基于决策者风险偏好的模糊鲁棒性项目调度优化

不确定条件下模糊鲁棒性项目调度计划的生成受决策者风险偏好影响。本文研究模糊活动工期下考虑决策者风险偏好的鲁棒性项目调度优化问题，目标是合理安排活动开始时间，生成特定风险偏好下鲁棒性最大的进度计划。首先界定问题，构建优化模型；随后针对问题NP-hard属性和模型特点设计交替禁忌搜索启发式算法，求解得到不同风险偏好下满意的进度计划；最后用实例验证说明，并分析关键参数影响。结论如下：决策者风险偏好由规避转乐观时，项目冲突区间总和增多；截止日期、资源可用量较紧张时，风险偏好变化对冲突区间总和变化影响更大；风险偏好乐观时，截止日期变化对冲突区间总和变化影响更大。研究成果可为不同风险偏好决策者在不具历史数据的高不确定环境中制定合理前摄性计划提供决策支持。     

不同风险态度下的三层供应链集成优化

研究了在决策者不同风险态度下的由供应商、分销商和零售商组成三层供应链集成优化问题。考虑供应链中价格、质量、交货、需求和供应的不确定风险因素为模糊变量,应用可信性理论建立了模糊机会约束规划模型,可信性测度的置信水平代表了决策者的风险偏好程度。使用模糊变量的乐观值和悲观值将机会约束转化为清晰的等价形式。最后,通过数值算例分析了决策者的风险态度的变化对集成供应链的影响。     

基于风险态度的两级供应链设计与优化

决策者的风险态度将对供应链系统性能产生重要影响。供应链中考虑了供应商的产品价格、风险等级、供应能力和零售商需求为不确定性风险因素,研究了基于决策者风险态度的两级供应链设计与优化问题。建立了基于可信性测度的模糊机会约束规划模型,分析了风险态度的变化对供应链系统结构和目标的影响。     

考虑决策者风险偏好的区间直觉模糊多属性群决策方法

提出了一种考虑决策者风险偏好且属性权重信息不完全的区间直觉模糊数多属性群决策方法。同时考虑相似度和接近度,确定每一属性的决策者权重。为了考虑决策者风险偏好对决策结果的影响和避免区间直觉模糊矩阵的渐进性,引入了决策者风险偏好系数,将集结后的综合决策矩阵转换成区间数矩阵。然后,为了客观地求出属性权重信息不完全环境下属性的权重,构建了基于区间直觉模糊交叉熵的属性权重目标规划模型,该模型不仅考虑了评价值的偏差,也强调了评价值自身的可信度。最后,通过研发项目选择问题的实例分析说明了所提方法的合理性和优越性。     

直觉模糊数风险偏好一致排序方法和决策

证明直觉模糊数的Hong排序法、刘华文排序法和陈东峰排序法都要求决策者的风险态度随直觉模糊数变化而变化,不满足风险偏好一致性,违背决策者的风险态度相对稳定的实际情况.提出基于风险偏好系数的直觉模糊数排序方法,它能保证决策者风险偏好一致;并且,面对相同的决策问题,不同风险偏好的决策者可能有不同的决策结果.最后,把基于风险偏好系数的直觉模糊数排序法应用于直觉模糊集多属性决策.     

含模糊变量的水污染控制系统研究

在水流量为模糊变量且河流中工业污水含量标准给定的条件下,分别建立了水污染控制系统问题的模糊期望值模型和模糊机会约束规划模型来满足不同的优化需求.为了有效求解优化模型,采用了将模糊模拟、神经元网络及遗传算法相结合的混合智能算法.最后用算例进行了验证,结果表明该算法是有效可行的.     

基于不同风险偏好的退货可再销报童模型研究

考虑退货可再销售的情况下,应用条件风险价值(CVaR),构建纳入不同风险偏好的报童模型,并给出了不同环境下的最优订货量.通过解析和数值仿真,进一步分析了决策者的风险偏好水平、产品退货率以及残值对最优订货量决策的影响.研究结果表明:最优订货量随着决策者风险追逐偏好水平的增加而增加,随着风险规避偏好水平的增加而减小;最优订货量随着退货率的增加而减小,随着产品残值的增加而增加.结合订货决策的实际特点,给出了不同环境下的最优订货量,为决策者提供了全面且动态的决策建议.     

