一种多随从双层条件风险值模型的等价性定理

多随从风险决策问题是供应链风险决策中普遍存在的问题,文章研究了风险厌恶下的多随从双层条件风险值模型,引入了多随从上下层决策的VaR损失值(最小风险值)和CVaR损失值(最小风险值对应的条件期望损失值或条件风险价值度量)概念,提出了一种风险厌恶下的多随从双层条件风险值模型,该模型的目标是求上下层的基于权值的多损失CVaR达最小的最优解,文章证明了它可以通过另一个较容易求解的双层规划模型获得最优解的等价性定理.     

基于CVaR准则的折扣回购策略双层风险决策

首先,引入条件风险值(CVaR)准则,作为风险厌恶型的供应商和零售商的决策准则,建立了基于条件风险值(CVaR)准则的折扣回购策略双层风险决策模型.然后,导出了零售商在批发价格下的最优订购公式,证明了订购量随着折扣增大而增大,随着批发价格增大而减小,数值实验表明供应商可以通过折扣和批发价来分担零售商的风险损失,来使供应链达到协调.     

机会损失最小化报童模型中的订购决策研究

结合条件风险值(conditional value-at-risk,CVaR)准则对机会损失最小化报童模型中零售商的订购决策进行研究.研究结果表明:当订购过量损失大于订购不足损失时,零售商基于CVaR机会损失最小化的订购量小于期望机会损失最小化的订购量,且随着零售商对风险厌恶程度的增加而减少;反之,当订购过量损失小于订购不足损失时,零售商基于CVaR机会损失最小化的订购量大于期望机会损失最小化的订购量,且随着零售商对风险厌恶程度的增加而增加;随着零售商对风险规避程度的增加,零售商基于CVaR机会损失最小化的订购量所对应的期望利润和期望机会损失分别减少和增加,即低风险意味着低收益,高收益伴随着高风险.     

供应链金融三方风险控制的CVaR决策分析——基于预付账款融资模式

研究基于预付账款融资模式下的供应链金融三方决策问题,采用CVaR的风险度量准则作为决策标准,建立了随机需求下的单个风险规避零售商、单个风险规避制造商及单个风险规避银行组成的供应链金融风险模型.在权衡收益和风险的基础上,得出零售商的最优订购量、制造商的最优批发价格及银行的最优利率.研究以零售商是否存在违约为前提,通过对三方收益的分析,在考虑风险规避水平的同时,求解出最优订购量、批发价格和利率,并研究它们之间的关系.最后,通过Matlab数值仿真验证预付账款模式下CVaR模型的合理性.     

具有风险厌恶零售商的供应链合作博弈分析

论文考虑由一个制造商与一个零售商组成的两层供应链,零售商以条件在险价值(CVaR)作为其风险衡量,制造商为风险中性,制造商与零售商通过讨价还价机制决定批发价格以及订货量。研究结果表明,在零售商为风险厌恶的决策者而制造商为风险中性的决策者时,供应链成同的合作博弈存在均衡的批发价格以及订货量。研究还发现零售商对供应链利润的讨价还价能力随着其风险厌恶程度的增大而增加。     

基于混合CVaR的供应链回购策略优化与协调研究

研究了风险中性供应商与混合CVaR约束零售商构成的两级供应链模型中回购契约协调问题.混合CVaR是由最小化CVaR和最大化CVaR通过加权平均的方式得到的,它包括风险规避,风险中性和风险追求三种特殊情形.引入一个刻画决策者风险态度的"风险偏好系数",证明当风险偏好系数大于1时混合CVaR与前景理论中的损失规避均能刻画决策者对损失的敏感性高于对收益的敏感性.得到零售商最优订货量和最优利润关于风险偏好系数的单调性;证明无论风险偏好系数大于等于1或小于1,回购契约都能实现供应链协调,并推导出实现系统协调时最优契约参数之间的关系.最后结合数值例子验证了供应链回购契约机制的有效性.     

含MCVaR的投资组合优化统一模型

研究了Duarte提出的投资组合优化统一模型及条件风险价值(CVaR),分析了以CVaR为风险度量的投资组合优化模型的具体形式,建立了统一七种模型的投资组合优化统一模型,并发现统一模型是一个凸二次规划问题.     

