求解随机相关机会规划的有效算法

随机相关机会规划作为一类重要的随机规划,存在于许多领域中.为了寻找更为有效的求解随机相关机会规划的算法,采用随机仿真来逼近机会函数,在微粒群算法中利用随机仿真估计适应值,提出一种将随机仿真与微粒群算法相结合的随机相关机会规划算法.通过实例仿真测试该算法的性能,并与遗传算法进行比较,结果表明本算法具有一定的优势.     

基于PSO求解随机相关机会规划的有效算法

随机相关机会规划是一类有着广泛应用背景的随机规划问题,通过采用随机仿真产生样本训练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随机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。     

Dependent-chance programming with fuzzy decisions

Dependent-chance programming (DCP) is a new type of stochastic programming and has been extended to the area of fuzzy programming. This paper provides a spectrum of DCP and dependent-chance multiobjective programming (DCMOP) as well as dependent-chance goal programming (DCGP) models with fuzzy rather than crisp decisions. The terms of uncertain environment, event, chance function, and induced constraints are discussed in the case of fuzzy decisions. A technique of fuzzy simulation is also designed for computing chance functions. Finally, we present a fuzzy simulation-based genetic algorithm for solving these models and illustrate its effectiveness by some numerical examples     

Goal programming approach to solving network design problem with multiple objectives and demand uncertainty

The transportation network design problem (NDP) with multiple objectives and demand uncertainty was originally formulated as a spectrum of stochastic multi-objective programming models in a bi-level programming framework. Solving these stochastic multi-objective NDP (SMONDP) models directly requires generating a family of optimal solutions known as the Pareto-optimal set. For practical implementation, only a good solution that meets the goals of different stakeholders is required. In view of this, we adopt a goal programming (GP) approach to solve the SMONDP models. The GP approach explicitly considers the user-defined goals and priority structure among the multiple objectives in the NDP decision process. Considering different modeling purposes, we provide three stochastic GP models with different philosophies to model planners’ NDP decision under demand uncertainty, i.e., the expected value GP model, chance-constrained GP model, and dependent-chance GP model. Meanwhile, a unified simulation-based genetic algorithm (SGA) solution procedure is developed to solve all three stochastic GP models. Numerical examples are also presented to illustrate the practicability of the GP approach in solving the SMONDP models as well as the robustness of the SGA solution procedure.     

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．     

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。     

模糊相关机会规划的蚁群优化算法及收敛性分析

模糊相关机会规划(Fuzzy dependent-chance programming, FDCP)因其非线性、非凸性及模糊性,对经典的优化理论提出了极大的挑战. 本文为解决复杂的模糊相关机会规划问题设计了一种基于模糊模拟的蚁群优化算法, 证明了该算法的收敛性,并通过估算期望收敛时间以分析蚁群优化算法的收敛速度. 数值案例研究验证了该算法的有效性、稳定性及准确性.     

Fuzzy random dependent-chance programming

This paper presents the concepts of uncertain environment, event, chance function and principle of uncertainty for fuzzy random decision systems, thus offering a theoretical framework of fuzzy random dependent-chance programming. A hybrid intelligent algorithm is applied to solving fuzzy random dependent-chance programming models. Some numerical examples are also provided to illustrate the effectiveness of hybrid intelligent algorithm     

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.