基于条件风险值准则的供应链回购契约协调策略

研究了由具有风险偏好的零售商和风险中性的供应商组成的两级供应链回购契约协调问题.针对具有风险偏好的零售商,考虑了风险中性、风险厌恶和风险喜好三种态度,建立了由风险厌恶程度和悲观系数两个参数描述的基于条件风险值(CVaR)的集成目标决策函数.推导了不同风险偏好态度下的零售商最优订货决策,分析了不同风险偏好参数下的零售商订货决策变化情况.给出了能够完全协调风险偏好零售商和风险中性供应商的供应链回购契约协调机制.最后,进行了数值计算,验证了设计的供应链回购契约协调策略的有效性.结果表明,在给出的回购契约协调机制下,考虑风险偏好情况下的零售商最优订货决策能够保证整个供应链系统实现最优绩效,而供应链成员期望利润却随不同的风险偏好参数而不同.     

基于决策者风险偏好大数据分析的大群体应急决策方法

针对重大突发事件应急决策大群体成员的风险偏好复杂难测问题,提出了一种新的基于决策者风险偏好大数据分析的大群体应急决策方法。首先专家群体对突发事件进行快速响应,生成若干应急预案及其风险属性信息;其次,社会公众通过网络等渠道参与到应急决策中来并形成决策大群体,给出不同预案的偏好值;然后,利用证据推理算法得出公众对各预案的风险效用值,将预案风险效用值与预案偏好值加权组合,得到各个预案的大群体决策者的风险偏好值;最后,基于风险偏好值,利用大数据分析技术对大群体的风险偏好进行聚类识别,从中筛选出风险中立者组成新的应急决策群体,再次聚类得出应急决策群体的成员组成结构,以此为基础计算决策者权重和应急预案的最终效用值,得应急预案排序结果。最后通过算例分析验证了方法的有效性和可行性。     

基于决策者风险偏好特性的供应链竞合策略研究

在两条供应链相互竞争的背景下,采用均值—标准差风险度量准则研究了决策者的风险偏好特性(风险喜爱、风险中性或风险规避)对于供应链各成员企业的最优定价决策和竞合策略的影响。研究发现,供应链各成员企业的最优定价决策与决策者的风险偏好特性以及产品替代效应密切相关,而且供应链成员企业的风险偏好特性以及产品替代效应都是影响其合作策略选择的关键因素。此外,当供应链成员企业采用不同的竞合策略时,供应链系统的效用并不一定会随其成员企业的风险偏好度的减小而减小。最后,通过数值分析也证实了上述结论。     

Risk management strategies via minimax portfolio optimization

Recent extreme economic developments nearing a worst-case scenario motivate further examination of minimax linear programming approaches for portfolio optimization. Risk measured as the worst-case return is employed and a portfolio from maximizing returns subject to a risk threshold is constructed. Minimax model properties are developed and parametric analysis of the risk threshold connects this model to expected value along a continuum, revealing an efficient frontier segmenting investors by risk preference. Divergence of minimax model results from expected value is quantified and a set of possible prior distributions expressing a degree of Knightian uncertainty corresponding to risk preference determined. The minimax model will maximize return with respect to one of these prior distributions providing valuable insight regarding an investor’s risk attitude and decision behavior. Linear programming models for financial firms to assist individual investors to hedge against losses by buying insurance and a model for designing variable annuities are proposed.     

Heston随机波动率市场中带VaR约束的最优投资策略

本文研究了Heston随机波动率市场下, 基于VaR约束下的动态最优投资组合问题。
假设Heston随机波动率市场由一个无风险资产和一个风险资产构成,投资者的目标为最大化其终端的期望效用。与此同时, 投资者将动态地评估其待选的投资组合的VaR风险,并将其控制在一个可接受的范围之内。本文在合理的假设下,使用动态规划的方法,来求解该问题的最优投资策略。在特定的参数范围内,利用数值方法计算出近似的最优投资策略和相应值函数, 并对结果进行了分析。     

Stochastic chance constrained mixed-integer nonlinear programming models and the solution approaches for refinery short-term crude oil scheduling problem

Stochastic chance constrained mixed-integer nonlinear programming (SCC-MINLP) models are developed in this paper to solve the refinery short-term crude oil scheduling problem which concerns crude oil unloading, mixing, transferring and multilevel inventory control under demands uncertainty of distillation units. The objective of these models is the minimum expected value of total operation cost. It is the first time that the uncertain demands of Crude oil Distillation Units (CDUs) in these problems are set as random variables which have discrete and continuous joint probability distributions. This situation is close to the real world industry use. To reduce the computation complexity, these SCC-MINLP models are transformed into their equivalent stochastic chance constrained mixed-integer linear programming models (SCC-MILP). Stochastic simulation and stochastic sampling technologies are introduced in detail to solve these complex SCC-MILP models. Finally, case studies are effectively solved with the proposed approaches.     

