含有模糊和随机参数的混合机会约束规划模型

提出一类混合机会约束规划模型，该模型同时含有模糊和随机参数，运用随机模拟与模糊模拟相结合的技术，给出了求解该机会约束规划模型的遗传算法，通过对生产过程最优化决策的典型问题进行分析建模和数值求解，说明了该模型和算法的合理性和有效性。     

基于粒子群算法的随机和模糊混合机会约束规划研究

研究一类混合机会约束规划模型,该模型含有随机和模糊参数。基于粒子群算法运用随机模拟和模糊模拟相结合的技术,给出了一种求解该规划模型的混合智能算法。并通过对实际模型的规划问题进行分析和数值求解,说明了该模型和算法的合理性和有效性。     

求解模糊相关机会规划模型的混合差分进化算法

针对不确定规划领域中存在的模糊相关机会规划模型,基于群体智能的差分进化算法,设计一种新的求解模糊相关机会规划模型的混合智能算法．该算法基于粒子群优化算法对差分进化算法进行改进,并运用模糊模拟技术对模糊相关机会规划模型进行分析和数值求解,无需像传统的基于遗传算法的混合智能算法需要很长时间并经过复杂的计算才能得到合理的结果．最后,通过实例表明了所提混合智能算法的合理性和有效性．     

用基于计算机随机模拟的下降法求解报童问题

采用计算机随机模拟加上传统的梯度下降法,求解了报童每天卖报的期望收益最大的订报量,并给出了迭代变化图,结果表明此算法对于报童问题是相当有效的。对于企业订货等问题,本模型及算法可作相应推广。     

模糊需求条件下车辆路径问题的模糊模拟

研究具有模糊需求的车辆路径问题,针对具有不确定需求的单车辆单车场的车辆路径问题,建立了基于模糊可信性理论的模糊机会约束规划模型,并提出了求解该问题的一种基于模糊模拟的混合遗传算法。同时,在最小化车辆总行驶距离的目标下,通过实验研究决策者主观偏好对决策目标的影响,并给出最佳主观偏好值。     

基于MATLAB随机种群模型数值模拟方法的应用研究

随机微分方程被广泛地应用在生物的各个领域。通常情况下,随机种群模型很难找到解析解,因而数值模拟是研究这类模型数值解的一个有效的工具。为此,运用MATLAB软件,对随机两种群模型和带Markovian转换的随机种群模型进行数值模拟,展示数值模拟的过程,从而给出MATLAB软件求解这类数值模拟的一般方法。     

求解模糊相关机会规划的混合智能算法

模糊相关机会规划是一类有着广泛应用背景的随机规划问题,该文采用模糊模拟产生样本训练BP网络以逼近机会函数,然后应用微粒群算法并以逼近机会函数的神经网络作为适应值估计,从而提出了一种求解模糊相关机会规划的混合智能算法。最后通过实例仿真说明了算法的正确性和有效性。     

PSO优化在模糊联合补充问题中的应用

对用PSO算法解决需求为不确定的联合补充问题进行了研究。运用模糊规划方法处理需求为模糊变量的联合补充问题,得到了作为求解目标的模糊数学模型;采用PSO思想对该模型进行分析,转化为PSO问题模型,制定出算法流程,并用数值实例验证了提出的粒子群优化模型和求解算法的有效性;对随机生成的大量数据进行处理,结果证明问题规模相同时该算法较遗传算法具有更高的效率。     

两级供应链联合成本三角模糊需求随机过程模型

根据影响两级供应链系统库存控制及决策过程的随机与模糊特性,基于联合成本提出两级供应链三角模糊需求随机过程模型。对两级供应链系统进行研究,给出其联合成本优化模型。针对交货时间和需求率的模糊特性,分情况利用三角模糊函数设计缺货数量模糊模型,基于此对两级供应链联合成本优化模型进行改进,建立三角模糊需求随机过程模型,并给出该模型的求解过程。通过模型参数敏感性实验以及算法对比实验,验证了所提模糊随机过程模型的有效性。     

基于随机模拟与PSO算法相结合的随机机会约束规划算法

随机机会约束规划作为一类重要的随机规划,广泛存在于许多领域中.为了寻找更有效的求解随机机会约束规划的算法,通过采用随机模拟来逼近随机函数,并在微粒群算法PSO(Particle Swarm Optimization)中利用随机模拟实现估计适应值和检验解的可行性,从而给出了求解随机机会约束规划的新算法,最后,测试其性能并与遗传算法进行了比较,实例结果表明该算法的正确性和有效性.     

