考虑交易成本的投资组合效率估计方法

投资组合绩效评价是学术界研究的热点问题。本文在经典的经济学框架下,基于真实前沿面,给出了投资组合效率的明确定义。由于实际投资环境的影响,投资组合优化模型非常复杂,难以获得真实前沿面的解析解,这给投资组合效率的应用带来了很大的困难。本文基于投资组合理论,在投资组合模型所对应的前沿面为凹函数的情况下,采用基于数据的投资组合DEA评价模型构造前沿面来逼近真实的前沿面,从而估计一般情形下投资组合的效率。在此基础上研究了考虑交易成本的投资组合效率评价问题,并用实例说明了本文方法的合理性与可行性。     

DEA with streaming data

DEA can be interpreted as a tool for the identification of “frontier outliers” among data points. These are points that are potentially interesting because they exhibit extreme properties in that the values of their attributes, either alone or combined, are at the upper or lower limits of the data set to which they belong. A real challenge for this type of frontier analysis arises when data stream in at high rates and the DEA analysis needs to be performed quickly. This paper extends DEA into this dynamic data environment. The purpose is to propose a formal theoretical framework to handle streaming data and to answer the question of how fast data can be processed using this new framework. Potential applications involving large data sets include auditing, appraisals, fraud detection, and security. In such settings the situation is likely to be dynamic with the data domain constantly changing as new entities arrive in the course of time. New specialized tools to adapt DEA to deal with streaming data will be explored.     

Locations and vehicle routing designs of physical distribution systems

The design of a physical distribution system (PDS) involves the determination of the number and locations of distribution centres, estimation of required vehicle numbers and design of vehicle routings. Due to the enormous numbers of possible combinatorial designs of the system, it is difficult to obtain an optimal design in acceptable computation effort on many occasions. In this paper, a new solution framework for the design of PDS by implementing the genetic algorithm (GA) is presented. With the characteristic of simultaneous optimization of a large population of configuration, the proposed methodology has been proved to be an extremely efficient optimizer. In the experimental simulation conducted in this paper, it also indicates this approach can provide a near-optimal solution to the design of PDS. To analyse the growth and decay of many schemas contained in the population, the effects of the operation of reproduction, crossover and mutation on the schema are studied. The simulation evaluation about the system performance and genetic parameters is presented along with the discussions at the end of this paper.     

考虑交易成本的多阶段投资组合评价方法研究

多阶段投资组合评价是目前研究的热点问题,本文将交易成本考虑进去,构建了考虑交易成本的多阶段投资组合优化模型,基于真实前沿面定义了投资组合的效率并构建了相应的非线性模型进行计算。针对非线性模型难以求解及真实前沿面解析解难以获得等问题,本文证明了前沿面函数为凹函数,进而利用DEA模型的前沿面来逼近真实前沿面并估计多阶段投资组合的效率,最后通过仿真分析验证了本文方法的有效性。     

Closest targets and strong monotonicity on the strongly efficient frontier in DEA

The determination of closest efficient targets has attracted increasing interest of researchers in recent Data Envelopment Analysis (DEA) literature. Several methods have been introduced in this respect. However, only a few attempts exist that analyze the implications of using closest targets on the technical inefficiency measurement. In particular, least distance measures based on Hölder norms satisfy neither weak nor strong monotonicity on the strongly efficient frontier. In this paper, we study Hölder distance functions and show why strong monotonicity fails. Along this line, we provide a solution for output-oriented models that allows assuring strong monotonicity on the strongly efficient frontier. Our approach may also be extended to the most general case, i.e. non-oriented models, under some conditions of regularity.     

