Project portfolio designing using data envelopment analysis and De Novo optimisation

In a rapidly evolving economic world, projects become tools to support organization goals. Project portfolio is set of all projects that are implemented in the organisation at a time. Possible projects are characterized by sets of inputs and outputs, where inputs are resources for project realisation and outputs measure multiple goals of the organisation. The data envelopment analysis (DEA) is an appropriate approach to select efficient projects. The organisation has its total resources in limited quantities. Designing a portfolio of efficient projects not exceeding the limited resources does not always lead to the most efficient portfolio. De Novo optimisation is an approach for designing optimal systems by reshaping the feasible set. The paper proposes a new approach for project portfolio designing based on a systemic combination of DEA model and De Novo optimisation approach. A total available budget is a restriction on project portfolio. The proposed concept provides designing of optimal project portfolio with the minimal budget. Performance measures of the designed project portfolio are the efficiency of the portfolio and the effectiveness of outputs. Possible extensions of the concept are formulated and discussed.     

A zero-one model for project portfolio selection and scheduling

A zero-one integer linear programming model is proposed for selecting and scheduling an optimal project portfolio, based on the organisation's objectives and constraints such as resource limitations and interdependence among projects. The model handles some of the issues that frequently arise in real world applications but are not addressed by previously suggested models, such as situations in which the amount of available and consumed resources varies in different periods. It also allows for interactive adjustment following the optimisation process, to provide decision makers a method for controlling portfolio selection, based on criteria that may be difficult to elicit directly. It is critical for such a system to provide fast evaluation of alternatives the decision makers may want to examine, and this requirement is addressed. The proposed model not only suggests projects that should be incorporated in the optimal portfolio, but it also determines the starting period for each project. Scheduling considerations can have a major impact on the combination of projects that can be incorporated in the portfolio, and may allow the addition of certain projects to the portfolio that could not have been selected otherwise. An example problem is described and solved with the proposed model, and some areas for future research are discussed.     

A mean-risk index model for uncertain capital budgeting

This paper discusses the capital budgeting problem of projects using annual cash inflows, cash outflows and initial investment outlays given by experts’ evaluations when no historical data are available. Uncertain variables are used to describe the projects’ parameters. A profit risk index and a capital risk index are proposed, and a mean-risk index model is developed for optimal project selection. In addition, the deterministic forms of the model are given and a solution algorithm is provided. For the sake of illustration, a numerical example is also presented. The results of the example show that both profit risk index and capital risk index are important in investment risk control. However, when the profit risk control requirement is strong, the selected project portfolio may be insensitive to the capital risk constraint; when the profit risk control requirement is moderate, the capital risk constraint plays an important role. The results also show the tendency that when either the tolerable profit risk level or the tolerable capital risk level becomes higher, the obtained expected net present value of the project portfolio becomes larger, which is in agreement with the investment rule that the higher the risk, the higher the return.     

Multi-mode resource constrained multi-project scheduling and resource portfolio problem

This paper introduces a multi-project problem environment which involves multiple projects with assigned due dates; activities that have alternative resource usage modes; a resource dedication policy that does not allow sharing of resources among projects throughout the planning horizon; and a total budget. Three issues arise when investigating this multi-project environment. First, the total budget should be distributed among different resource types to determine the general resource capacities, which correspond to the total amount for each renewable resource to be dedicated to the projects. With the general resource capacities at hand, the next issue is to determine the amounts of resources to be dedicated to the individual projects. The dedication of resources reduces the scheduling of the projects’ activities to a multi-mode resource constrained project scheduling problem (MRCPSP) for each individual project. Finally, the last issue is the efficient solution of the resulting MRCPSPs. In this paper, this multi-project environment is modeled in an integrated fashion and designated as the resource portfolio problem. A two-phase and a monolithic genetic algorithm are proposed as two solution approaches, each of which employs a new improvement move designated as the combinatorial auction for resource portfolio and the combinatorial auction for resource dedication. A computational study using test problems demonstrated the effectiveness of the solution approach proposed.     

Robust portfolio modeling with incomplete cost information and project interdependencies

Robust portfolio modeling (RPM) [Liesiö, J., Mild, P., Salo, A., 2007. Preference programming for robust portfolio modeling and project selection. European Journal of Operational Research 181, 1488–1505] supports project portfolio selection in the presence of multiple evaluation criteria and incomplete information. In this paper, we extend RPM to account for project interdependencies, incomplete cost information and variable budget levels. These extensions lead to a multi-objective zero-one linear programming problem with interval-valued objective function coefficients for which all non-dominated solutions are determined by a tailored algorithm. The extended RPM framework permits more comprehensive modeling of portfolio problems and provides support for advanced benefit–cost analyses. It retains the key features of RPM by providing robust project and portfolio recommendations and by identifying projects on which further attention should be focused. The extended framework is illustrated with an example on product release planning.     

