存在借贷资金的随机资金预算问题研究

讨论了企业运用自有资金及银行贷款进行投资时的资金预算问题,与以往的研究不同,本文假设投资支出、年投资收益以及银行贷款都为随机变量,而且,文章的研究并不要求待选的投资项目具有相同的投资期或具有相同的寿命周期.给出了随机环境下净现值收益的期望值模型及期望值目标规划模型,并设计了基于随机模拟的遗传算法,给出了模型的一般解决方法,此外,还提供了两个数值例子,用以说明建模思想,并例证算法的有效性.     

基于可信性理论的运输机会约束模型

基于可信性理论,将提出一类带有模糊参数的运输计划机会约束模型.然后,讨论可信性函数的逼近方法并且设计一个基于逼近方法、神经网络和遗传算法的启发式算法来求解这个模糊运输计划机会约束模型.最后,给出一个数值例子来表明所设计算法的实用性和有效性.     

带有模糊参数的农业生产计划模型

在现实的生产系统中, 由于材料价格, 产品价格, 市场需求以及劳动者能力等不确定因素的影响, 生产计划问题常常是一个不确定规划问题. 因此, 带有常系数的生产计划模型不能准确有效的描述生产决策环境. 基于可信性理论, 本文将提出一类新的带有模糊参数的生产计划模型. 然后, 我们讨论了可信性函数的逼近并且设计一个基于逼近方法、神经网络和遗传算法的启发式算法来求解这个模糊生产计划问题. 最后, 给出了一个数值例子来表明所设计算法的可行性和有效性.     

模糊批量生产计划问题的机会约束规划

描述了模糊单位利润、模糊生产能力以及模糊需求下的批量生产计划,并应用模糊机会约束规划规划建立了模型.当模糊变量是梯形模糊数时,我们将模糊模型转化为确定意义下的模型.为了求解优化模型,我们设计了基于模糊模拟的遗传算法.最后,通过一个数值例子说明算法的有效性.     

模糊批量生产计划问题的可信性规划模型与算法

描述模糊单位利润、模糊生产能力以及模糊需求下的批量生产计划,并应用可信性规划建立了模型.当模糊变量是梯形模糊数时,我们将模糊模型转化为确定意义下的模型.为了求解优化模型,我们设计了基于模糊模拟的遗传算法.最后,通过一个数值例子说明算法的有效性.     

模糊环境下的流动资金管理模型及其混合智能算法

基于模糊VaR方法建立一类新的流动资金管理模型,并设计含有逼近方法、神经网络和遗传算法的混合智能算法对提出的模糊流动资金管理问题进行求解。最后,给出一个模糊流动资金管理问题的数值例子表明所设计模型和算法的有效性。     

模糊环境下批量制造业提前/拖期交货的生产计划

描述了基于客户需求为模糊量的批量生产提前/拖期交货的生产计划,并建立了模糊环境下的三个模型.为了有效求解优化模型,我们将模糊模拟和遗传算法相结合给出了混合智能算法.最后通过数值例子说明算法的有效性.     

含有模糊决策的线性分布式多目标规划

针对实际问题中决策变量通常是模糊的情况,讨论具有块角结构的含有模糊决策的线性分式多目标规划模型。使用α－水平集,建立了对应的α－多目标规划模型。为解决这类问题,设计了基于模糊模拟的遗传算法。数值例子表明,遗传算法很好地解决了这个问题。     

两阶段模糊生产计划期望值模型

在现实的生产系统中,生产计划问题常常是-个确定的线性规划问题.但是,在许多的实际情况中,由于生产系统中不确定性因素的影响,带有常系数的线性规划模型不能合理地描述现实的决策环境.为了准确有效地描述生产决策环境,本文提出一类新的带有模糊参数的两阶段生产计划期望值模型并且讨论模型的一些基本性质.然后,讨论补偿函数的逼近并且设计-个基了:逼近方法、神经网络和遗传算法的启发式算法来求解这个两阶段模糊生产计划模型.最后,给出一个数值例子来表明所设计算法的可行性和有效性.     

二层随机规划基于随机模拟的遗传算法

本提出了二层随机规划模型，给出了求解二层随机规划问题的基于随机模拟的遗传算法。实际算例表明算法是可行的、有效的。     

Uncertain programming: a unifying optimization theory in various uncertain environments

By uncertain programming we mean the optimization theory in generally uncertain (random, fuzzy, fuzzy random, grey, etc.) environments. Three broad classes of uncertain programming are expected value models and chance-constrained programming as well as dependent-chance programming. In order to solve general uncertain programming models, a simulation-based genetic algorithm is also documented. Finally, some applications and further research problems appearing in this area are posed.     

