目标与机会平衡规划法

提出一种目标与机会平衡规划法．基于待决策问题的有效决策前沿曲线形状的全局信息,通过权衡决策目标收益和实现机会两个因素的数值关系以及二者沿有效决策前沿曲线变化速度的对比关系,最大限度地提高决策效用．该方法能以更合理、更全面、更灵活的方式分析处理随机优化问题,有效解决期望值模型、机会约束规划法和相关机会规划法之间的矛盾．最后通过算例表明了所提出方法的主要特点和有效性．

     

基于第三方回收再制造逆向物流网络设计

针对再制造逆向物流网络设计问题, 在考虑产品回收量和消费市场需求量不确定的条件下, 以第三方物流企业收益最大化和制造商收益最大化为目标建立了基于第三方回收多周期再制造逆向物流网络模型. 利用该模型可以确定每个周期制造商购买第三方物流企业产品的最优价格, 可以确定网络中回收中心和检测/拆卸中心的开设数量以及各设施间的物流量分配. 将不确定参数用三角模糊数表示, 借助模糊机会约束规划方法将该模型转化为确定性等价模型. 利用算例验证了模型的有效性, 并用目标规划法分析了两个目标之间的关系.     

节水减排的水系统集成优化研究进展

水系统集成是节水减排的最有效方法.在过去的二十多年里,水系统集成优化的理论和实践取得了长足的发展.水系统集成的方法分为图示法和数学规划法.本文简单回顾了近年来国内外在这两种方法的研究进展.主要针对废水再生循环和再生回用系统,介绍了采用图示法确定系统目标的方法,以及数学规划法超结构和数学模型的建立.并通过一个实例,说明了方法的应用并比较了废水直接回用/废水再生循环和废水再生回用系统的优化结果.     

基于信标的柔性制造系统的优化死锁预防策略

针对柔性制造系统（FMS）中的死锁问题，根据矩阵理论给出了Petri网中基本信标的概念，进而提出一种基于基本信标和混合整数规划法（MIP）的死锁预防策略．该策略将最优基本信标作为控制对象，以混合整数规划法给出的系统无死锁条件为目标函数．不考虑从属信标受控条件便可在多项式时间内使系统受控．该控制策略的显著特点是以较低的计算复杂度实现整个系统受控，并使需要添加的控制库所和连接弧大大减少．控制实例证明了其有效性．     

基于遗传算法和逐步二次规划法的混合频域H_∞建模

研究了基于遗传算法和逐步二次规划法的混合频域H∞ 建模方法 .混合建模方法既有较好的全局优化能力 ,也有较好的局部优化能力 ,使得辨识结果更稳定、更精确     

动态规划法中返求最优可靠度分配的实用算法

本文简单介绍动态规划法应用，并着重对动态规划法中返求最优可靠度分配的算法进行研究，提出了一种比较简单实用的实现算法，该方法的优点在于：简单实用，易于编程和实现，解决参数化编程求解问题。     

每对结点间最短路径VC++实现

最短路径问题应用广泛、实现多样。动态规划法能够有效计算出每对节点间的最短路径。本文利用VC++实现了动态规划法的每对节点间最短路径问题。     

基于遗传算法和逐步二次规划法的混合频域H∞建模

研究了基于遗传算法和逐步二次规划法的混合频域H∞建模方法,混合建模方法既有较好的全局优化能力,也有较好的局部优化能力,使得辨识结果更稳定、更精确。     

基于改进动态规划法的电力系统机组组合问题研究

机组组合问题是在调度周期内满足各种运行约束条件的前提下,通过合理地控制机组的开、停状态以实现总运行成本最低目标的问题。本文以电力系统机组组合为研究对象,对优先顺序法与动态规划法在机组组合中的应用进行了研究和探讨,并根据他们的优缺点,建立了改进的算法—基于优先顺序法的动态规划法。     

基于多目标决策的隐写检测评估

提出多目标隐写检测评估方法,分析隐写检测的应用领域,针对目前隐写检测指标过于粗略的问题,对隐写检测评估指标进行扩展和描述。结合隐写检测应用领域的不同需求,应用数学规划法对扩展的6个指标进行优选,包括正检测率、负检测率、虚警率、漏警率、检测速度、适用性。按照应用对各个评估指标的侧重,找出最适合于该应用领域的检测度量,使隐写检测评估更加客观和有效。     