基于资金现值约束的双目标项目组合选择模型研究

近年来,项目组合选择问题已引起人们越来越多的关注,如何从众多项目中选择合适项目以满足企业长期发展战略已成为企业面临的重要问题。因此,本文在考虑项目可打断的基础上构建了一个净现值和效用并存的双目标项目组合选择模型,同时把模型中的资金约束转变为资金现值约束,并通过理论给予证明,使模型得以简化。最后,通过实际算例进行分析。结果表明:基于双目标的项目组合选择模型比单一目标更加符合企业长期发展战略,该模型也为投资决策者进行项目组合选择提供了较完善的理论依据。     

A Portfolio Approach to Capital Budgeting: An Application to the Expansion to Additional Product Lines

The importance of the covariance of returns between capital assets is one of the basic principles of modern portfolio theory. An investor should seek capital assets which have negative covariance of returns, or if such capital assets are not available, capital assets with low covariance should be sought for a portfolio. From the variance-covariance structure of returns of the capital assets and the expected returns for each capital asset, a risk-reward trade-off or efficient frontier can be generated. The trade-off represents the minimum risk, as measured by portfolio variance, that could be incurred to realize a desired rate of return for the portfolio. This concept applies to a portfolio of capital budgeting projects as well as to a portfolio of securities. This paper demonstrates how this concept of portfolio diversification can be applied to a capital budgeting problem. The problem involves an actual problem faced by a U.S. distributor who must decide whether to expand sales into one of two industries. Quadratic programming is used to generate the risk-reward relationships and it is shown that the entry into one industry clearly provides a superior risk-reward relationship than entry into the other industry and compared to the company's present sales policy.     

受融资影响的项目组合选择研究

本文构建了一个考虑融资的项目组合选择模型,允许企业根据每期的投资计划,合理地调节当期所需要投入的资金预算。如果期初的可用资金量超过了当期的投资额,企业可以尽早将多余的资金释放;反之,若期初的可用资金量不足以支持本期项目的执行,允许企业从外部融资,如银行贷款等,以满足企业实际的需求。融资需要付出一定的代价,且代价与执行项目的风险程度相关。基于此,偿还融资代价对项目组合经济性的影响被引入到项目组合选择模型中。企业需要在全部资金带来的投资收益及融资代价之间进行权衡,以谋求利润最大化。鉴于所构建模型非线性化的特点,本文给出了该模型的等价形式,并进行了理论证明。最后,通过企业实际算例分析,得出结论:1)考虑融资后能够为企业带来更高回报;2)融资相关因素对项目组合的经济性有显著影响。     

A multiobjective evolutionary approach for linearly constrained project selection under uncertainty

In the project selection problem a decision maker is required to allocate limited resources among an available set of competing projects. These projects could arise, although not exclusively, in an R&D, information technology or capital budgeting context. We propose an evolutionary method for project selection problems with partially funded projects, multiple (stochastic) objectives, project interdependencies (in the objectives), and a linear structure for resource constraints. The method is based on posterior articulation of preferences and is able to approximate the efficient frontier composed of stochastically nondominated solutions. We compared the method with the stochastic parameter space investigation method (PSI) and illustrate it by means of an R&D portfolio problem under uncertainty based on Monte Carlo simulation.     

A fuzzy approach to R&D project portfolio selection

A major advance in the development of project selection tools came with the application of options reasoning in the field of Research and Development (R&D). The options approach to project evaluation seeks to correct the deficiencies of traditional methods of valuation through the recognition that managerial flexibility can bring significant value to projects. Our main concern is how to deal with non-statistical imprecision we encounter when judging or estimating future cash flows. In this paper, we develop a methodology for valuing options on R&D projects, when future cash flows are estimated by trapezoidal fuzzy numbers. In particular, we present a fuzzy mixed integer programming model for the R&D optimal portfolio selection problem, and discuss how our methodology can be used to build decision support tools for optimal R&D project selection in a corporate environment.     