Common benchmarking and ranking of units with DEA

This paper develops a common framework for benchmarking and ranking units with DEA. In many DEA applications, decision making units (DMUs) experience similar circumstances, so benchmarking analyses in those situations should identify common best practices in their management plans. We propose a DEA-based approach for the benchmarking to be used when there is no need (nor wish) to allow for individual circumstances of the DMUs. This approach identifies a common best practice frontier as the facet of the DEA efficient frontier spanned by the technically efficient DMUs in a common reference group. The common reference group is selected as that which provides the closest targets. A model is developed which allows us to deal not only with the setting of targets but also with the measurement of efficiency, because we can define efficiency scores of the DMUs by using the common set of weights (CSW) it provides. Since these weights are common to all the DMUs, the resulting efficiency scores can be used to derive a ranking of units. We discuss the existence of alternative optimal solutions for the CSW and find the range of possible rankings for each DMU which would result from considering all these alternate optima. These ranking ranges allow us to gain insight into the robustness of the rankings.     

基于偏好DEA模型的中国纺织业效率评价

基于具有输入输出指标偏好信息的数据包络分析模型评价中国纺织工业的效率,给出了描述规模报酬不变和规模报酬可变假设的DEA模型及其对偶规划的一种形式。模型克服传统DEA模型应用中常见的决策单元权重为零进而高估决策单元效率的不合理现象,有效地测算了中国31个地区纺织工业的经济运行效率,并分析了地区间效率水平差异的原因。利用各决策单元在经验生产前沿面上的投影点,估计了中国纺织工业的随机生产前沿函数,最后用效率弹性线性递减模型分析了销售收入、人均资产、台港澳和外商投资对各地区效率的微观影响关系。     

随机环境下的多投入多产出生产前沿面估计

传统的DEA模型假设观测样本的投入产出都是确定型数据, 这使得DEA在实际应用中受到限制, 本文提出的基于拟似然估计的多投入多产出随机非参数包络数据 (PLE-StoNED) 方法拓展了这个假设, 能够估计随机环境下的生产前沿面.本文证明, 生产可能集假设条件下的前沿面可以用一个有凹凸性和单调性限制的函数来表示.相较之前的StoNED方法, 本文提出的方法可以估计随机环境下多投入多产出决策单元 (DMU) 的前沿面.通过Monte Carlo实验, 多投入多产出PLE-StoNED方法的有效性得到验证, 它可纠正DEA等传统方法产生的偏误.最后, 实证研究部分运用这一新提出的方法估计了中国大陆商业银行的生产前沿面和效率.本文提出的方法弥补了DEA缺乏统计性的不足, 可为决策者在随机环境下对多投入多产出决策单元进行生产力和效率评估提供决策参考.     

Cell-to-switch level planning in mobile wireless networks for efficient management of radio resources

A major step in effectively managing radio resources in a cellular network is to design an appropriate scheme for assigning cells to a location area (LA), serviced by a switch, and allocate resources for individual switches. However, this assignment is already proven in the literature to be an NP-hard problem [Merchant A, Sengupta B. Assignment of cells to switches in PCS networks. IEEE/ACM Transactions on Networking 3(5) (1995) 521–6] that requires efficient heuristic search techniques for obtaining real-time solutions. This work presents a state-space search technique, which is a variant of best first search heuristic technique. The algorithm called the block depth first search (BDFS), allocates cells to switches during switch level resource planning. Under various simulated performance criteria, we compare the performance of the proposed technique with other similar procedures in the literature. Our results indicate that the BDFS outperforms the meta-heuristic procedures in terms of both efficiency and quality of solutions. Hence, we conclude that our proposed technique can be effectively used for switch level planning leading to an efficient management of scarce radio resource in cellular networks.     

物流敏捷调运决策支持系统的研究

由于物流敏捷调运优化方案的生成过程,本质上是一个决策过程。针对各决策环节,提出了借鉴IDEF0方法描述的敏捷调运优化适度递阶控制模型,在此基础上构建了决策支持系统总体框架。针对该决策支持系统运行模块,提出了基于敏捷性准则的运行控制思想。进而,对该决策支持系统中的数据库系统、模型库与方法库进行了研究,利用递阶设计思想设计了数据库的三大体系,提出了以"剩余装载总量"极小化为目标的物流敏捷调运优化(0-1)_h模型和对分搜索算法。通过对搜索次数的估计来衡量(0-1)_h模型求解算法的计算复杂性,结果表明该算法效率较高。     

