鲁棒优化与多智能体协调的电梯群控调度

针对电梯群控调度过程中交通流不确定和优化求解复杂性的问题,提出一种混合鲁棒优化方法和多智能体协调的电梯群控调度方法。分析了电梯群控调度中的交通流不确定问题,建立了解决该不确定问题的电梯调度决策鲁棒优化模型。为了弥补模型求解复杂度过高的不足,进一步提出鲁棒优化和多智能体调度结合的混合调度决策方法。仿真结果表明,该方法能有效改善群控调度的性能,提高群控调度对交通模式的适应性。     

需求不确定的电子供应链多目标鲁棒动态运作模型

建立了由多个供应商和多个不确定需求的顾客构成的多阶段供应链动态运作模型。供应链中的供应商可以通过电子市场也可以直接将多种产品供应给不同的顾客。采用已知概率的情景集合描述顾客不确定需求,利用基于情景分析的鲁棒优化方法,建立了供应链的运作模型。该模型为一个多目标动态规划问题,满足诸如尽可能达到顾客需求、系统的总成本最小、供应商的开工率不低于某一指定水平、对应于不确定需求的决策的鲁棒性等多个相互冲突目标。数值仿真结果表明,模型的解是最保守的,但却能够有效地保证供应链运作的鲁棒性。     

基于不确定中断概率的鲁棒供应链网络设计

为了给决策者在不确定环境中设计具有鲁棒性的供应链网络提供决策支持,考虑供应链网络中上游制造商设备故障或网络中节点间链接失效导致的供应中断情况,建立了随机需求和供应中断下基于路径的供应链网络设计非线性规划模型。为解决非线性规划难以求解的问题,采用分段线性化方法将其转化为线性规划模型。进一步考虑了中断情景概率不确定的情况,并采用区间和椭球不确定集进行建模,给出了不确定中断概率下的供应链网络设计鲁棒优化模型;运用线性规划和拉格朗日对偶理论,将其转化为易于求解的线性规划和二阶锥规划问题。以实际案例为背景进行数值计算,验证了所建模型的有效性。     

订单不确定下双资源约束多装配线鲁棒调度

考虑生产过程中的订单不确定等因素,建立了以最大化交付满意度、最大化装配线平衡率及最小化完工时间跨度为目标的鲁棒调度模型,基于差分进化算法和粒子群算法提出了对模型进行求解的混合优化算法,并通过算例验证了混合优化算法求解该鲁棒调度模型的可行性和有效性。最后综合分析PTCN公司二厂多装配线生产车间的实际生产情况,将所建立的鲁棒调度模型和提出的混合优化算法应用于实际的多装配线生产过程,获得了较优的调度结果。     

基于变分贝叶斯的鲁棒自适应因子图优化组合导航算法

复杂环境下的量测粗差和时变噪声严重影响了状态估计的精度和可靠性,对此提出了一种基于变分贝叶斯的鲁棒自适应因子图优化组合导航算法。首先,基于先验和后验两阶段更新将变分贝叶斯推断引入因子图优化框架中,以估计时变量测噪声协方差;其次,利用相邻帧间的平均新息构造量测协方差预测值,作为粗差判据来实现稳健估计。基于INS/GNSS组合导航的仿真和现场实验评估表明,所提方法能在粗差干扰的情况下有效估计时变量测噪声,相比M估计和滑动窗口自适应因子图优化算法的水平定位误差分别减小了26.7%和39.8%,兼顾了估计精度和抗差性能,具有较好的复杂环境适应性。     

不确定信息条件下Job-shop调度的混合智能算法

目前大多数生产调度的研究往往聚焦于经典调度问题的优化算法而忽略了车间中大量存在的不确定性，因而难以应用于实际车间调度。采用随机变量来描述真实车间中存在的一些不确定信息，在基于不确定规划理论的基础上建立了相应的不确定性调度模型，并研究了解决此类问题的混合智能算法。开发了混合智能优化原型系统，并结合仿真工具对该调度模型和混合智能算法进行了验证。     

基于鲁棒优化模型的单交叉口交通信号配时的研究

针对城市交通道路单交叉口多相位的控制问题,考虑到各相位交通流具有不确定性的特性,在以交叉口车均延误最小为控制目标的基础上,提出了基于情景的鲁棒优化模型,并针对该优化模型以相位绿灯时间和周期时长为控制变量,采用了遗传算法进行优化。最后,以某交叉路口为例进行了仿真实验,并将确定性模型与鲁棒模型的结果进行了对比。结果表明,鲁棒模型所求得的交通信号配时方案能够更为有效地应对交通流不确定性导致的随机差异,具有较强的鲁棒性。     

带有区间不确定任务时间的装配线鲁棒平衡

为解决任务完成时间为连续区间参数的第二类装配线平衡问题,提出了装配线鲁棒平衡的思想.采用最小化最大后悔值的鲁棒决策准则,建立了装配线鲁棒平衡的数学模型,提出了计算给定任务分配方案对应节拍时间最大后悔值的策略,并设计了基于遗传算法的求解方法.通过一系列测试问题和某实际汽车总装生产线平衡问题,验证了所提方法的可行性和有效性.     