基于回购契约的风险厌恶零售商的供应链协调

研究了由风险中性的供应商和风险厌恶的零售商组成的二级供应链协调问题.零售商的风险厌恶由CVaR来度量,研究表明：零售商的风险厌恶加剧了双重边际效应,恶化了供应链效益.为了实现供应链的协调,供应商提出回购契约以减轻零售商的风险顾虑引导其增加订货量,结果表明：当零售商的风险厌恶超过了一定的程度,回购契约不能实现供应链协调;当供应链可以通过回购契约实现协调时,供应链的协调利益可以在供应商和零售商之间进行任意的分配,具体的分配结果取决于他们的讨价还价能力.     

基于CVaR风险测度标准的价格补贴策略下的协调研究

基于条件风险值模型(CVaR),探讨了在一个风险中性制造商和一个风险规避零售商组成的制造商领导的斯塔克伯格博弈供应链中,制造商如何与风险规避零售商订立批发价契约以最大化其期望利润的问题。设计了价格补贴的契约协调机制,给出了该机制下风险规避程度对零售商和制造商最优决策的影响。证明了在一定的实施条件下,制造商通过设立价格补贴机制,可改善供应链双方利润与供应链效率。最后,用算例验证了模型和理论分析的可行性。     

基于外部融资的风险厌恶零售商的订货策略研究

选取资金不足风险厌恶的零售商为研究对象,在零售商选取银行贷款的融资模式下假定市场需求是随机的,应用CVaR风险准则计算零售商的最优订货量,并与风险中性时零售商的最优订货量进行对比,得出在银行贷款融资方式下,风险厌恶的零售商的订货量小于风险中性的最优订货量,并进一步分析出现这种结果的原因.对全文得到的结论进行总结,指出文中得到的理念结果可为供应链企业提供决策的参考依据,并进一步指出未来的研究方向.     

一类非线性双层规划问题的最优性条件

双层规划模型是描述具有层次特性管理决策系统的有效方法.本文讨论了一类有广泛代表性的非线性双层规划模型,给出了该类模型最优解的条件.     

Robust portfolio optimization with copulas

Conditional Value at Risk (CVaR) is widely used in portfolio optimization as a measure of risk. CVaR is clearly dependent on the underlying probability distribution of the portfolio. We show how copulas can be introduced to any problem that involves distributions and how they can provide solutions for the modeling of the portfolio. We use this to provide the copula formulation of the CVaR of a portfolio. Given the critical dependence of CVaR on the underlying distribution, we use a robust framework to extend our approach to Worst Case CVaR (WCVaR). WCVaR is achieved through the use of rival copulas. These rival copulas have the advantage of exploiting a variety of dependence structures, symmetric and not. We compare our model against two other models, Gaussian CVaR and Worst Case Markowitz. Our empirical analysis shows that WCVaR can asses the risk more adequately than the two competitive models during periods of crisis.     

On solving the dual for portfolio selection by optimizing Conditional Value at Risk

This note is focused on computational efficiency of the portfolio selection models based on the Conditional Value at Risk (CVaR) risk measure. The CVaR measure represents the mean shortfall at a specified confidence level and its optimization may be expressed with a Linear Programming (LP) model. The corresponding portfolio selection models can be solved with general purpose LP solvers. However, in the case of more advanced simulation models employed for scenario generation one may get several thousands of scenarios. This may lead to the LP model with huge number of variables and constraints thus decreasing the computational efficiency of the model. To overcome this difficulty some alternative solution approaches are explored employing cutting planes or nondifferential optimization techniques among others. Without questioning importance and quality of the introduced methods we demonstrate much better performances of the simplex method when applied to appropriately rebuilt CVaR models taking advantages of the LP duality.     

二(双)层规划综述

二(双)层规划是研究二层决策的递阶优化问题.其理论、方法和应用在过去的30多年取得了很大的发展.本文对二层规划问题的基本概念、性质和算法作了综述,并且对下层规划问题的解不唯一的情况也作了介绍,最后还给出了几种常见的二层规划模型.     

