计算各因素的综合权重.从表层因素、中间因素和深层因素三个层面分析各影响因素对物流业低成本发展的影响,并提出了与物流业低成本发展相关的对策和建议,具有一定的参考价值.     

金融风险的最坏VaR方法与投资组合优化

在股价及其走势均不确定的情况下,采用最坏VaR方法,对投资的潜在损失进行最保守的度量,并得到其等价的优化形式为一个二阶锥优化问题.接着考虑相应的投资组合优化问题:如何选择合适的头寸,使得当股票组合的期望收益达到给定水平的情况下,风险最低,即最坏VaR值最小,最后对模型进行实证分析.     

Optimal expansion of capacitated transshipment networks

In this paper, we address the problem of allocating a given budget to increase the capacities of arcs in a transshipment network to minimize the cost of flow in the network. The capacity expansion costs of arcs are assumed to be piecewise linear convex functions. We use properties of the optimum solution to convert this problem into a parametric network flow problem. The concept of optimum basis structure is used which allows us to consider piecewise linear convex functions without introducing additional arcs. The resulting algorithm yields an optimum solution of the capacity expansion problem for all budget levels less than or equal to the given budget. For integer data, the algorithm performs almost all computations in integers. Detailed computational results are also presented.     

Heuristics for Multi-Stage Interdiction of Stochastic Networks

We describe and compare heuristic solution methods for a multi-stage stochastic network interdiction problem. The problem is to maximize the probability of sufficient disruption of the flow of information or goods in a network whose characteristics are not certain. In this formulation, interdiction subject to a budget constraint is followed by operation of the network, which is then followed by a second interdiction subject to a second budget constraint. Computational results demonstrate and compare the effectiveness of heuristic algorithms. This problem is interesting in that computing an objective function value requires tremendous effort. We exhibit classes of instances in our computational experiments where local search based on a transformation neighborhood is dominated by a constructive neighborhood.     

An Iterated Local Search for the Budget Constrained Generalized Maximal Covering Location Problem

Capacitated covering models aim at covering the maximum amount of customers’ demand using a set of capacitated facilities. Based on the assumptions made in such models, there is a unique scenario to open a facility in which each facility has a pre-specified capacity and an operating budget. In this paper, we propose a generalization of the maximal covering location problem, in which facilities have different scenarios for being constructed. Essentially, based on the budget invested to construct a given facility, it can provide different service levels to the surrounded customers. Having a limited budget to open the facilities, the goal is locating a subset of facilities with the optimal opening scenario, in order to maximize the total covered demand and subject to the service level constraint. Integer linear programming formulations are proposed and tested using ILOG CPLEX. An iterated local search algorithm is also developed to solve the introduced problem.     

Global search algorithms for minimum concave-cost network flow problems

We present algorithms for the single-source uncapacitated version of the minimum concave cost network flow problem. Each algorithm exploits the fact that an extreme feasible solution corresponds to a sub-tree of the original network. A global search heuristic based on random extreme feasible initial solutions and local search is developed. The algorithm is used to evaluate the complexity of the randomly generated test problems. An exact global search algorithm is developed, based on enumerative search of rooted subtrees. This exact technique is extended to bound the search based on cost properties and linear underestimation. The technique is accelerated by exploiting the network structure.     

A particle swarm optimization-based hybrid algorithm for minimum concave cost network flow problems

Traditionally, minimum cost transshipment problems have been simplified as linear cost problems, which are not practical in real applications. Some advanced local search algorithms have been developed to solve concave cost bipartite network problems. These have been found to be more effective than the traditional linear approximation methods and local search methods. Recently, a genetic algorithm and an ant colony system algorithm were employed to develop two global search algorithms for solving concave cost transshipment problems. These two global search algorithms were found to be more effective than the advanced local search algorithms for solving concave cost transshipment problems. Although the particle swarm optimization algorithm has been used to obtain good results in many applications, to the best of our knowledge, it has not yet been applied in minimum concave cost network flow problems. Thus, in this study, we employ an arc-based particle swarm optimization algorithm, coupled with some genetic algorithm and threshold accepting method techniques, as well as concave cost network heuristics, to develop a hybrid global search algorithm for efficiently solving minimum cost network flow problems with concave arc costs. The proposed algorithm is evaluated by solving several randomly generated network flow problems. The results indicate that the proposed algorithm is more effective than several other recently designed methods, such as local search algorithms, genetic algorithms and ant colony system algorithms, for solving minimum cost network flow problems with concave arc costs.     

基于NSGAII的协同产品开发项目自动协商决策

供应链环境下的协同产品开发项目中,核心企业与合作企业之间,需要对项目工期、预算和质量进行协商。科学高效的协商决策方法有助于协同产品开发项目参与各方实现双赢的协商目标。采用多Agent系统建立了协商问题的框架,构建了协商Agent以自身效用最大化和与对方建议相似度最大化为目标的多目标反建议模型;提出了基于NSGAII的自动协商决策算法,算法采用了正整数和小数混合的实数编码方式,并在遗传操作中增加了约束限制,剔除不可行个体。计算机仿真实验结果表明了算法对提高协商成功率和协商效果的有效性,说明了协商Agent可接受的最低效用值对于协商结果的影响。     

A column generation based heuristic for sensor placement,activity scheduling and data routing in wireless sensor networks

A wireless sensor network is a network consisting of distributed autonomous electronic devices called sensors. In this work, we develop a mixed-integer linear programming model to maximize the network lifetime by optimally determining locations of sensors and sinks, sensor-to-sink data flows, and activity schedules of the deployed sensors subject to coverage, flow conservation, energy consumption and budget constraints. Since solving this model is difficult except for very small instances, we propose a heuristic method which works on a reformulation of the problem. In the first phase of this heuristic, the linear programming relaxation of the reformulation is solved by column generation. The second phase consists of constructing a feasible solution for the original problem using the columns obtained in the first phase. Computational experiments conducted on a set of test instances indicate that both the accuracy and the efficiency of the proposed heuristic is quite promising.     

Linear time optimal approaches for reverse obnoxious center location problems on networks

This paper is concerned with a reverse obnoxious (undesirable) center location problem on networks in which the aim is to modify the edge lengths within an associated budget such that a predetermined facility location on the underlying network becomes as far as possible from the existing customer points under the new edge lengths. Exact combinatorial algorithms with linear time complexities are developed for the problem under the weighted rectilinear norm and the weighted Hamming distance. Furthermore, it is shown that the problem with integer decision variables can also be solved in linear time.     

Optimal intervention under stress scenarios: A case of the Korean financial system

We consider optimal intervention methods under budget constraints when financial systems face economic shocks. We propose two policies formulated by mixed-integer linear programs where regulators inject cash into institutions. One is to minimize systemic losses, and the other is to minimize the number of defaulting institutions. Using publicly available data on the Korean financial system, we construct its entire network and apply stress scenarios to the system to compare the performances of intervention strategies and derive insights on their workings.