Robust Optimization for Power Systems Capacity Expansion under Uncertainty

We develop a robust optimization model for planning power system capacity expansion in the face of uncertain power demand. The model generates capacity expansion plans that are both solution and model robust. That is, the optimal solution from the model is ‘almost’ optimal for any realization of the demand scenarios (i.e. solution robustness). Furthermore, the optimal solution has reduced excess capacity for any realization of the scenarios (i.e. model robustness). Experience with a characteristic test problem illustrates not only the unavoidable trade-offs between solution and model robustness, but also the effectiveness of the model in controlling the sensitivity of its solution to the uncertain input data. The experiments also illustrate the differences of robust optimization from the classical stochastic programming formulation.     

一类投资项目评估与选择问题的数学模型

章给出在模糊环境下求解带有平衡条件的投资项目评估与选择问题的数学模型，用模糊来描述决策人对项目的主观评价以及多个评价因素的综合，用模糊整数规划模型描述了各种不同门类利益间的平均。     

煤炭矿井设置的多目标选址决策分析

提供了约束条件下煤炭矿井设置的多目标选址方法,并将模糊规划模型转化为0-1规划模型进行求解,通过一个实证分析说明了该方法的实用性和有效性.并就合理有效利用煤炭资源、建设循环型煤炭矿井,为地方煤炭生产管理部门优化煤炭矿井设置提出了相关的政策建议.     

A Multi-Stage Stochastic Integer Programming Approach for Capacity Expansion under Uncertainty

This paper addresses a multi-period investment model for capacity expansion in an uncertain environment. Using a scenario tree approach to model the evolution of uncertain demand and cost parameters, and fixed-charge cost functions to model the economies of scale in expansion costs, we develop a multi-stage stochastic integer programming formulation for the problem. A reformulation of the problem is proposed using variable disaggregation to exploit the lot-sizing substructure of the problem. The reformulation significantly reduces the LP relaxation gap of this large scale integer program. A heuristic scheme is presented to perturb the LP relaxation solutions to produce good quality integer solutions. Finally, we outline a branch and bound algorithm that makes use of the reformulation strategy as a lower bounding scheme, and the heuristic as an upper bounding scheme, to solve the problem to global optimality. Our preliminary computational results indicate that the proposed strategy has significant advantages over straightforward use of commercial solvers.     

B&B Frameworks for the Capacity Expansion of High Speed Telecommunication Networks Under Uncertainty

The purpose of this paper is to investigate branch and bound strategies and the comparison of branch and cut with pure branch and bound approaches on high speed telecommunication network design under uncertainty. We model the problem as a two-stage stochastic program with discrete first-stage (investment) variables. Two formulations of the problem are used. The first one with general integer investment variables and the second one, a variant of the first model, with 0-1 investment variables. We present computational results for three solution approaches: the integer L-shaped (Benders) decomposition, a branch and bound framework and a disjunctive cutting plane method. This work was supported by France Telecom.     

重大基础设施工程管理中的适应性选择原理与策略

根据重大基础设施工程及工程管理复杂性的特点,提出管理主体在工程管理实践中的适应性选择行为基本准则,论述了该准则的基本原理及科学内涵;并从实际操作与执行力出发,设计了该准则的实施策略,为重大工程管理主体应对管理问题复杂性提供了新的方法论。     

模糊环境下带有平衡条件的投资项目评估与选择决策方法

文章提出在模糊环境下求解带有平衡条件的投资项目评估与选择问题的决策方法。该方法由模糊综合评价系统和项目选择的模糊整数规划模型两部分组成。其中模糊综合评价系统采用三角模糊数来描述决策人对项目的主观评价以及多个评价因素的综合，而模糊整数规划模型则描述了各种不同门类利益之间的平衡。最后以实例说明该方法的应用。     

