Investment under ambiguity with the best and worst in mind

Recent literature on optimal investment has stressed the difference between the impact of risk and the impact of ambiguity—also called Knightian uncertainty—on investors’ decisions. In this paper, we show that a decision maker’s attitude towards ambiguity is similarly crucial for investment decisions. We capture the investor’s individual ambiguity attitude by applying α-MEU preferences to a standard investment problem. We show that the presence of ambiguity often leads to an increase in the subjective project value, and entrepreneurs are more eager to invest. Thereby, our investment model helps to explain differences in investment behavior in situations which are objectively identical.     

Designing an optimal resource recovery facility

With the ever expanding consuming societies all over the world, communities are producing increasing amounts of solid waste. A proper disposal of the waste has always been considered a matter of public interest and a basic service to the residents. Since this service requires a vast amount of economic resources and land sites, which are becoming scarce, many communities, particularly in the United States. are facing a difficult problem of how to continue to provide a cost-effective solid waste disposal service to their residents.A search for suitable land for siting a landfill is both difficult and costly. Further, the sensitivity in recent years to the quality of environment has measurably heightened the cost of developing and operating a landfill site. Consequently, more and more communities have been studying alternative disposal programs to alleviate the problem. One such system, which has recently come into focus, is the resource recovery alternative. The combustion of solid waste in specially designed incinerators and simultaneous recovery of the heat energy are the underlying principles this system is built on.This paper suggests a model for optimizing the size and configuration of resource recovery facility to suit the present and anticipated disposal needs of a community. The model discussed requires a minimal computational effort.     

Portfolio Optimization with Stochastic Volatilities and Constraints: An Application in High Dimension

In this paper we are interested in an investment problem with stochastic volatilities and portfolio constraints on amounts. We model the risky assets by jump diffusion processes and we consider an exponential utility function. The objective is to maximize the expected utility from the investor terminal wealth. The value function is known to be a viscosity solution of an integro-differential Hamilton-Jacobi-Bellman (HJB in short) equation which could not be solved when the risky assets number exceeds three. Thanks to an exponential transformation, we reduce the nonlinearity of the HJB equation to a semilinear equation. We prove the existence of a smooth solution to the latter equation and we state a verification theorem which relates this solution to the value function. We present an example that shows the importance of this reduction for numerical study of the optimal portfolio. We then compute the optimal strategy of investment by solving the associated optimization problem.     

The optimal resource portfolio under consumer choice and demand risk for vertically differentiated products

We study the optimal resource portfolio of a firm that sells two vertically differentiated products and utilizes resource flexibility and responsive pricing. We model this decision problem as a two-stage stochastic programming problem with recourse: In the first stage, the firm determines its resource mix and capacities so as to maximize the expected profit under demand uncertainty; in the second stage, uncertainty is resolved and the firm determines its production and pricing decision, constrained by its investment decision. We show that the objective function of this decision problem is not well-behaved (ie, it may have multiple local maxima). Using the concept of Pareto dominance, we reduce the feasible investment region, without loss of optimality, to one in which the objective function is well-behaved everywhere. This reduction allows us to derive the necessary and sufficient conditions for the optimal capacity decision and to gain insights.     

Planning federal public investment in Mexico using multiobjective decision making

The scope of this article is showing how multicriteria decision making can be anefficient tool to manage public investment planning in complex situations. Forthis aim, we will analyse the problem in all its aspects: building the modelfrom data using econometrical tools, solving the resulting highly complex modelusing modern efficient techniques (multiobjective meta-heuristics) and helpingthe decision maker to introduce his preferences in order to achieve the mostpreferred solution. This holistic approach let us provide an efficient solutionto a complex public investment planning situation, improving the current stateof the country relating not only economical aspects, but also social and humandevelopment aspects. The real situation studied is focused on Mexico, where, inrecent decades, has undergone remarkable improvements in terms of economicgrowth, which has not been matched by significant improvements in several otherbasic aspects of human development, nor by reductions in regional inequalities.This suggests the need to establish policies aimed at improving these aspectsand reducing inequalities. Federal public investment is an important tool inregional policy to promote and improve these aspects; so we introduce amultiobjective programming problem for planning federal public investment inMexico. This model will focus on improving national levels in four maindimensions of human development (economic growth, education, health andhousing), and on reducing regional inequalities for those dimensions.     

