模糊多分配p枢纽站中位问题探讨

文章提出了一种模糊多分配p枢纽站中位问题,其中运输成本定义为模糊变量。问题的目标函数是在给定的可信性水平下,最小化总的运输成本。对于梯形和正态运输成本,问题等价于确定的混合整数线性规划问题。文章还利用修改后的CAB基准数据对模型和求解方法进行了验证,计算结果说明了模型的有效性和求解方法的可行性。     

物流运输组合优化模型及求解算法

由于企业选择运输路线或运输工具不合理而导致物流运输成本不能最小化的问题普遍存在,而管理运筹学却能很好的解决此问题.文章通过科学的方法对问题进行具体化,再建立数学模型并求解,就能找到运输成本最小的运输组合.     

有害物品运输风险度量过程中的计算误差分析

有害物品运输与其它物品运输的区别在于运输过程中事故发生的相关风险。从运输路径选择角度看,有害物品运输问题通常归结为以运输风险和运输成本为目标的双目标问题。显然,运输风险度量便成为运输路径选择的基础,其精确与否直接影响到运输路径的选择结果。文章分析了传统风险模型在事故影响后果估计、事故概率的近似计算以及离散化处理等方面存在的计算误差,并分别对其影响路径运输风险的程度进行了评价。结果表明,在传统风险模型中,影响后果估计的误差有可能比较明显,且纠正成本较高;而后两类误差实际上很小,不会影响计算结果的应用。对于其它几种常用的风险度量模型,其计算误差的情形与传统风险模型的相关结论基本一致。     

时变条件下易逝品运输的路径选择

随着经济的发展,易逝品的运输量在不断增加.在时变条件下的运输网络中,易逝品的运输成本和耗损随着时间的变化而有所不同.文章在时变网络条件下,获得易逝品运输的成本和耗损的基础上,研究了易逝品运输的路径选择;给出了有达到时间限制的易逝品运输的路径选择模型;设计了路径选择的算法,并对算法的计算复杂性进行了讨论;最后给出了一个算例.     

基于作业整合具有提前期的运输与库存联合优化

文章基于作业整合的零售商与运输商的运输与库存联合优化问题;在库存方面,假设零售商面临正态需求,且相互独立;而在运输方面,将采用两个作业进行联合运输的策略;零售商提前期由固定的运输时间以及为了降低运输成本而选择作业整合的等待时间构成;允许零售商缺货,缺货成本与提前期长短相关;基于此建立零售商运输商的联合期望总成本模型,并求解最佳订货点及合适的提前期。     

应急定位-路径的模糊机会约束规划模型

文章针对需求量和运输费用不确定的应急定位-路径问题,以系统总成本和灾害损失成本之和最小为目标,构建了基于模糊机会约束规划的应急系统定位-路径模型,并设计了遗传算法对其进行求解。实例计算结果表明,该模型和算法可以有效地解决应急物流系统中定位-路径问题,从而为政府机构应对重大突发事件提供科学的决策参考。     

物流配送网的资源优化配置研究

基于图论中的路径优化理论提出了一种物流配送网层次表达模型,该模型以最小化运输成本为优化目的,采用不同的边和权值表示不同的配送方式,通过调整边的分布和边的权值,再利用改进的Dijkstra路由算法在动态变化的配送网络中为配送请求寻找最优运输路径。通过和其他传统配送方法的仿真比较证明层次表达模型在运输成本和配送业务的损失率上都有更好的性能。     

网络广告效果FAHP模型的Excel算法实现

文章针对评价因素多样性、层次性的特点,用基于加速遗传算法的模糊层次分析法筛选评价指标,构建了网络广告效果评价体系。利用Excel软件编程,实现对用层次分析法赋权且利用模糊综合评判法建立的网络广告效果评价模型的快速、简单求解。使用该程序对具体实例进行求解,结果表明该方法具有科学、直观、快捷以及操作简便的特点,在解决各类系统综合评价问题的计算中具有一定的应用价值和参考价值。     

应用Excel巧解模糊综合评价法

在没有专业软件的情况下，求解模糊综合评价法依靠手算，要重复着繁琐的计算过程，费时费力。巧妙地应用Excel编制计算表，可以简洁明了地求解模糊综合评价法，计算结果瞬息可就。文章详细介绍了计算表的结构及公式编辑方法。这种算法不仅思路清晰，而且能够输出各个环节的计算结果，还便于计算判断矩阵的敏感度。     

基于记分函数的直觉模糊多属性群决策方法

针对属性权重未知且属性值为直觉模糊值,决策者给出方案直觉模糊值形式偏好信息的不确定多属性群决策问题,提出了一种基于模糊优选的群决策方法。首先在计算直觉模糊相似度的基础上通过非线性规划模型求解出属性权重。在明确直觉模糊多属性决策问题中直觉模糊集的定义基础上,提出了一种新的记分函数方法,进而得到各决策者决策矩阵的正、负理想方案。然后通过各决策者的模糊优选模型得到各方案的决策值,通过决策群体的模糊优选模型得到各方案的群体综合决策值。最后通过一个算例说明了方法的有效性。     

