微生物发酵非线性系统辨识、控制及 并行优化研究

主要研究微生物发酵过程中不同工况下的非线性、非光滑且无法求得解析解的动力系统及其主要性质,建立了具有数百个不同动力系统为主要约束、有连续与离散两种辨识参量、依据实验数据与生物系统鲁棒性为性能指标的辨识模型,阐述了此类辨识模型与最优控制模型的建立方法、数值模拟方法及并行优化计算方法,并介绍了笔者的著作《非线性发酵动力系统——辨识、控制与并行优化》的基本内容。     

多阶段应急决策的方案链选择方法

针对多阶段不同情景下多指标多任务的应急决策问题,提出了一种方案链选择方法。在该方法中,首先给出了决策方案链的概念,并对多阶段多指标多任务的应急决策问题进行了描述;然后根据相邻阶段的子方案之间的相容性,构建了相容性关系矩阵;进一步地,在考虑相邻阶段子方案之间相容性的情形下,以各阶段的子方案的综合评价值最大为目标,建立了应急决策的方案链选择的优化模型。通过求解模型,得到应急决策的最优方案链。最后,通过一个算例说明了该方法的可行性和有效性。     

基于遗传算法的LuGre轮胎模型参数辨识研究

LuGre轮胎模型是一种动态轮胎摩擦力模型,该模型能够精确描述轮胎摩擦环节的动态特性,但由其高度非线性使得参数辨识非常困难.针对LuGre轮胎模型,提出一种基于遗传算法的模型参数两步辨识方法.首先由PD控制辨识出静态参数;然后由PID控制辨识出动态参数.在每一步辨识中,均采用遗传算法作为优化工具,从而避免了采用拟和辨识方法中误差较大,试验条件难以控制的缺点.该算法仅仅使用轮胎转速数据,而转速传感器是汽车防滑刹车控制系统(ABS)的基本组成部分,因此该算法可以与ABS结合工作,低成本的实现LuGre轮胎模型参数辨识.     

基因调控非耦联批式流加发酵切换系统的路径辨识北大核心CSCD

针对一类非耦联批式流加发酵生产1,3-丙二醇(1,3-PD)问题,综合考虑细胞内外环境、dha调节子的调控作用以及细胞内中间代谢产物3-羟基丙醛(3-HPA)对甘油脱水酶(GDHt)可能的抑制方式,建立了一个包含四条路径及四种切换模式的16维非线性切换系统模型.给出了状态变量关于参数的鲁棒性定义,并以胞外状态变量的计算值与实验值的相对误差、胞内状态变量的鲁棒性为性能指标,建立了一个非耦联批式流加发酵参数辨识模型.分析了系统状态对参数的灵敏度,基于此给出了性能指标与状态约束关于参数的梯度公式.构造了改进的并行粒子群一序列二次规划算法(PPSO-SQP).应用该算法,对辨识模型进行了数值求解,得到了数值最优的系统参数及3-HPA最可能的抑制方式.     

基因调控非耦联批式流加发酵切换系统的路径辨识

针对一类非耦联批式流加发酵生产1,3-丙二醇(1,3-PD)问题,综合考虑细胞内外环境、dha调节子的调控作用以及细胞内中间代谢产物3-羟基丙醛(3-HPA)对甘油脱水酶(GDHt)可能的抑制方式,建立了一个包含四条路径及四种切换模式的16维非线性切换系统模型.给出了状态变量关于参数的鲁棒性定义,并以胞外状态变量的计算值与实验值的相对误差、胞内状态变量的鲁棒性为性能指标,建立了一个非耦联批式流加发酵参数辨识模型.分析了系统状态对参数的灵敏度,基于此给出了性能指标与状态约束关于参数的梯度公式.构造了改进的并行粒子群一序列二次规划算法(PPSO-SQP).应用该算法,对辨识模型进行了数值求解,得到了数值最优的系统参数及3-HPA最可能的抑制方式.     

微生物批式流加发酵脉冲系统的参数辨识及稳定性

针对微生物批式流加发酵生产1,3-丙二醇的非线性脉冲系统,建立敏感参数的优化辨识模型(PDP),论述了模型解的性质、解与参量的关系以及辨识问题最优解的存在性.通过构造算法求得辨识问题最优解,并讨论了新参数下脉冲系统解的稳定性.     

基于CPLEX的原始——对偶嵌套分解算法

本文介绍了一种求解大规模下三角结构线性规划问题的原始一对偶嵌套分解算法，并以CPLEX9.0作为核心求解器将算法实现。原始—对偶嵌套分解算法将原问题分解成一系列子问题，每个子问题既可以收到来自前一阶段子问题的价格信息，又可以收到来自后一阶段子问题的资源信息，较传统嵌套分解算法具有更加平衡的信息传递方式和良好的收敛性。实验数据表明，该算法在求解较大规模、稀疏度较小、耦合度较小的下三角结构线性规划问题时，相比单纯形法，在时间效率上有明显提高。     

微生物批式流加发酵多阶段最优控制的最优性条件

在微生物批式流加发酵生产1,3一丙二醇(1,3-PD)过程中,关键是如何控制甘油和碱的流加速度.本文将流加速度看成一个随时间变化的控制函数,提出一个带控制的多阶段动力系统描述批式发酵过程,并证明了系统的一些性质.以终端时刻1,3-PD的生产强度最大为性能指标,以上述动力系统和连续状态不等式为约束条件建立了最优控制模型,最后利用不可微优化理论得到了最优控制问题的最优性条件,并证明了最优性条件和最优性函数零点的等价性.     