Necessary optimality conditions for a special class of bilevel programming problems with unique lower level solution

We consider a bilevel programming problem in Banach spaces whose lower level solution is unique for any choice of the upper level variable. A condition is presented which ensures that the lower level solution mapping is directionally differentiable, and a formula is constructed which can be used to compute this directional derivative. Afterwards, we apply these results in order to obtain first-order necessary optimality conditions for the bilevel programming problem. It is shown that these optimality conditions imply that a certain mathematical program with complementarity constraints in Banach spaces has the optimal solution zero. We state the weak and strong stationarity conditions of this problem as well as corresponding constraint qualifications in order to derive applicable necessary optimality conditions for the original bilevel programming problem. Finally, we use the theory to state new necessary optimality conditions for certain classes of semidefinite bilevel programming problems and present an example in terms of bilevel optimal control.     

An interval approach based on expectation optimization for fuzzy random bilevel linear programming problems

This paper considers a class of bilevel linear programming problems in which the coefficients of both objective functions are fuzzy random variables. The main idea of this paper is to introduce the Pareto optimal solution in a multi-objective bilevel programming problem as a solution for a fuzzy random bilevel programming problem. To this end, a stochastic interval bilevel linear programming problem is first introduced in terms of α-cuts of fuzzy random variables. On the basis of an order relation of interval numbers and the expectation optimization model, the stochastic interval bilevel linear programming problem can be transformed into a multi-objective bilevel programming problem which is solved by means of weighted linear combination technique. In order to compare different optimal solutions depending on different cuts, two criterions are given to provide the preferable optimal solutions for the upper and lower level decision makers respectively. Finally, a production planning problem is given to demonstrate the feasibility of the proposed approach.     

Two-Stage Approach for Quantitative Policy Analysis Using Bilevel Programming

Quantitative policy analysis problems with hierarchical decision-making can be modeled as bilevel mathematical programming problems. In general, the solution of these models is very difficult; however, special cases exist in which an optimal solution can be obtained by ordinary mathematical programming techniques. In this paper, a two-stage approach for the formulation, construction, solution, and usage of bilevel policy problem is presented. An outline of an example for analyzing Israel's public expenditure policy is also given.     

二层供应链网络均衡模型的研究

利用均衡理论和二层规划理论来研究供应链网络均衡问题。针对供应链网络中上下层成员之间具有的Stackelberg博弈特征以及同层成员之间具有的非合作博弈特征,构建了二层供应链网络的均衡模型,该模型实际上一个均衡约束的二层规划问题。此外,为了使得供应链网络在整体上实现最优,本文还在模型中引入回收契约以协调供应链网络。最后,利用罚函数法对模型进行了求解,算例分析说明了模型的合理性和有效性。     

A smoothing method for solving portfolio optimization with CVaR and applications in allocation of generation asset

This paper focuses on the computation issue of portfolio optimization with scenario-based CVaR. According to the semismoothness of the studied models, a smoothing technology is considered, and a smoothing SQP algorithm then is presented. The global convergence of the algorithm is established. Numerical examples arising from the allocation of generation assets in power markets are done. The computation efficiency between the proposed method and the linear programming (LP) method is compared. Numerical results show that the performance of the new approach is very good. The remarkable characteristic of the new method is threefold. First, the dimension of smoothing models for portfolio optimization with scenario-based CVaR is low and is independent of the number of samples. Second, the smoothing models retain the convexity of original portfolio optimization problems. Third, the complicated smoothing model that maximizes the profit under the CVaR constraint can be reduced to an ordinary optimization model equivalently. All of these show the advantage of the new method to improve the computation efficiency for solving portfolio optimization problems with CVaR measure.     

Designing robust coverage networks to hedge against worst-case facility losses

In order to design a coverage-type service network that is robust to the worst instances of long-term facility loss, we develop a facility location–interdiction model that maximizes a combination of initial coverage by p facilities and the minimum coverage level following the loss of the most critical r facilities. The problem is formulated both as a mixed-integer program and as a bilevel mixed-integer program. To solve the bilevel program optimally, a decomposition algorithm is presented, whereby the original bilevel program is decoupled into an upper level master problem and a lower level subproblem. After sequentially solving these problems, supervalid inequalities can be generated and appended to the upper level master in an attempt to force it away from clearly dominated solutions. Computational results show that when solved to optimality, the bilevel decomposition algorithm is up to several orders of magnitude faster than performing branch and bound on the mixed-integer program.