考虑混合信息表征和属性交互的目标导向新产品开发方案选择方法

将目标值融入到新产品开发方案选择中,考虑方案属性值达成目标值的情况,有助于企业选择更具竞争力的产品开发方案。针对属性值和目标值的混合信息表征以及属性交互的问题,提出基于目标导向决策分析和k-可加模糊测度的新产品开发方案选择方法。首先,考虑目标值和属性值表示为区间值、模糊数、语言值等混合信息的情形,将其转化为概率密度;结合属性的三种偏好,利用目标导向决策分析计算属性值达成目标值的概率。其次,基于属性交互方向和强度等信息,利用最小方差法识别k-可加模糊测度,进而利用Choquet积分算子集结各属性的目标达成概率作为产品开发方案选择的依据。最后,将方法应用于大型集成电路测试仪的开发方案选择,验证了有效性。     

基于多目标优化的对抗反辐射导弹诱饵部署

针对目前研究部署诱饵保护地基雷达免遭反辐射导弹攻击未考虑多个目标的情况,提出了基于多目标优化的诱饵最优位置模型.首先对影响部署诱饵的各因素进行分析,并用随机仿真模型进行建模,目标是同时最大化反辐射导弹爆炸点与地基雷达以及诱饵之间距离;然后采用概率多目标模拟退火算法获得诱饵位置组合的非支配组合;根据决策者的不完全偏好信息,采用多属性效用函数,获得偏好非支配组合.最后对模型进行仿真,仿真结果表明模型的有效性.     

概率约束随机规划的一种近似方法及其它的有效解模式

根据最小风险的投资最优问题，我们给出了一个统一的概率约束随机规划模型。随后我们提出了求解这类概率约束随机规划的一种近似算法，并在一定的条件下证明了算法的收敛性。此外，提出了这种具有概率约束多目标随机规划问题的一种有效解模型。     

时滞随机切换脉冲混杂系统的随机积分输入状态稳定性

考虑了时滞随机切换脉冲混杂非线性系统的随机积分输入状态稳定性问题.首先,导出估计给定随机过程上界的一个充分条件.基于此,得到随机脉冲非线性系统依概率全局渐近稳定和随机积分输入状态稳定的一系列条件.进一步,利用多Lyapunov-Krasovskii泛函方法和驻留时间技术得到随机切换脉冲混杂非线性系统随机积分输入状态稳定的判据.最后,仿真例子证实了所得结论的有效性.     

用改进的遗传算法求解危险货物零担运输多目标配装问题

危险货物零担运输配装问题是铁路部门复杂而急需解决的实际问题.给出了多目标的数学规划模型,先对待装货物进行预处理,然后运用自适应的遗传算法对问题进行了求解,该算法中自适应杂交变异概率的应用提高了收敛速度.最后通过实例证明了该方法的可行性和有效性.     

多目标随机线性规划问题的模糊求解方法

研究了资源量b_i为随机变量的多目标随机线性规划问题,建立了相应等价的确定性多目标规划模型,提出了有效的模糊求解方法,并用实例作了有效性说明。     

基于模糊系统的改进型NSGA-III算法

在利用多目标进化算法解决高维多目标优化问题时，随着目标函数个数的增加，非支配解的个数呈指数增长，使得在环境选择阶段缺少足够的选择压力，进而影响算法性能。基于分解的NSGA-III算法是一种能够有效解决上述问题的多目标进化算法，但在该算法中采用固定的交叉概率和变异概率生成新的解，使得算法在处理一些复杂的高维多目标问题时表现较差。因此，本文提出一种基于模糊系统的改进型NSGA-III算法，该算法利用模糊系统动态调整子代生成过程中算子的交叉概率与变异概率。对于模糊系统的设计，采用与算法密切相关的Spread值和迭代次数作为输入，利用模糊逻辑推理后输出交叉概率与变异概率。将所提算法与其他基于分解技术的算法在20个高维多目标优化问题上进行实验对比，结果表明本文算法可以有效提高收敛速度，且能很好地保持种群的多样性和收敛性。     

求解随机凸规划概率约束问题的对偶算法

1引言随机规划中的概率约束问题在工程和管理中有广泛的应用.因为问题中包含非线性的概率约束,它们的求解非常困难.如果目标函数是线性的,问题的求解就比较容易.给出了一个求解随机线性规划概率约束问题的综述.原-对偶算法和切平面算法是比较有效的.在本文中,我们讨论随机凸规划概率约束问题:     

随机模拟算法在体育教学评价中应用

针对传统体育教学评价中优劣判别的绝对性,以及多评价结论非一致性问题,构建一种凸显自身优势的自主优势评价方法,评价中运用一种基于概率型随机模拟算法,通过计算各评价对象之间的优胜度,来评判评价对象的优势.经过算例验证方法的有效性,得到带有概率信息的评价结论.     

服务大规模定制模式下的供应链调度优化

为了提升服务大规模定制(SMC)模式下供应链系统的运作柔性,应对客户较强的多样化需求特征,本文在对服务定制特征分析、服务阶段界定以及服务规模效应探讨的基础上,指出SCM模式下的供应链调度问题是一个典型的随机需求与随机资源约束的多目标动态优化问题。研究了SMC模式下供应链调度的优化目标与约束条件,建立了完整的随机多目标动态调度优化数学模型。基于SMC运作的特点,运用改进的蚁群算法对调度问题进行了求解。最后,通过实例分析了模型及算法的可行性、有效性及适用性。     

Interactive multiobjective fuzzy random linear programming: Maximization of possibility and probability

This paper considers multiobjective linear programming problems with fuzzy random variables coefficients. A new decision making model is proposed to maximize both possibility and probability, which is based on possibilistic programming and stochastic programming. An interactive algorithm is constructed to obtain a satisficing solution satisfying at least weak Pareto optimality.     