A hybrid approach combining interior-point and branch-and-bound methods applied to the problem of sugar cane waste

This paper proposes a hybrid approach for solving the multi-objective model related to the minimisation of sugar cane waste collection costs and/or the maximisation of produced energy by this waste, with the aid of strategies for solving multi-objective problems, which transform the problem into a set of single-objective problems. This approach combines the predictor-corrector primal-dual interior-point and branch-and-bound methods in order to solve these single-objective problems. The model consists in identifying the sugar cane varieties with the lowest waste collection costs, while simultaneously it aims to obtain the greatest amount of produced energy by this waste. The hybrid methods are implemented in C++ programming language, and tests are performed to determine the efficient solutions in Pareto optimal sense of the multi-objective model and compare the performance of the hybrid method using the integrality test and without considering it. The mathematical results confirm that the proposed hybrid method for solving the aforementioned models presents good computational performance and reliable solutions.     

Optimal portfolio choice for an insurer with loss aversion

The problem of optimal investment for an insurance company attracts more attention in recent years. In general, the investment decision maker of the insurance company is assumed to be rational and risk averse. This is inconsistent with non fully rational decision-making way in the real world. In this paper we investigate an optimal portfolio selection problem for the insurer. The investment decision maker is assumed to be loss averse. The surplus process of the insurer is modeled by a Lévy process. The insurer aims to maximize the expected utility when terminal wealth exceeds his aspiration level. With the help of martingale method, we translate the dynamic maximization problem into an equivalent static optimization problem. By solving the static optimization problem, we derive explicit expressions of the optimal portfolio and the optimal wealth process.     

电商平台主导的E-闭环供应链定价、服务决策与回收模式

E-闭环供应链(E-CLSC)管理须有科学的定价与服务决策支撑。针对集中和分散回收模式,构建电商平台主导的Stackelberg博弈模型,研究E-CLSC定价与平台服务决策。通过对产品销售价格、平台服务水平等均衡策略分析,揭示回收主体投资有效性、回收转移价格等对E-CLSC均衡策略影响。研究表明:集中回收模式优于分散回收模式;在分散回收模式下,若回收主体投资有效性相同,制造商、平台均偏好制造商回收模式;平台回收与第三方回收模式相比,产品销售价格、平台服务水平相同,前者回收渠道效率较高;平台回收模式下,单位佣金与回收转移价格负相关,产品销售价格、平台服务水平、废旧产品回收率均与回收转移价格无关;若回收主体投资有效性差异程度较大,制造商回收模式并非总是最优的,回收主体投资有效性差异显著影响产品销售价格、回收渠道效率、平台服务水平和E-CLSC各成员利润。上述结论通过数值仿真进行了验证。     

房地产风险投资的多目标决策分析和应用

房地产风险投资决策是复杂的多目标决策问题 ,本文介绍采用模糊迭代方法对决策各参数指标数值进行处理 ,求出各指标值权重相应优属度数值 ,完成风险投资方案的排序择优 ,并结合应用介绍该模型的评价方法 .     

基于CAR-DEA方法的环境效率评价研究

现有环境效率评价的DEA方法没有考虑多维偏好约束问题,即不同决策单元对不同期望产出和不期望产出的偏好不同. 以地区为例,不同地区对GDP、废水和废气赋予的权重偏好各不相同. 在这种情况下,由于各决策单元的偏好约束不同,形成多维偏好约束集,在传统DEA模型中容易出现无可行解现象. 针对这一问题,基于CAR-DEA方法,结合保证域理论,提出一种解决多维偏好约束集问题的环境效率评价模型. 采用中国工业系统的环境效率评价实例对提出的方法进行了分析和说明.     