基于局部多项式展开的多元非参数模型贝叶斯带宽选择

在多元非参数模型中带宽和阶的选择对局部多项式估计量的表现十分重要。本文基于交叉验证准则提出一个自适应贝叶斯带宽选择方法。在给定的误差密度函数下,该方法可推导出对应的似然函数,并构造带宽参数的后验密度函数。随后,通过带宽的后验期望可同时获得阶和带宽的估计。数值模拟的结果表明,该方法不仅比大拇指准则方法精确,且比交叉验证方法耗时更少。与此同时,与Nadaraya-Watson估计相比,所提带宽选择方法对多元非参数模型的适应性要更好。最后,本文通过一组实际数据说明有限样本下所提贝叶斯带宽选择的表现很好。     

Optimal Approximation of the Electrostatic Capacity Matrix of Two Conducting Spheres by a Short-Distance Asymptotic Expansion

An integral representation for the electrostatic capacity matrixC=[c_ij]_i,j=1,2 of two conducting spheres of radii R₁, and R₂is obtained. A short-distance asymptotic expansion is then derivedand its approximation properties for fixed (surface) distancer between the spheres are investigated. An error function is defined for c_ij(r) and its nthorder asymptotic approximant it has the property following from the divergence of the expansion, and thereby shows thatthe optimal approximation of c_ij(r) is achieved by an approximantof finite order n = n_ij(r) depending possibly on r and the indicesi,j. The value gives the quality of approximation of c_ij by the asymptotic expansion for a givendistance r between the spheres. The point sets and are introduced in order to describe the distance ranges where c_ij can be approximatedwithin a given error >0 by an asymptotic approximant of given order n, or at least by theoptimal approximant, respectively. The optimal order n_ij(r)and the -approximation sets and D() are investigated numerically.     

PPP项目建造和营运的治理模式选择研究

PPP模式在中国情境下实施、建造和营运环节的治理和协调是一个十分重要的问题。基于交易费用经济学和新产权理论,建立数学模型,对PPP模式的治理展开探讨。首先建立了PPP项目的一般治理模式的数学模型,分析了最优社会福利,然后针对需求风险偿付契约分别建立了PPP项目的建造、营运分立治理模型和建造、营运合一的治理模型。研究表明:在需求风险偿付契约下,建、营合一治理模式比建、营分立的治理模式具有更高的社会福利。     

基于AHP和动量BP神经网络的工程项目承包商选择模型

运用BP神经网络技术,采用动量BP算法,构建了基于动量BP神经网络的工程项目承包商选择模型,并将AHP的评价结果作为学习样本,对BP神经网络模型进行训练和测试.结果表明,基于AHP和动量BP神经网络的工程项目承包商选择模型是可行的,该模型具有较高的自组织、自适应和自学习能力以及较强的容错功能,能够为一般的工程项目承包商选择活动提供有效的参考和依据.     

需求随机下房地产开发商最优进入-退出与能力扩张决策模型

在租赁市场上,房地产开发商常常需要同时决定进入-退出时机及开发能力扩张的的时机.然而这一研究在已往的房地产投资有关文献中有所忽视.鉴于此,在需求随机的条件下,通过一两阶段决策模型同时研究了房地产开发商在租赁市场的进入-退出及能力扩张问题.指出了进入、退出决策的隐式解并给出了扩张决策的阀值及扩张投资额度.研究同时得出结论:不确定性与成本的提高会增大了开发商进入-退出的决策刚性,并同时抑制了开发商的扩张投资.文章同时在行文中分析了结论的经济含义与政策含义.     

基于演化博弈的PPP项目运营期政府监管方式选择研究

PPP项目运营期内,投资者可能为了追求私利而采取投机行为。针对这种投机行为,本文从监管的角度,运用演化博弈理论分析了政府监管方式策略选择问题。本文将监管方式分为两类:过程监管和结果监管。结果表明,政府监管部门的策略选择与监管部门结果监管时能够识别投机行为的概率有关。以投资者投机所得的“价值”为标准,当结果监管识别投机行为的概率小于投资者采取机会主义所得的“价值”时,双方最终的策略选择为(不采取机会主义行为,结果监管);相反,则双方之间不存在一组进化稳定策略,博弈的最终结果与双方的收益以及系统所处的初始状态有关。此外,本文还讨论了不同参数变化对双方行为策略选择的影响,为政府监管方式的选择提供参考,以期最大程度抑制机会主义行为。