ISTMO: An interval reference point-based method for stochastic multiobjective programming problems

In this paper, we present an interactive algorithm (ISTMO) for stochastic multiobjective problems with continuous random variables. This method combines the concept of probability efficiency for stochastic problems with the reference point philosophy for deterministic multiobjective problems. The decision maker expresses her/his references by dividing the variation range of each objective into intervals, and by setting the desired probability for each objective to achieve values belonging to each interval. These intervals may also be redefined during the process. This interactive procedure helps the decision maker to understand the stochastic nature of the problem, to discover the risk level (s)he is willing to assume for each objective, and to learn about the trade-offs among the objectives.     

An interactive fuzzy satisficing method for multiobjective stochastic linear programming problems through an expectation model

For decision making problems involving uncertainty, both stochastic programming as an optimization method based on the theory of probability and fuzzy programming representing the ambiguity by fuzzy concept have been developing in various ways. In this paper, we focus on multiobjective linear programming problems with random variable coefficients in objective functions and/or constraints. For such problems, as a fusion of these two approaches, after incorporating fuzzy goals of the decision maker for the objective functions, we propose an interactive fuzzy satisficing method for the expectation model to derive a satisficing solution for the decision maker. An illustrative numerical example is provided to demonstrate the feasibility of the proposed method.     

Solution approaches for the multiobjective stochastic programming

We survey in this paper various solution approaches for multiobjective stochastic problems where random variables can be in both objectives and constraints parameters. Once a problem requires a stochastic formulation, a first step consists in transforming the problem into its deterministic formulation. We propose to classify and evaluate such transformations with regards to the many proposed concepts of efficiency. The paper addresses also some applications of the multiobjective stochastic programming models.     

On the convergence of multiobjective evolutionary algorithms

We consider the usage of evolutionary algorithms for multiobjective programming (MOP), i.e. for decision problems with alternatives taken from a real-valued vector space and evaluated according to a vector-valued objective function. Selection mechanisms, possibilities of temporary fitness deterioration, and problems of unreachable alternatives for such multiobjective evolutionary algorithms (MOEAs) are studied. Theoretical properties of MOEAs such as stochastic convergence with probability 1 are analyzed.     

An interactive fuzzy satisficing method for random fuzzy multiobjective integer programming problems through probability maximization with possibility

This paper considers multiobjective integer programming problems where each coefficient of the objective functions is expressed by a random fuzzy variable. A new decision making model is proposed by incorporating the concept of probability maximization into a possibilistic programming model. For solving transformed deterministic problems, genetic algorithms with double strings for nonlinear integer programming problems are introduced. An interactive fuzzy satisficing method is presented for deriving a satisficing solution to a decision maker by updating the reference probability levels. An illustrative numerical example is provided to clarify the proposed method.     

Risk-control approach for bottleneck transportation problem with randomness and fuzziness

Solving transportation problems is essential in engineering and supply chain management, where profitability depends on optimal traffic flow. This study proposes risk-control approaches for two bottleneck transportation problems with random variables and preference levels to objective functions with risk parameters. Each proposed model is formulated as a multiobjective programming problem using robust-based optimization derived from stochastic chance constraints. Since it is impossible to obtain a transportation pattern that optimizes all objective functions, our proposed models are numerically solved by introducing an aggregation function for the multiobjective problem. An exact algorithm that performs deterministic equivalent transformations and introduces auxiliary problems is also developed.     

A compromise solution for the multiobjective stochastic linear programming under partial uncertainty

This paper solves the multiobjective stochastic linear program with partially known probability. We address the case where the probability distribution is defined by crisp inequalities. We propose a chance constrained approach and a compromise programming approach to transform the multiobjective stochastic linear program with linear partial information on probability distribution into its equivalent uniobjective problem. The resulting program is then solved using the modified L-shaped method. We illustrate our results by an example.     

A remark on multiobjective stochastic optimization via strongly convex functions

Many economic and financial applications lead (from the mathematical point of view) to deterministic optimization problems depending on a probability measure. These problems can be static (one stage), dynamic with finite (multistage) or infinite horizon, single objective or multiobjective. We focus on one-stage case in multiobjective setting. Evidently, well known results from the deterministic optimization theory can be employed in the case when the “underlying” probability measure is completely known. The assumption of a complete knowledge of the probability measure is fulfilled very seldom. Consequently, we have mostly to analyze the mathematical models on the data base to obtain a stochastic estimate of the corresponding “theoretical” characteristics. However, the investigation of these estimates has been done mostly in one-objective case. In this paper we focus on the investigation of the relationship between “characteristics” obtained on the base of complete knowledge of the probability measure and estimates obtained on the (above mentioned) data base, mostly in the multiobjective case. Consequently we obtain also the relationship between analysis (based on the data) of the economic process characteristics and “real” economic process. To this end the results of the deterministic multiobjective optimization theory and the results obtained for stochastic one objective problems will be employed.     

Concavity and efficient points of discrete distributions in probabilistic programming

We consider stochastic programming problems with probabilistic constraints involving integer-valued random variables. The concept of a p-efficient point of a probability distribution is used to derive various equivalent problem formulations. Next we introduce the concept of r-concave discrete probability distributions and analyse its relevance for problems under consideration. These notions are used to derive lower and upper bounds for the optimal value of probabilistically constrained stochastic programming problems with discrete random variables. The results are illustrated with numerical examples. Received: October 1998 / Accepted: June 2000?Published online October 18, 2000