基于二阶段随机规划的不确定条件下过程优化研究

在基于二阶段随机规划的不确定条件下过程优化研究中,Ierapetritou and Pistikopoulos(1994)提出了可行域求解策略,Liu and Sahinidis(1996)在此基础上用蒙特卡洛积分策略代替了高斯积分策略,但对于可行域的限定条件尚有欠缺。本文分析和比较了前人的工作,将蒙特卡罗积分策略与基于对偶理论的可行域限定条件相结合,提出了新的求解策略,不仅避免了可行域求解策略中求解一系列子问题而引起的计算负荷随不确定参数数目呈指数增加的不足,而且使蒙特卡洛积分策略算法中的可行域限定条件更加合理,应用文献中的算例进行了仿真实验,证明了该算法的有效性。     

The use of metamodeling techniques for optimization under uncertainty

Metamodeling techniques have been widely used in engineering design to improve efficiency in the simulation and optimization of design systems that involve computationally expensive simulation programs. Many existing applications are restricted to deterministic optimization. Very few studies have been conducted on studying the accuracy of using metamodels for optimization under uncertainty. In this paper, using a two-bar structure system design as an example, various metamodeling techniques are tested for different formulations of optimization under uncertainty. Observations are made on the applicability and accuracy of these techniques, the impact of sample size, and the optimization performance when different formulations are used to incorporate uncertainty. Some important issues for applying metamodels to optimization under uncertainty are discussed.     

蚁群优化算法在求解随机组合优化问题中的应用综述*

不确定条件下的优化问题更贴近真实世界环境,因而日益受到广泛关注。综述了蚁群优化在求解一组不确定条件下的组合优化问题,即随机组合优化问题方面的应用。首先介绍了不确定条件下组合优化问题的概念分类模型,给出了随机组合优化问题的一般定义;然后指出了其与求解传统确定性组合优化问题的不同之处,即目标函数的计算存在不确定性,并详细论述了目前解决方法的进展;最后分析了该领域值得重点关注的几个研究方向,并对其未来发展进行了展望。     

Multiobjective process line optimization under uncertainty applied to papermaking

Multiobjective optimization methodology for the development of the papermaking process is considered. The aim is to find efficient and reliable solution procedures for the process line model consisting of sequential unit-process models; some of them based on physics, whereas others on experimental data. By the consequence of modeling procedures, nonphysical states or inherited from modeling data in statistical case, the unit-process models may suffer from undesired unreliability. To control the uncertainty resulting from the unit-process models, a new multiobjective optimization approach is introduced where both the papermaking targets as well as the uncertainty related unit-process models are simultaneously taken into account. We illustrate the solution process by numerical examples related to the quality of the produced paper.     

不确定条件下控制分配问题的鲁棒优化方法

针对具有冗余执行机构的过驱动系统, 在考虑控制效率不确定性的条件下, 提出了一种基于鲁棒优化理论的控制分配算法. 研究了原始不确定鲁棒优化模型的建立和基于椭球不确定集的鲁棒对等式的转化问题, 并推广到可由锥二次不等式表示的不确定集的情况. 讨论了鲁棒优化控制分配算法的求解方法及其计算复杂度. 最后, 针对多操纵面飞机的最优控制分配问题与传统算法进行了仿真比较, 结果表明鲁棒优化算法能有效降低控制效率不确定性的影响, 使分配结果更为合理, 从而具有更好的鲁棒性, 同时能有效提高操纵面故障情况下闭环系统的控制重构能力, 很好地改善了飞控系统的性能.     

Real-time optimization under parametric uncertainty: a probability constrained approach

Uncertainty is an inherent characteristic in most industrial processes, and a variety of approaches including sensitivity analysis, robust optimization and stochastic programming have been proposed to deal with such uncertainty. Uncertainty in a steady state nonlinear real-time optimization (RTO) system and particularly making robust decisions under uncertainty in real-time has received little attention. This paper discusses various sources of uncertainty within such closed loop RTO systems and a method, based on stochastic programming, that explicitly incorporates uncertainty into the RTO problem is presented. The proposed method is limited to situations where uncertain parameters enter the constraints nonlinearly and uncertain economics enter the objective function linearly. Our approach is shown to significantly improve the probability of a feasible solution in comparison to more conventional RTO techniques. A gasoline blending example is used to demonstrate the proposed robust RTO approach.     