A hybrid differential evolution algorithm to vehicle routing problem with fuzzy demands

In this paper, the vehicle routing problem with fuzzy demands (VRPFD) is considered, and a fuzzy chance constrained program model is designed, based on fuzzy credibility theory. Then stochastic simulation and differential evolution algorithm are integrated to design a hybrid intelligent algorithm to solve the fuzzy chance constrained program model. Moreover, the influence of the dispatcher preference index on the final objective of the problem is discussed using stochastic simulation, and the best value of the dispatcher preference index is obtained.     

Using greedy clustering method to solve capacitated location-routing problem with fuzzy demands

In this paper, the capacitated location-routing problem with fuzzy demands (CLRP-FD) is considered. In CLRP-FD, facility location problem (FLP) and vehicle routing problem (VRP) are observed simultaneously. Indeed, the vehicles and the depots have a predefined capacity to serve the customers that have fuzzy demands. To model this problem, a fuzzy chance constrained programming model of that is designed based upon the fuzzy credibility theory. To solve this problem, a greedy clustering method (GCM) including the stochastic simulation is proposed. To obtain the best value of the dispatcher preference index of the model and to analyze its influence on the final solution, numerical experiments are carried out. Finally, to show the performance of the greedy clustering method, associated results are compared with the lower bound of the solutions.     

An approach to the valuation and decision of ERP investment projects based on real options

The risks and uncertainties inherent in most enterprise resources planning (ERP) investment projects are vast. Decision making in multistage ERP projects investment is also complex, due mainly to the uncertainties involved and the various managerial and/or physical constraints to be enforced. This paper tackles the problem using a real-option analysis framework, and applies multistage stochastic integer programming in formulating an analytical model whose solution will yield optimum or near-optimum investment decisions for ERP projects. Traditionally, such decision problems were tackled using lattice simulation or finite difference methods to compute the value of simple real options. However, these approaches are incapable of dealing with the more complex compound real options, and their use is thus limited to simple real-option analysis. Multistage stochastic integer programming is particularly suitable for sequential decision making under uncertainty, and is used in this paper and to find near-optimal strategies for complex decision problems. Compared with the traditional approaches, multistage stochastic integer programming is a much more powerful tool in evaluating such compound real options. This paper describes the proposed real-option analysis model and uses an example case study to demonstrate the effectiveness of the proposed approach.     

基于动态VaR约束与随机波动率模型的最优投资策略

研究Stein-Stein随机波动率模型下带动态VaR约束的最优投资组合选择问题. 假设投资者的目标是最大化终端财富的期望幂效用,可投资于无风险资产和一种风险资产, 风险资产的价格过程由Stein-Stein随机波动率模型刻画. 同时, 投资者期望能在投资过程中利用动态VaR约束控制所面对的风险.运用Bellman动态规划方法和Lagrange乘子法, 得到了该约束问题最优策略的解析式及特殊情形下最优值函数的解析式; 并通过理论分析和数值算例, 阐述了动态VaR约束与随机波动率对最优投资策略的影响.     

带有随机因素的逆DEA模型

本文讨论含有随机因素的逆 DEA模型 .逆 DEA模型解决的问题是 :对于某个决策单元 (DMU ) ,若增加其输入 ,在保持相对效率水平不变的情况下 ,估计 (预测 )输出应增加多少 .因此逆 DEA模型可用于短期预测问题 .带有随机因素的逆 DEA模型 ,是将该问题转化成机会约束的多目标规划问题 ,在某些特殊情况下 ,成为机会约束的线性规划问题 .     

Relaxations and approximations of chance constraints under finite distributions

Optimization problems with constraints involving stochastic parameters that are required to be satisfied with a prespecified probability threshold arise in numerous applications. Such chance constrained optimization problems involve the dual challenges of stochasticity and nonconvexity. In the setting of a finite distribution of the stochastic parameters, an optimization problem with linear chance constraints can be formulated as a mixed integer linear program (MILP). The natural MILP formulation has a weak relaxation bound and is quite difficult to solve. In this paper, we review some recent results on improving the relaxation bounds and constructing approximate solutions for MILP formulations of chance constraints. We also discuss a recently introduced bicriteria approximation algorithm for covering type chance constrained problems. This algorithm uses a relaxation to construct a solution whose (constraint violation) risk level may be larger than the pre-specified threshold, but is within a constant factor of it, and whose objective value is also within a constant factor of the true optimal value. Finally, we present some new results that improve on the bicriteria approximation factors in the finite scenario setting and shed light on the effect of strong relaxations on the approximation ratios.