INTERACTIVE FUZZY PROGRAMMING BASED ON FRACTILE CRITERION OPTIMIZATION MODEL FOR TWO-LEVEL STOCHASTIC LINEAR PROGRAMMING PROBLEMS

In this article, we focus on two-level linear programming problems involving random variable coefficients in objective functions and constraints. Following the concept of chance constrained programming, the two-level stochastic linear programming problems are transformed into deterministic ones based on the fractile criterion optimization model. After introducing fuzzy goals for objective functions, interactive fuzzy programming to derive a satisfactory solution for decision makers is presented as a fusion of a stochastic approach and a fuzzy one. An illustrative numerical example is provided to demonstrate the feasibility of the proposed method.     

模糊线性支持向量回归机

该文研究了当训练点的输出为三角模糊数时,支持向量回归机的构建问题。首先将模糊回归问题转化为模糊分类问题,并将求模糊最优分类超平面问题转化为求解带有模糊决策的机会约束规划问题。利用基于模糊模拟的遗传算法求解带有模糊决策的机会约束规划,得到模糊最优分类超平面(模糊方程),解模糊方程得到模糊回归函数。在此基础上,得出模糊线性支持向量回归机(算法)。从而较好地解决了支持向量机中含有模糊信息的模糊回归问题。最后,给出显示模糊线性支持向量回归机特点的模糊支持向量集的定义。     

考虑混合型号托盘的托盘共用调度随机规划模型

如何将托盘科学地在系统内调度是目前托盘共用系统管理者们亟待解决的问题。面对各类参数的随机性和多种多样的托盘,管理者很难仅凭经验做出科学的决策。利用随机机会约束规划的方法,构建了考虑混合型号托盘的托盘共用系统调度随机规划模型,使用确定性等价转化的方法将机会约束转化为了其确定等价形式,通过算例进行了数值求解和分析,验证了模型的有效性,提出了决策策略建议。     

Fuzzy Optimal Control for Bilinear Stochastic Systems with Fuzzy Parameters

In this paper we consider the control problem for a class of partially observed bilinear stochastic systems with fuzzy parameters. Using Takagi–Sugeno fuzzy model, the problem is described by three sets of fuzzy stochastic differential equations: one for the state process, one for the observed process and one for the controller which is assumed to be driven by the observed process. With this formulation, the original stochastic control problem can be treated as a deterministic identification problem in which the controller parameters and the corresponding membership functions are the unknowns. Using a suitable performance index, we have developed a set of necessary conditions for determining the parameters of the controller and the corresponding membership functions. Finally, some numerical simulations are presented to illustrate the effectiveness of the proposed fuzzy control scheme.     

Robust fuzzy control of nonlinear stochastic delay systems with impulsive effects

The problem of robust fuzzy control for a class of nonlinear fuzzy impulsive stochastic systems with time-varying delays is investigated. The nonlinear delay system is represented by the well-known T–S fuzzy model. The so-called parallel distributed compensation idea is employed to design the state feedback controller. Sufficient conditions for mean square exponential stability of the closed-loop system are derived in terms of linear matrix inequalities. Finally, a numerical example is given to illustrate the applicability of the theoretical results.     

Solving linear programming problems under fuzziness and randomness environment using attainment values

In this paper, the author presents a model to measure attainment values of fuzzy numbers/fuzzy stochastic variables. These new measures are then used to convert the fuzzy linear programming problem or the fuzzy stochastic linear programming problem into the corresponding deterministic linear programming problem. Numerical comparisons are provided to illustrate the effectiveness of the proposed method.     

Genetic algorithm-based fuzzy goal programming for class of chance-constrained programming problems

This paper presents a procedure for solving a multiobjective chance-constrained programming problem. Random variables appearing on both sides of the chance constraint are considered as discrete random variables with a known probability distribution. The literature does not contain any deterministic equivalent for solving this type of problem. Therefore, classical multiobjective programming techniques are not directly applicable. In this paper, we use a stochastic simulation technique to handle randomness in chance constraints. A fuzzy goal programming formulation is developed by using a stochastic simulation-based genetic algorithm. The most satisfactory solution is obtained from the highest membership value of each of the membership goals. Two numerical examples demonstrate the feasibility of the proposed approach.     

基于网络声誉的供应商选择模糊规划模型

根据网络声誉在供应商选择中的重要作用, 运用不确定理论, 对选择最有效的供应商组合问题进行研究。先运用模糊线性权重法确定供应商的声誉综合评价值,再结合产品成本、市场需求等内外环境因素,建立了模糊环境下的供应商组合选择模型,以确定最优的供应商组合及订货量。通过运用一种基于模糊模拟、遗传算法及神经网络的混合智能算法进行算例分析,验证了模型的可行性。