基于中值和多尺度的组合优化滤波器

考虑到一般机器人视觉导航滤波算法的通用性和有效性比较差的缺点,结合中值滤波器和多尺度自适应融合滤波器这两种算法的优点,提出一种基于中值和多尺度的组合优化滤波器。算法首先应用中值滤波,然后进行多尺度滤波处理,最后根据最小平均绝对误差准则,将滤波后结果进行融合处理。实验结果表明：该算法能很好地滤除机器人道路图像各种常见的噪声,从而提高算法的有效性和通用性。     

基于MATLAB的局域网多机并行遗传算法求解不确定优化问题

本文提出一种新的不确定非线性优化问题的确定化形式,采用局域网多机并行遗传算法求解不确定优化问题,应用常用的计算软件MATLAB来实现该算法。通过在主机上开辟共享缓冲区,在其中设立数据文件,主机和从机都用MATLAB语言中读写文件的命令来交换数据,以实现局域网中的并行计算环境。该算法编程简便,充分利用操作系统提供的数据共享传输功能,性价比较高。     

Energy supply planning and supply chain optimization under uncertainty

In energy supply planning and supply chain design, the coupling between long-term planning decisions like capital investment and short-term operation decisions like dispatching present a challenge, waiting to be tackled by systems and control engineers. The coupling is further complicated by uncertainties, which may arise from several sources including the market, politics, and technology. This paper addresses the coupling in the context of energy supply planning and supply chain design. We first discuss a simple two-stage stochastic program formulation that addresses optimization of an energy supply chain in the presence of uncertainties. The two-stage formulation can handle problems in which all design decisions are made up front and operating parameters act as ‘recourse’ decisions that can be varied from one time period to next based on realized values of uncertain parameters. The design of a biodiesel production network in the Southeastern region of the United States is used as an illustrative example. The discussion then moves on to a more complex multi-stage, multi-scale stochastic decision problem in which periodic investment/policy decisions are made on a time scale orders of magnitude slower than that of operating decisions. The problem of energy capacity planning is introduced as an example. In the particular problem we examine, annual acquisition of energy generation capacities of various types are coupled with hourly energy production and dispatch decisions. The increasing role of renewable sources like wind and solar necessitates the use of a fine-grained time scale for accurate assessment of their values. Use of storage intended to overcome the limitations of intermittent sources puts further demand on the modeling and optimization. Numerical challenges that arise from the multi-scale nature and uncertainties are reviewed and some possible modeling and numerical solution approaches are discussed.     

Hierarchical stochastic modeling and optimization for petroleum field development under geological uncertainty

Real-world simulation optimization (SO) problems entail complex system modeling and expensive stochastic simulation. Existing SO algorithms may not be applicable for such SO problems because they often evaluate a large number of solutions with many simulation calls. We propose an integrated solution method for practical SO problems based on a hierarchical stochastic modeling and optimization (HSMO) approach. This method models and optimizes the studied system at increasing levels of accuracy by hierarchical sampling with a selected set of principal parameters. We demonstrate the efficiency of HSMO using the example problem of Brugge oil field development under geological uncertainty.     

用随机神经网络优化求解改进算法的研究

随机神经网络是一种仿照实际的生物神经网络的生理机制而定义的网络,其网络结构及应用具有自身的特点。在详细讨论了动态随机神经网络求解典型NP优化问题TSP的算法的同时,特别提出了一种有效改进算法,使得参数在简单选取的情况下保证能量函数的下降,在组合优化问题上具有普遍意义,并且在10城市TSP对改进算法进行验证,指出RNN是解决TSP问题的有效途径。     

An approach to solving discrete vector optimization problems over a combinatorial set of permutations

Complex discrete multicriteria problems over a combinatorial set of permutations are analyzed. Some properties of an admissible domain for a combinatorial multicriteria problem embedded into an arithmetic Euclidian space are considered. Optimality conditions are obtained for different types of effective solutions. A new approach to solving the problems formulated is constructed and substantiated. This work was supported by the Fundamental Research Fund of Ukraine (project Φ251/094). __________ Translated from Kibernetika i Sistemnyi Analiz, No. 3, pp. 158–172, May–June 2008.     