A generalized geometric-programming solution to “An economic production quantity model with flexibility and reliability considerations”

The classical economic production quantity (EPQ) model assumes that items are produced by a perfectly reliable production process with a fixed set-up cost. While the reliability of the production process cannot be perfected cost-free, the set-up cost can be reduced by investment in flexibility improvement. In this paper, we propose an EPQ model with a flexible and imperfect production process. We formulate this inventory decision problem using geometric programming (GP), establish more general results using the arithmetic-geometric mean inequality, and solve the problem to obtain a closed-form optimal solution. Following the theoretical treatment, we provide a numerical example to demonstrate that GP has potential as a valuable analytical tool for studying a certain class of inventory control problems. Finally we discuss some aspects of sensitivity analysis of the optimal solution based on the GP approach.     

非常规油气资源开发投资决策优化模型研究

非常规油气资源作为最现实的可替代能源,对其进行勘探和开发对于降低日益加大的石油供需矛盾缺口和确保国家能源安全均具有重要的战略意义。然而,非常规油气资源勘探开发十分复杂,开发投资决策好坏已经成为制约其能否实现规模化和产业化的关键问题,科学投资决策问题已逐步成为石油企业高层管理者的主要职责。针对非常规油气资源开发投资的多阶段多目标决策优化难题,以可供开发区块的资源分配为重点研究对象,从解决不同区块投资规模入手,运用多阶段决策、多目标决策和不确定多属性方案优选的方法理论,通过剖析非常规油气开发投资决策过程及其复杂性特征,将开发投资决策过程进行形式化描述并在计算机中加以实现,从而得以实现开发投资决策方案的动态性调整。本项研究不仅有助于深化多目标动态优化决策理论的研究,还为解决非常规油气资源开发投资决策难题提供一种新的思路和方法。     

股份制公司的综合投资方案决策模型

对股份制公司的综合投资方案的决策问题进行了研究.首先依据多个投资方案的风险与收益并存的实际情况,建立了最佳投资组合方案的多目标决策模型.然后,由董事会综合各股东所持股份和相互评价权值,利用群决策的方法得到一个最终投资方案,此方案在理论上能使公司获得最大收益.     

广义均值保持变换的随机单调性及其在库存系统中的应用

本文提出一类非线性且均值可能不等的广义均值保持变换,研究实现其变换前后随机变量比较的充分条件或充分必要条件,并用此变换来定量刻画需求不确定性对库存系统决策和利润的影响。首先给出变换前后或不同参数下分布函数的关系及其满足一阶随机占优和割准则序的充分条件,特征刻画此变换与广义TTT变换之间的关系。进一步,用三类特殊的广义均值保持变换进行验证。最后,将此变换应用到报童模型中,得出该变换对包含最小化成本及最大化利润的一致化报童问题的随机单调性。     

Climate change and optimal energy technology R&D policy

Public policy response to global climate change presents a classic problem of decision making under uncertainty. Theoretical work has shown that explicitly accounting for uncertainty and learning in climate change can have a large impact on optimal policy, especially technology policy. However, theory also shows that the specific impacts of uncertainty are ambiguous. In this paper, we provide a framework that combines economics and decision analysis to implement probabilistic data on energy technology research and development (R&D) policy in response to global climate change. We find that, given a budget constraint, the composition of the optimal R&D portfolio is highly diversified and robust to risk in climate damages. The overall optimal investment into technical change, however, does depend (in a non-monotonic way) on the risk in climate damages. Finally, we show that in order to properly value R&D, abatement must be included as a recourse decision.     