以函数为参量的间歇发酵非线性动力系统及其辨识

建立以连续分段线性函数为参量的间歇发酵非线性动力系统,证明该动力系统的主要性质及解的存在性.以实验数据拟合得到的光滑曲线为依据,提出了连续分段线性函数为优化变量的辨识模型,论述可辨识性.依状态变量与辨识函数的相关性,构造求解辨识模型的优化算法,并给出优化算法的收敛性分析及数值结果.     

兼顾效率和成本的ARMA控制图多目标优化设计

ARMA控制图是一种有效的自相关过程质量监控方法,为了能够同时对ARMA控制图监控方案的效率和成本进行优化,本文分别研究了ARMA控制图的平均运行长度和质量成本的计算方法,并由此建立了ARMA控制图的多目标优化设计模型。采用NSGA-Ш智能优化算法,通过一个具体的算例对该模型的计算方法进行了说明,针对不同程度的过程偏移给出了多目标优化设计的非劣解解集。然后通过灵敏度分析的方法研究了模型中的主要设计参数对监控方案的效率和成本的影响程度。最后,通过与其它几种ARMA控制图优化设计方案的比较分析,说明了本文提出的设计方法的优势。     

基于GFM-VAR的我国通用航空产业演进阶段识别及政策强度有效性分析

为揭示通用航空产业系统演化的动态和有序规律,挖掘产业形成和可持续发展的阶段识别证据,针对产业演化数据的信息不完全和连续聚类特性,基于灰色系统理论和最优分割理论构建通用航空产业演进阶段识别的灰色生成序列最优分割模型(Grey sequence generation-Fisher Model,GFM),并运用VAR模型分析通航产业政策强度的有效性。首先运用灰色系统理论的(位置加权)几何平均强化缓冲算子(W)GASBO((Weight)Geometry average strengthening buffer operator)扩张演化数据波形特征并生成灰色序列矩阵;运用最优分割理论的组内离差平方和最小判断原则判定通用航空产业演化的有序递进阶段;然后,通过我国2004~2013年通用航空产业演进阶段的实证研究,发现:我国通用航空产业演化以2010年为分界点,先后经历了初创和成长两个阶段;基于VAR模型的脉冲响应函数确定我国通航产业政策强度受“倒逼出台”、“时滞限制”的影响呈弱有效性;基于政策强度(WGASBO算子)的灰色生成序列最优分割模型更有效、更具有可行性。     

考虑路径冲突的AGV配置与调度优化

合理调度有限的码头资源以满足船舶的装卸时间要求是自动化集装箱码头的重要目标之一。针对自动化集装箱码头自动导引车(automated guided vehicle,AGV)配置与调度问题,考虑船舶装卸时间要求和AGV运输过程中的路径冲突,提出分阶段调度策略。将船舶装卸作业分为卸船阶段、装卸同步阶段、装船阶段三个阶段,在每个阶段中,建立以最小化最大完工时间和最小化AGV空载和等待时间为双目标的调度优化模型,并设计基于NSGA-Ⅱ的启发式算法求解。根据本阶段的实际完工时间,从最优解集中选择下一阶段AGV的配置与调度方案。最后对比其他调度方案表明本文调度方案能够满足集装箱船的装卸时间要求,且提高了AGV的利用率,更符合码头实际作业要求。     

Optimal stopping with a capacity constraint: Generalizing Shepp’s urn scheme

We formulate an optimal stopping problem for a variant of Shepp’s urn model in which it is possible to sample more than one item at each stage. Using a Markov decision process model, we establish monotonicity of the optimal value function and show that the optimal policy is a monotone threshold policy that prescribes either not sampling, or sampling the maximum number of items permitted. A special case exhibits convexity and submodularity, but these properties do not hold in general.     

Optimal two-phase vaccine allocation to geographically different regions under uncertainty

In this article, we consider a decision process in which vaccination is performed in two phases to contain the outbreak of an infectious disease in a set of geographic regions. In the first phase, a limited number of vaccine doses are allocated to each region; in the second phase, additional doses may be allocated to regions in which the epidemic has not been contained. We develop a simulation model to capture the epidemic dynamics in each region for different vaccination levels. We formulate the vaccine allocation problem as a two-stage stochastic linear program (2-SLP) and use the special problem structure to reduce it to a linear program with a similar size to that of the first stage problem. We also present a Newsvendor model formulation of the problem which provides a closed form solution for the optimal allocation. We construct test cases motivated by vaccine planning for seasonal influenza in the state of North Carolina. Using the 2-SLP formulation, we estimate the value of the stochastic solution and the expected value of perfect information. We also propose and test an easy to implement heuristic for vaccine allocation. We show that our proposed two-phase vaccination policy potentially results in a lower attack rate and a considerable saving in vaccine production and administration cost.     