回收不确定下WEEE逆向物流网络鲁棒优化设计

电子电器废弃物（WEEE）存在对环境和人体健康的危害,有效对其进行回收能避免此类危害和提高资源的利用率。WEEE逆向物流回收网络的设计为实现这一目标起到了关键的作用。考虑WEEE逆向物流网络运作的不确定性,引入风险偏好系数和约束背离惩罚系数,建立了WEEE逆向物流网络的鲁棒优化模型。该模型能允许决策者对系统运作的鲁棒水平进行调节,同时能允许决策者对风险偏好进行调节。仿真结果表明建立的模型能有效抑制逆向物流系统运作的不确定性,使系统具有更低的风险。     

CART:组合自适应随机测试方法

自适应随机测试方法将测试用例均匀分布于整个输入空间,以提高测试效率.然而,当被测软件的输入参数存在错误相关性,使失效区域形状成为“片状”时,传统的自适应随机测试方法效率将急剧下降.针对“片状”失效区域的特点,本文提出了一种新的自适应随机测试方法:组合自适应随机测试方法.该方法将输入参数划分为多个不同的组;每一组被视作一个独立的输入空间并通过自适应随机测试方法生成“准”测试用例序列;最终的测试用例将由各组“准”测试用例组合而成.实验表明,组合自适应随机测试方法能大幅度提高测试用例发现错误的效率.     

粒子群算法在组合优化问题上的研究与发展

粒子群优化算法是一种新兴的基于群智能搜索的优化技术。该算法简单、易实现、参数少,具有较强的全局优化能力,可有效应用于科学与工程实践中。介绍了算法的基本原理和算法在组合优化上一些改进方法的主要应用形式。最后,对粒子群算法作了一些深入分析并在此基础上对粒子群算法应用于组合优化问题做了一些总结。     

Methods of Constructing Argument of Objective Function in Combinatorial Optimization Problems

Combinatorial configurations of different types are studied. A new point of view is proposed for their classification. Three recurrent combinatorial operators are introduced, and used to form different types of combinatorial configurations.     

组合优化问题的一种精确求解方法

本文分析了深度优先搜索方法(DFS)和广度优先搜索(BFS)方法的特点,提出了一种混合使用动态规划方法和下界(上界)算法的精确求解方法求解组合优化问题。实验结果表明,下界(上界)非常接近问题的最优值时,这种方法非常有效。     

Multi-objective biogeography-based optimization for supply chain network design under uncertainty

This paper proposes a new two-stage optimization method for multi-objective supply chain network design (MO-SCND) problem with uncertain transportation costs and uncertain customer demands. On the basis of risk-neutral and risk-averse criteria, we develop two objectives for our SCND problem. We introduce two solution concepts for the proposed MO-SCND problem, and use them to define the multi-objective value of fuzzy solution (MOVFS). The value of the MOVFS measures the importance of uncertainties included in the model, and helps us to understand the necessity of solving the two-stage multi-objective optimization model. When the uncertain transportation costs and customer demands have joined continuous possibility distributions, we employ an approximation approach (AA) to compute the values of two objective functions. Using the AA, the original optimization problem becomes an approximating mixed-integer multi-objective programming model. To solve the hard approximating optimization problem, we design an improved multi-objective biogeography-based optimization (MO-BBO) algorithm integrated with LINGO software. We also compare the improved MO-BBO algorithm with the multi-objective genetic algorithm (MO-GA). Finally, a realistic dairy company example is provided to demonstrate that the improved MO-BBO algorithm achieves the better performance than MO-GA in terms of solution quality.