两阶段DEA前沿面投影问题研究——兼对我国上市银行运营绩效进行评价

DEA方法,即数据包络分析方法,是一种用于评价决策单元(Decision Making Units,DMUs)相对有效性的实证方法。近年来DEA方法已经广泛的应用于各行各业的绩效评价中,并发展出两阶段DEA方法。两阶段DEA方法相对于传统DEA方法的优势在于,它不但可以提供被评价对象的总体效率值,还可以分别生成每一阶段的效率值。但正是由于中间要素的存在,按照传统的DEA方法来调整两阶段DEA投入、产出要素的优化过程已不能成功投影在有效前沿面上。本文基于两阶段DEA方法,通过加入"虚拟中间要素"在两阶段DEA中嵌入一个"虚拟阶段",这样不但完善了两阶段DEA的逻辑结构,而且成功的将被评价单元投影到有效前沿面。最后本文应用以上方法对我国上市银行的运营绩效进行了实证分析。实证结果令我们意外的是,国有商业银行运营绩效优于股份制银行。     

基于最近距离投影的DEA两阶段效率评价方法研究

数据包络分析是进行效率评价最重要的方法之一。传统的数据包络分析理论主要寻找有效前沿面上的最远距离投影，在极大化无效性指数的同时也面临着效率改进的巨大难度和高额成本。对于具有两阶段内部生产结构的决策单元，本文从考虑最小改进难度的视角出发，提出了最近距离投影的两阶段效率评价方法。该方法首先得到所有强有效决策单元的线性组合，且这些组合均占优于被评价的两阶段决策单元。然后建立了两阶段范围调整效率评价模型，在确定具有最近投影距离的占优组合的同时，得到了两阶段评价效率。最后，本文运用我国32家上市银行的年度数据对所提出方法进行了应用验证。     

基于DEA的作业分析与评价模型

论文将数据包络分析(DEA)引入了作业成本管理(ABM),用定量化分析模型从不同角度分析了作业消耗资源和提供价值的效率,并进行了实证分析。基于DEA的作业分析与评价模型可以得到以下信息:(1)确定作业消耗资源的DEA有效性;(2)确定各DMU在有效生产前沿面上的投影,并为下一期间的作业改进提供信息;(3)确定资源输入的总体利用率和各项输入可变资源的利用率;(4)采用复合DEA模型评价非财务指标对作业有效性的影响。     

A method combining genetic algorithm with simultaneous perturbation stochastic approximation for linearly constrained stochastic optimization problems

This paper considers the optimization of linearly constrained stochastic problem which only noisy measurements of the loss function are available. We propose a method which combines genetic algorithm (GA) with simultaneous perturbation stochastic approximation (SPSA) to solve linearly constrained stochastic problems. The hybrid method uses GA to search for optimum over the whole feasible region, and SPSA to search for optimum at local region. During the GA and SPSA search process, the hybrid method generates new solutions according to gradient projection direction, which is calculated based on active constraints. Because the gradient projection method projects the search direction into the subspace at a tangent to the active constraints, it ensures new solutions satisfy all constraints strictly. This paper applies the hybrid method to nine typical constrained optimization problems and the results coincide with the ideal solutions cited in the references. The numerical results reveal that the hybrid method is suitable for multimodal constrained stochastic optimization problem. Moreover, each solution generated by the hybrid method satisfies all linear constraints strictly.     

基于DEA的作业消耗资源效率分析

将数据包络分析(DEA)引入了作业成本管理(ABM),给出了适用的定量化分析方法,从不同角度分析了作业消耗资源的效率.确定作业消耗资源的DEA有效性;确定各DMU在有效生产前沿面上的"投影",并为下一期间的作业改进提供信息;确定资源投入的总体利用率和各项输入可变资源的利用率.最后,用该模型对某企业的作业进行了实证分析.     