基于粗集复合模糊物元的多产品组合生产目标贴近度研究

在企业实际生产过程中,产品组合生产配置问题是企业制定生产计划时需要解决的首要问题,为了协助企业处理产品组合生产配置问题,提出了一种基于粗集复合模糊物元的多产品组合生产目标贴近度的方法。该方法以产品与产品之间的内在关系为切入点,运用复合模糊物元确定各指标的从优隶属度,构建粗集理论和熵权法对各产品组合配置的生产目标贴近度研究模型,并运用层次分析法对各产品组合生产目标贴近度进行修正。最后,运用该方法对某企业多产品组合配置方案的生产目标贴近度进行了研究分析。研究结果表明,该模型能够有效挖掘产品与产品与产品间的内在关系,以两产品组合配置问题为例,帮组企业确定了两产品组合最优配置方案,并通过与传统产品组合配置选择方法进行比较,说明该模型能够更加有效地实现企业多产品组合生产管理最优目标并提供决策参考。     

A model and approach to the challenge posed by optimal power systems planning

Currently, there is a need to plan and analyze the electric power transmission system in greater detail and over larger geographic areas. Existing models approach the problem from different perspectives. Each model addresses different aspects of and has different approximations to the optimal planning process. In order to scope out the huge challenge of optimal transmission planning, this paper presents a new modeling approach for inter-regional planning and investment in a competitive environment. This modeling approach incorporates the detailed generator, topology and operational aspects found in production cost planning models into a larger framework that can find optimal sets of transmission expansion projects. The framework proposed here can be used in an auction to award investment contracts or as a part of a more general policy analysis. The solution yields the set of transmission projects that have the highest expected benefits, while also representing generic generation expansions under the same objective. The model is a two-stage, mixed-integer, multi-period, N-1-reliable model with investment, unit commitment, and transmission switching. The combination of combinatorial, stochastic and operational elements means this model may be computationally intractable without judicious modelling aggregations or approximations to reduce its size and complexity. Nevertheless we show via a dual problem that analysing the economics and sensitivity of the solution is computationally more straightforward.     

Un modéle d'lnspections optimales

We consider the following model: we inspect the motion of a Markov process with which an “evolution cost” is associated. We inspect the process at times T ₁…, T _n ,…. If when we inspect, its value is in a given set A, it continues its evolution, otherwise we kill it. At each inspection we associate an "inspection cost" and a "killing cost". The problem consists of finding a sequence of optimal inspections. After the modelization we construct the value function by an iterative procedure as in impulse control theory, by using the theory of analytic functions and theorems of section. Thanks to the criteria of optimality we get a sequence of optimal inspections under very general hypotheses.     

A numerical method to approximate optimal production and maintenance plan in a flexible manufacturing system

The simultaneous planning of the production and the maintenance in a flexible manufacturing system is considered in this paper. The manufacturing system is composed of one machine that produces a single product. There is a preventive maintenance plan to reduce the failure rate of the machine. This paper is different from the previous researches in this area in two separate ways. First, the failure rate of the machine is supposed to be a function of its age. Second, we assume that the demand of the manufacturing product is time dependent and its rate depends on the level of advertisement on that product. The objective is to maximize the expected discounted total profit of the firm over an infinite time horizon. In the process of finding a solution to the problem, we first characterize an optimal control by introducing a set of Hamilton–Jacobi–Bellman partial differential equations. Then we realize that under practical assumptions, this set of equations can not be solved analytically. Thus to find a suboptimal control, we approximate the original stochastic optimal control model by a discrete-time deterministic optimal control problem. Then proposing a numerical method to solve the steady state Riccati equation, we approximate a suboptimal solution to the problem.     

Formalizing and solving the problem of clustering in MCDA

The topic of clustering has been widely studied in the field of Data Analysis, where it is defined as an unsupervised process of grouping objects together based on notions of similarity. Clustering in the field of Multi-Criteria Decision Aid (MCDA) has seen a few adaptations of methods from Data Analysis, most of them however using concepts native to that field, such as the notions of similarity and distance measures. As in MCDA we model the preferences of a decision maker over a set of decision alternatives, we can find more diverse ways of comparing them than in Data Analysis. As a result, these alternatives may also be arranged into different potential structures. In this paper we wish to formally define the problem of clustering in MCDA using notions that are native to this field alone, and highlight the different structures which we may try to uncover through this process. Following this we propose a method for finding these structures. As in any clustering problem, finding the optimal result in an exact manner is impractical, and so we propose a stochastic heuristic approach, which we validate through tests on a large set of artificially generated benchmarks.     

饮料罐尺寸的最优解证明及其优化设计

为了发掘节省原料的潜力,对销量很大的饮料易拉罐进行优化设计.建立起具有通用性的优化模型;运用压缩变量技术,解决了模型复杂不易求解的困难;给出了便于实施计算的一整套显式求解公式.通过方向导数分析和流动极值分析,证明了解的最优性,指出了目标函数的退化特征及其临界状态;结合数学理论分析、具体数值计算和实际工艺条件,形成了层层递进式的优化设计途径.