托盘共用系统调度两阶段随机机会约束规划模型研究

根据托盘共用系统调度的特点,建立了以托盘共用系统调度总成本最小为目标的两阶段随机机会约束规划模型．该模型综合考虑了需求随机、供给随机、运输能力随机、装卸能力随机等因素;采用机会约束规划方法对模型进行了确定性等价转换;通过算例进行了数值求解和数值分析,验证了模型的可行性和有效性．     

供应链中二级分销网络优化设计的模糊机会约束规划模型

提出了供应链中二级分销网络优化设计的模糊机会约束规划模型. 模型中将各个需求地对产品的需求量以及各分厂的生产能力等难于确定的参数看成是模糊参数, 并进一步讨论了如何将模型中的机会约束清晰化. 文中还讨论了采用启发式算法同分枝定界法相结合以提高问题的求解速度.     

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.     

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.     

考虑鲁棒性的生产调度期望值模型

对于含有随机因素的生产调度过程,期望值模型不能保证调度的鲁棒性．本文借鉴机会约束规划思想,根据随机参数概率分布的两种不同情况,提出了使期望值模型可行解满足鲁棒性能指标的充分条件,并据此构造了鲁棒性约束,使解空间收缩,提高模型的鲁棒性．仿真实验结果表明该模型在解决调度鲁棒性的问题方面是有效的．     

模糊环境下多周期多决策生鲜闭环物流网络

针对生鲜品因易腐易损性而产生的高频次物流配送及不确定需求与退货量的问题，提出了模糊环境下多周期生鲜闭环物流网络系统，以实现最小系统成本、最优设施选址与最佳配送路径的多决策安排。为求解系统对应的模糊混合整数线性规划（FMILP）模型，首先将生鲜需求量和退货量设定为三角模糊值，其次运用模糊机会约束规划方法将模糊约束等价变换为清晰式，最后利用遗传算法（GA）和粒子群优化（PSO）算法搜索案例的最优解。实验结果表明，多周期闭环系统比单周期更能兼顾多决策规划，同时三角模糊量的置信水平变化对企业最优运作有着显著影响，进而为相关决策者提供借鉴。     

Interactive fuzzy stochastic multi-level 0–1 programming using tabu search and probability maximization

In this paper, we consider interactive fuzzy programming for multi-level 0–1 programming problems involving random variable coefficients both in objective functions and constraints. Following the probability maximization model together with the concept of chance constraints, the formulated stochastic multi-level 0–1 programming problems are transformed into deterministic ones. Taking into account vagueness of judgments of the decision makers, we present interactive fuzzy programming. In the proposed interactive method, after determining the fuzzy goals of the decision makers at all levels, a satisfactory solution is derived efficiently by updating satisfactory levels of the decision makers with considerations of overall satisfactory balance among all levels. For solving the transformed deterministic problems efficiently, we also introduce novel tabu search for general 0–1 programming problems. A numerical example for a three-level 0–1 programming problem is provided to illustrate the proposed method.     

Uncertain Optimal Control Approach for CO2 Mitigation Problem

The new types of uncertain control approaches based on the critical value criterion and chance criterion are respectively introduced to deal with multi‐stage dynamic optimization problems in the presence of target constraints. Integrating the method of dynamic programming with the viability approach, the solving procedure of the optimal control sequence is provided. As a case study, a cost‐effectiveness problem of mitigation policies for uncertain carbon dioxide emissions is analyzed and numerically computed to illustrate the proposed approaches.     

Fuzzy random programming with equilibrium chance constraints

To model fuzzy random decision systems, this paper first defines three kinds of equilibrium chances via fuzzy integrals in the sense of Sugeno. Then a new class of fuzzy random programming problems is presented based on equilibrium chances. Also, some convex theorems about fuzzy random linear programming problems are proved, the results provide us methods to convert primal fuzzy random programming problems to their equivalent stochastic convex programming ones so that both the primal problems and their equivalent problems have the same optimal solutions and the techniques developed for stochastic convex programming can apply. After that, a solution approach, which integrates simulations, neural network and genetic algorithm, is suggested to solve general fuzzy random programming problems. At the end of this paper, three numerical examples are provided. Since the equivalent stochastic programming problems of the three examples are very complex and nonconvex, the techniques of stochastic programming cannot apply. In this paper, we solve them by the proposed hybrid intelligent algorithm. The results show that the algorithm is feasible and effectiveness.