Fuzzy multi-objective optimization for optimum allocation in multivariate stratified sampling with quadratic cost and parabolic fuzzy numbers

This article deals with the uncertainties in a multivariate stratified sampling problem. The uncertain parameters of the problem, such as stratum standard deviations, measurement costs, travel costs and total budget of the survey, are considered as parabolic fuzzy numbers and the problem is formulated as a fuzzy multi-objective nonlinear programming problem with quadratic cost function. Using α-cut, parabolic fuzzy numbers are defuzzified and then the compromise allocations of the problem are obtained by fuzzy programming for a prescribed value of α. To demonstrate the utility of the proposed problem a numerical example is solved with the help of [LINGO User?s Guid. Lindo Systems Inc., 1415 North Dayton Street, Chicago,Illinois-60622, (USA), 2013] software and the derived compromise optimum allocation is compared with deterministic and proportional allocations.     

Multiobjective Stochastic Multivariate Stratified Sampling in Presence of Nonresponse

The case of nonresponse in multivariate stratified sampling survey was first introduced by Hansen and Hurwitz in 1946 considering the sampling variances and costs to be deterministic. However, in real life situations sampling variance and cost are often random (stochastic) and have probability distributions. In this article, we have formulated the multivariate stratified sampling in the presence of nonresponse with random sampling variances and costs as a multiobjective stochastic programming problem. Here, the sampling variance and costs are considered random and converted into a deterministic NLPP by using chance constraint and modified E-model. A solution procedure using three different approaches are adopted viz. goal programming, fuzzy programming, and D₁ distance method to obtain the compromise allocation for the formulated problem. An empirical study has also been provided to illustrate the computational details.     

Genetic Algorithm-Based Redundancy Optimization Problems in Fuzzy Framework

This article uses a genetic algorithm to solve the series parallel redundancy optimization problem which is in a fuzzy framework. Three nonlinear chance constrained programing models and three goal programing models are formulated based on possibility measure and credibility measure. A fuzzy simulation-based genetic algorithm is then employed to solve these kinds of fuzzy programing. Finally, numerical examples are also given.     

Compromise allocation in multivariate stratified surveys with stochastic quadratic cost function

In many real life situations the linear cost function does not approximate the actual cost incurred adequately. The cost of traveling between the units selected in the sample within a stratum is significant, instead of linear cost function. In this paper, we have considered the problem of finding a compromise allocation for a multivariate stratified sample survey with a significant travel cost within strata is formulated as a problem of non-linear stochastic programming with multiple objective functions. The compromise solutions are obtained through Chebyshev approximation technique, D ₁- distance and goal programming. A numerical example is presented to illustrate the computational details of the proposed methods.     

A Stochastic Approach to General Nonlinear Programming Problems

Nonlinear programming problem is the general case of mathematical programming problem such that both the objective and constraint functions are nonlinear and is the most difficult case of smooth optimization problem to solve. In this article, we suggest a stochastic search method to general nonlinear programming problems which is not an iterative algorithm but it is an interior point method. The proposed method finds the near-optimal solution to the problem. The results of a few numerical studies are reported. The efficiency of the new method is compared and is found to be reasonable.     

Surrogate geometric programming estimates of restricted multinomial proportions

Maximum-likelihood estimation of multinomial proportions subject to linear inequality or equality constraints is considered. A demonstration that surrogate constraints yield a zero-degree-of-difficulty geometric programming problem is given. A general solution in terms of surrogate multipliers and the unconstrained estimates is exhibited. A computational algorithm is presented and used to solve an example problem pertaining to coal deliveries received by a power plant.     

基于可信性理论的Mean-CVaR投资组合优化

选取CVaR作为风险度量指标,在可信性理论的基础上构建Mean-CVaR投资组合模型,采用Markov过程预测作为模糊变量的预期投资收益率,并设计基于模糊模拟和遗传算法的混合智能算法以求解;选取上证50成份股2013—2014年的日度历史交易数据,将该模型应用到中国证券市场,结果发现该投资组合模型与中国证券市场的环境具有一定的适应性,能够为投资者的投资决策提供依据。     

熵权模糊综合评判法在人口综合发展评价模型中的应用

统筹解决人口问题是中国实现经济发展、社会进步和可持续发展面临的重大而紧迫的战略任务。从人口综合发展指标体系出发，针对人口综合发展评价中客观存在的模糊性与随机性，将信息论汇总的熵值理论引入人口综合发展评价模型中，利用熵权法与模糊综合评判对全国及四个直辖市的人口综合发展状况进行评价检验，证明把该方法应用于人口综合发展评价模型具有较高的可信度。     

Selective Maintenance in System Reliability with Random Costs of Repairing and Replacing the Components

In this paper we consider the problem of determining the optimum number of repairable and replaceable components to maximize a system's reliability when both, the cost of repairing the components and the cost of replacement of components by new ones, are random. We formulate it as a problem of non-linear stochastic programming. The solution is obtained through Chance Constrained programming. We also consider the problem of finding the optimal maintenance cost for a given reliability requirement of the system. The solution is then obtained by using Modified E-model. A numerical example is solved for both the formulations.