基于博弈论的节能量保证型EPC合同决策分析

合同能源管理(EPC)是一种以未来节约的能源费用支付节能项目成本的节能管理机制。节能量保证型EPC模式中,耗能企业负责为项目融资,节能服务公司提供项目的全程服务并向客户企业保证一定的节能效益。若达不到承诺值,节能服务公司向客户进行补偿,若超出承诺值,客户给予节能服务公司一定的奖励。合同决策问题是该模式应用中的重要问题。本文以节能量保证型EPC合同中初始项目投资、合同期限和超额节能效益奖励的决策问题为研究对象,建立了客户和节能服务公司之间的决策博弈模型,分析二者的最优合同决策。数值试验结果表明,该方法不仅能让客户企业和节能服务公司均受益,还可以有效提高项目的投资报酬率,并且较高的节能服务公司技术水平和客户初始耗能水平能产生更高的节能效率。     

Portfolio Optimization Models on Infinite-Time Horizon

A portfolio optimization problem on an infinite-time horizon is considered. Risky asset prices obey a logarithmic Brownian motion and interest rates vary according to an ergodic Markov diffusion process. The goal is to choose optimal investment and consumption policies to maximize the infinite-horizon expected discounted hyperbolic absolute risk aversion (HARA) utility of consumption. The problem is then reduced to a one-dimensional stochastic control problem by virtue of the Girsanov transformation. A dynamic programming principle is used to derive the dynamic programming equation (DPE). The subsolution/supersolution method is used to obtain existence of solutions of the DPE. The solutions are then used to derive the optimal investment and consumption policies. In addition, for a special case, we obtain the results using the viscosity solution method.     

On the application of a transportation model for revenue optimization in waste management: a case study

This paper describes an interactive decision support system called Opti-Link which has been developed for a company operating in the area of waste and raw material management. Built around a specific transportation problem, the system is used to maximize the revenue generated by selling waste paper to paper mills. Furthermore, the dual variables of the linear program allow the planner to identify upper bounds for setting bid prices to buy waste paper from waste collection companies. First operational results indicate a significant increase in profit while at the same time the duration of the planning process could be cut by more than half.     

Dynamic multi-criteria evaluation of co-evolution strategies for solving stock trading problems

Risk and return are interdependent in a stock portfolio. To achieve the anticipated return, comparative risk should be considered simultaneously. However, complex investment environments and dynamic change in decision making criteria complicate forecasts of risk and return for various investment objects. Additionally, investors often fail to maximize their profits because of improper capital allocation. Although stock investment involves multi-criteria decision making (MCDM), traditional MCDM theory has two shortfalls: first, it is inappropriate for decisions that evolve with a changing environment; second, weight assignments for various criteria are often oversimplified and inconsistent with actual human thinking processes.In 1965, Rechenberg proposed evolution strategies for solving optimization problems involving real number parameters and addressed several flaws in traditional algorithms, such as their use of point search only and their high probability of falling into optimal solution area. In 1992, Hillis introduced the co-evolutionary concept that the evolution of living creatures is interactive with their environments (multi-criteria) and constantly improves the survivability of their genes, which then expedites evolutionary computation. Therefore, this research aimed to solve multi-criteria decision making problems of stock trading investment by integrating evolutionary strategies into the co-evolutionary criteria evaluation model. Since co-evolution strategies are self-calibrating, criteria evaluation can be based on changes in time and environment. Such changes not only correspond with human decision making patterns (i.e., evaluation of dynamic changes in criteria), but also address the weaknesses of multi-criteria decision making (i.e., simplified assignment of weights for various criteria).Co-evolutionary evolution strategies can identify the optimal capital portfolio and can help investors maximize their returns by optimizing the preoperational allocation of limited capital. This experimental study compared general evolution strategies with artificial neural forecast model, and found that co-evolutionary evolution strategies outperform general evolution strategies and substantially outperform artificial neural forecast models. The co-evolutionary criteria evaluation model avoids the problem of oversimplified adaptive functions adopted by general algorithms and the problem of favoring weights but failing to adaptively adjust to environmental change, which is a major limitation of traditional multi-criteria decision making. Doing so allows adaptation of various criteria in response to changes in various capital allocation chromosomes. Capital allocation chromosomes in the proposed model also adapt to various criteria and evolve in ways that resemble thinking patterns.