面向产品族架构的鲁棒双层优化模型

针对产品族架构中平台参数和个性化参数之间的主从特点以及产品族与产品族中各产品之间的全局与局部的设计关系,在不确定的产品设计环境下,建立了一种具有不确定系数的面向产品族架构的一主多从双层优化设计模型,并应用鲁棒优化方法将其转化为从者无关联的确定性非线性双层规划,设计了一种混合遗传算法进行求解,使产品族架构设计具有较强的鲁棒性。最后,通过桁架产品族架构设计阐述了模型的应用。     

Multi-objective optimization for model predictive control

This paper presents a technique of multi-objective optimization for Model Predictive Control (MPC) where the optimization has three levels of the objective function, in order of priority: handling constraints, maximizing economics, and maintaining control. The greatest weights are assigned dynamically to control or constraint variables that are predicted to be out of their limits. The weights assigned for economics have to out-weigh those assigned for control objectives. Control variables (CV) can be controlled at fixed targets or within one- or two-sided ranges around the targets. Manipulated Variables (MV) can have assigned targets too, which may be predefined values or current actual values. This MV functionality is extremely useful when economic objectives are not defined for some or all the MVs. To achieve this complex operation, handle process outputs predicted to go out of limits, and have a guaranteed solution for any condition, the technique makes use of the priority structure, penalties on slack variables, and redefinition of the constraint and control model. An engineering implementation of this approach is shown in the MPC embedded in an industrial control system. The optimization and control of a distillation column, the standard Shell heavy oil fractionator (HOF) problem, is adequately achieved with this MPC.     

New algorithm for CBR-RBR fusion with robust thresholds

The case-based reasoning(CBR) and rule-based reasoning(RBR) fusion systems include a diverse range of fusion methods and their tasks are characterized by interleaving combination of the reasoning procedures. Existing approaches cannot clarify the complex relationships between data from the knowledge sources nor uniformly represent the heterogeneous case and rule knowledge in one fusion space. As a result, existing approaches fail to solve system fragility due to knowledge uncertainty and reasoning unreliability. For the purpose of addressing the difficulties, a novel algorithm for CBR-RBR fusion with robust thresholds(CRFRT) is proposed. Heterogeneous case and rule knowledge are uniformly represented in one defined fusion unitary space. The robust thresholds have been achieved to distinguish the complex relationships between meta-knowledge in the fusion space and to enhance system capacity of knowledge identification. Furthermore, fusion reasoning strategies are constructed for CRFRT and its procedure based on which robust solution of the fusion reasoning problem is obtained. Finally, CRFRT is validated by benchmark problems in machine learning. Compared with other CBR and RBR approaches, the reasoning efficiency and accuracy are increased by 5% and 2.2% respectively. The variations of system accuracy are decreased by 2% to 3.8%. The above results show that the CRFRT algorithm boosts the system’s effectiveness and robustness. The proposed CRFRT can solve the fragility of complex intelligence decision system and give quality performance for fault diagnosis.     

间歇过程PSO SQP混合优化算法研究*

针对SQP算法在求解具有复杂约束的间歇过程优化时容易陷入局部极值点的问题,本文提出一种PSO-SQP混合优化算法。该算法首先采用外点罚函数法将间歇过程有约束的优化问题转换为无约束的优化问题,利用PSO强大的全局搜索能力对其进行求解,并把搜索结果作为SQP搜索初始点,以此弥补SQP全局搜索弱的缺点,再利用SQP良好的局部收敛性和较强的非线性收敛速度对原优化问题进行精细搜索,弥补了PSO局部搜索弱的缺点,通过不断的迭代最终获得优化问题的全局最优解。该算法充分利用了SQP和PSO的优缺点,增强了其对复杂约束优化问题的求解能力。将本文提出的算法用于连续搅拌化学反应系统温度控制中,仿真结果表明产物浓度能够充分逼近期望值,且反应器的温度轨迹收敛,从而验证了该算法的有效性和实用价值。     

兼顾用户满意度和发电侧收益的微电网系统量子粒子群优化

兼顾微电网系统发电侧与用户侧的综合利益,从能量管理的角度出发,建立了以用户满意度和发电侧收益为目标的优化模型。首先,采用多目标局部变异-自适应量子粒子群算法(Multi-objective local mutation adaptive quantum particle swarm optimization,MO-LM-AQPSO)获得用户满意度及发电侧收益的Pareto前沿。然后,引入缺电损失,以发电侧收益最大为目标,选取了非支配解中的最优解,并通过算例仿真验证其有效性。进而引入可平移负荷及分时电价激励机制,通过合理的峰谷电价比以引导用户积极参与需求侧响应。仿真结果表明,合理的激励措施,可提高微电网收益和用户满意度实现可再生能源的最大化利用及蓄电池运行损耗的有效减少。     

Application of multi-objective controller to optimal tuning of PID gains for a hydraulic turbine regulating system using adaptive grid particle swam optimization

A hydraulic turbine regulating system (HTRS) is one of the most important components of hydropower plant, which plays a key role in maintaining safety, stability and economical operation of hydro-electrical installations. At present, the conventional PID controller is widely applied in the HTRS system for its practicability and robustness, and the primary problem with respect to this control law is how to optimally tune the parameters, i.e. the determination of PID controller gains for satisfactory performance. In this paper, a kind of multi-objective evolutionary algorithms, named adaptive grid particle swarm optimization (AGPSO) is applied to solve the PID gains tuning problem of the HTRS system. This newly AGPSO optimized method, which differs from a traditional one-single objective optimization method, is designed to take care of settling time and overshoot level simultaneously, in which a set of non-inferior alternatives solutions (i.e. Pareto solution) is generated. Furthermore, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto set. An illustrative example associated with the best compromise solution for parameter tuning of the nonlinear HTRS system is introduced to verify the feasibility and the effectiveness of the proposed AGPSO-based optimization approach, as compared with two another prominent multi-objective algorithms, i.e. Non-dominated Sorting Genetic Algorithm II (NSGAII) and Strength Pareto Evolutionary Algorithm II (SPEAII), for the quality and diversity of obtained Pareto solutions set. Consequently, simulation results show that this AGPSO optimized approach outperforms than compared methods with higher efficiency and better quality no matter whether the HTRS system works under unload or load conditions.