Simulation optimization through direct search for multi-objective manufacturing systems

Simulation modelling has been one of the most widely used techniques for analysing complex manufacturing systems. In this paper, we propose a direct search algorithm expanded from the Hooke-Jecves pattern search to systematically and efficiently locate satisfactory solutions for multi-objective simulation models. The user-specified goals can be precise and/or fuzzy. Heuristic rules stemming from the simulation result of resource statistics are incorporated into the Hooke-Jeeves pattern search. The proposed heuristic rules make the search procedure effective regardless of different initial points and various bounded ranges of decision variables. Experimental results show that the proposed approach is suitable for analysing complex manufacturing systems, in which multiple objectives and multiple decision variables are encountered.     

LASSO variable selection in data envelopment analysis with small datasets

The curse of dimensionality problem arises when a limited number of observations are used to estimate a high-dimensional frontier, in particular, by data envelopment analysis (DEA). The study conducts a data generating process (DGP) to argue the typical “rule of thumb” used in DEA, e.g. the required number of observations should be at least larger than twice of the number of inputs and outputs, is ambiguous and will produce large deviations in estimating the technical efficiency. To address this issue, we propose a Least Absolute Shrinkage and Selection Operator (LASSO) variable selection technique, which is usually used in data science for extracting significant factors, and combine it in a sign-constrained convex nonparametric least squares (SCNLS), which can be regarded as DEA estimator. Simulation results demonstrate that the proposed LASSO-SCNLS method and its variants provide useful guidelines for the DEA with small datasets.     

Serial Chain Merger Evaluation Model and Application to Mortgage Banking*

Mortgage banking operations can be viewed from a supply chain perspective, where the primary and secondary markets are upstream and downstream chain members, respectively. This article describes a serial‐chain‐merger data envelopment analysis (DEA) model to assess potential gains from the merger of different chain operations. We show that in our framework the merger of different chains with many sub‐chains is efficient within the DEA paradigm if and only if the mergers of sub‐chain members are all efficient. A case study of a banking operations merger is conducted to illustrate and validate the proposed approach. The computations show that merger of operations can result in overall efficiency improvement in the banking industry if the merger preserves the features assumed in the DEA model.     

Incorporating cost and environmental factors in quality function deployment using data envelopment analysis

Quality function deployment (QFD) is an important tool available to organizations for efficient product design and development. Traditionally, QFD rates the design requirements (DRs) with respect to customer needs, and aggregates the ratings to get relative importance scores of DRs. An increasing number of studies stress on the need to incorporate additional factors, such as cost and environmental impact, while calculating the relative importance of DRs. However, there is a paucity of methodologies for deriving the relative importance of DRs when several additional factors are considered. In this paper, data envelopment analysis (DEA) is suggested for the purpose. It is proved that the relative importance values computed by DEA coincide with traditional QFD calculations when only the ratings of DRs with respect to customer needs are considered, and when only one additional factor, namely cost, is considered. DEA provides a general framework facilitating QFD computations when more factors need to be considered. The calculations are explained using a step-by-step procedure and illustrations. The proposed QFD–DEA methodology is applied to the design of security fasteners for a Chinese company. Though traditional QFD calculations consider the ratings as cardinal numbers, DEA has the flexibility to treat the ratings as qualitative variables. This aspect is illustrated in a separate section.     

Estimation of distribution algorithms using Gaussian Bayesian networks to solve industrial optimization problems constrained by environment variables

Many real-world optimization problems involve two different subsets of variables: decision variables, and those variables which are not present in the cost function but constrain the solutions, and thus, must be considered during optimization. Thus, dependencies between and within both subsets of variables must be considered. In this paper, an estimation of distribution algorithm (EDA) is implemented to solve this type of complex optimization problems. A Gaussian Bayesian network is used to build an abstraction model of the search space in each iteration to identify patterns among the variables. As the algorithm is initialized from data, we introduce a new hyper-parameter to control the influence of the initial data in the decisions made during the EDA execution. The results show that our algorithm improves the cost function more than the expert knowledge does.