含模糊变量的水污染控制系统研究

在水流量为模糊变量且河流中工业污水含量标准给定的条件下,分别建立了水污染控制系统问题的模糊期望值模型和模糊机会约束规划模型来满足不同的优化需求.为了有效求解优化模型,采用了将模糊模拟、神经元网络及遗传算法相结合的混合智能算法.最后用算例进行了验证,结果表明该算法是有效可行的.     

A bicriteria solid transportation problem with fixed charge under stochastic environment

In this paper, a bicriteria solid transportation problem with stochastic parameters is investigated. Three mathematical models are constructed for the problem, including expected value goal programming model, chance-constrained goal programming model and dependent-chance goal programming model. A hybrid algorithm is also designed based on the random simulation algorithm and tabu search algorithm to solve the models. At last, some numerical experiments are presented to show the performance of models and algorithm.     

Maximum cut in fuzzy nature: Models and algorithms

The maximum cut (Max-Cut) problem has extensive applications in various real-world fields, such as network design and statistical physics. In this paper, a more practical version, the Max-Cut problem with fuzzy coefficients, is discussed. Specifically, based on credibility theory, the Max-Cut problem with fuzzy coefficients is formulated as an expected value model, a chance-constrained programming model and a dependent-chance programming model respectively according to different decision criteria. When these fuzzy coefficients are represented by special fuzzy variables like triangular fuzzy numbers and trapezoidal fuzzy numbers, the crisp equivalents of the fuzzy Max-Cut problem can be obtained. Finally, a genetic algorithm combined with fuzzy simulation techniques is designed for the general fuzzy Max-Cut problem under these models and numerical experiment confirms the effectiveness of the designed genetic algorithm.     

Optimization models and a GA-based algorithm for stochastic time-cost trade-off problem

In real-life projects, both the trade-off between the project cost and the project completion time, and the uncertainty of the environment are considerable aspects for decision-makers. However, the research on the time-cost trade-off problem seldom concerns stochastic environments. Besides, optimizing the expected value of the objective is the exclusive decision-making criterion in the existing models for the stochastic time-cost trade-off problem. In this paper, two newly developed alternative stochastic time-cost trade-off models are proposed, in which the philosophies of chance-constrained programming and dependent-chance programming are adopted for decision-making. In addition, a hybrid intelligent algorithm integrating stochastic simulations and genetic algorithm is designed to search the quasi-optimal schedules under different decision-making criteria. The goal of the paper is to reveal how to obtain the optimal balance of the project completion time and the project cost in stochastic environments.     

Toward Fuzzy Optimization without Mathematical Ambiguity

Fuzzy programming has been discussed widely in literature and applied in such various disciplines as operations research, economic management, business administration, and engineering. The main purpose of this paper is to present a brief review on fuzzy programming models, and classify them into three broad classes: expected value model, chance-constrained programming and dependent-chance programming. In order to solve general fuzzy programming models, a hybrid intelligent algorithm is also documented. Finally, some related topics are discussed.     

模糊反向物流网络设计模型的一种改进遗传算法

反向物流是物流研究中的一个重要分支,其相关问题是目前研究的热点问题。该研究在模糊环境中根据不同的决策标准,建立了关于反向物流问题中的回收问题的三种不同类型的模型:期望值模型,机会约束模型和相关机会模型,并设计了一个模糊模拟和遗传算法相结合的混合智能算法来解决提出的模型,最后给出了一个数值例子,结果证明了将此混合智能算法用于求解模糊反向物流网络设计模型问题的有效性。     

Chance-constrained programming models for capital budgeting with NPV as fuzzy parameters

In an uncertain economic environment, experts’ knowledge about outlays and cash inflows of available projects consists of much vagueness instead of randomness. Investment outlays and annual net cash flows of a project are usually predicted by using experts’ knowledge. Fuzzy variables can overcome the difficulties in predicting these parameters. In this paper, capital budgeting problem with fuzzy investment outlays and fuzzy annual net cash flows is studied based on credibility measure. Net present value (NPV) method is employed, and two fuzzy chance-constrained programming models for capital budgeting problem are provided. A fuzzy simulation-based genetic algorithm is provided for solving the proposed model problems. Two numerical examples are also presented to illustrate the modelling idea and the effectiveness of the proposed algorithm.     

Stochastic modelling of reservoir operations

The nature of hydrologic parameters in reservoir management models is uncertain. In mathematical programming models the uncertainties are dealt with either indirectly (sensitivity analysis of a deterministic model) or directly by applying a chance-constrained type of formulation or some of the stochastic programming techniques (LP and DP based models). Various approaches are reviewed in the paper. Moran's theory of storage is an alternative stochastic modelling approach to mathematical programming techniques. The basis of the approach and its application is presented. Reliability programming is a stochastic technique based on the chance-constrained approach, where the reliabilities of the chance constraints are considered as extra decision variables in the model. The problem of random event treatment in the reservoir management model formulation using reliability programming is addressed in this paper.