Assessing the cost of capital for longevity risk

The cost of capital is a key element of the embedded value methodology for the valuation of a life business. Further, under some solvency approaches (in particular, the Swiss Solvency Test and the developing Solvency 2 project) assessing the cost of capital constitutes a step in determining the required capital allocation.Whilst the cost of capital is usually meant as a reward for the risks encumbering a given life portfolio, in actuarial practice the relevant parameter has been traditionally chosen, at least to some extent, inconsistently with such risks. The adoption of market-consistent valuations has then been advocated to reach a common standard.A market-consistent value usually acknowledges a reward to shareholders’ capital as long as the market does, namely if the risk is systematic or undiversifiable. When dealing with a life annuity portfolio (or a pension plan), an important example of systematic risk is provided by the longevity risk, i.e. the risk of systematic deviations from the forecasted mortality trend. Hence, a market-consistent approach should provide appropriate valuation tools.In this paper we refer to a portfolio of immediate life annuities and we focus on longevity risk. Our purpose is to design a framework for a valuation of the portfolio which is market-consistent, and therefore based on a risk-neutral argument, while involving some of the basic items of a traditional valuation, viz best estimate future flows and allocated capital. This way, we try to reconcile the traditional with a market-consistent (or risk-neutral) approach. This allows us, in particular, to translate the results obtained under the risk-neutral approach in terms of a properly redefined embedded value.     

Optimal strategies for selecting project portfolios using uncertain value estimates

Practically all organizations seek to create value by selecting and executing portfolios of actions that consume resources. Typically, the resulting value is uncertain, and thus organizations must take decisions based on ex ante estimates about what this future value will be. In this paper, we show that the Bayesian modeling of uncertainties in this selection problem serves to (i) increase the expected future value of the selected portfolio, (ii) raise the expected number of selected actions that belong to the optimal portfolio ex post, and (iii) eliminate the expected gap between the realized ex post portfolio value and the estimated ex ante portfolio value. We also propose a new project performance measure, defined as the probability that a given action belongs to the optimal portfolio. Finally, we provide analytic results to determine which actions should be re-evaluated to obtain more accurate value estimates before portfolio selection. In particular, we show that the optimal targeting of such re-evaluations can yield a much higher portfolio value in return for the total resources that are spent on the execution of actions and the acquisition of value estimates.     

Relief period optimization under budget constraints

Increased competition and the rapidly changing telecommunications environment have made it necessary to invest in new technologies and services. Additionally, there are pressures not to borrow heavily for these investments. When cash is tight and budgets must be constrained, the costs of placing capacity to cover forecast growth in the most economical manner may exceed the available capital budget. This paper investigates optimal capacity placement under a given budget, and presents a solution method to find optimal technology selection and project sizing. The problem is formulated as a nonlinear integer programming problem, typically with many hundreds of integer variables and many hundreds of continuous variables. A solution method is developed that exploits the structure of the problem, and reduces it to a two-dimensional implicit search over two multipliers: one that governs technology selection and one that governs project sizing.     

Regulatory capital decisions in the Context of consumer loan portfolios

A topic of interest in recent literature is regulatory capital requirements for consumer loan portfolios. Banks are required to hold regulatory capital for unexpected losses, while expected losses are to be covered by either provisions or future income. In this paper, we show the set of efficient operating points in the market share and profit space for a portfolio manager operating under Basel II capital requirement and under capital constraints are a union of single-cutoff-score and double-cutoff-score operating points. For a portfolio manager to increase market-share beyond the maximum allowable under a single-cutoff score policy (eg, with binding capital constraints) requires granting loans to higher than optimal risk applicants. We show this result in greater portfolio risk but without an increase in regulatory capital requirement amount. The increase in forecasted losses is assumed to be absorbed by provisions or future margin income. Given portfolio managers take on higher risk under the same regulatory capital amount, our findings call for greater focus on provision amounts and future margin income under the supervisory review pillar of Basel II. This research raises the issue of whether the design of the regulatory formula for consumer loan portfolios is flawed.     

项目组合网络实证研究

将复杂网络理论引入到项目组合管理中,以项目为节点,以项目之间的依赖关系为边,项目的成本看作点权,项目之间的依赖强度看作边权,将项目组合抽象为一个复杂加权网络。研究了4家企业的项目组合网络,在分析项目组合特性的基础上,概括了项目组合的复杂网络行为特征。对企业项目组合网络进行综合对比分析发现,项目组合网络具有如下相似特征:①节点度分布不同于其他社会网络,倾向于幂律分布,又有偏斜泊松分布的迹象;②度相关系数负相关,有别于其他社会网络;③具有集群结构;④聚集系数很大;⑤网络直径较小;⑥平均度数小于4。     

Empirical Bayes stock market portfolios

We exhibit a compound sequential Bayes portfolio selection algorithm based solely on the past which not only lives off market fluctuations but follows the drift as well. In fact, this sequential portfolio performs as well (up to first order terms in the exponent) as the optimal portfolio based on advance knowledge of the n-period empirical distribution of the market. Moreover, to first order in the exponent, the capital resulting from this portfolio will be no less than the best of the available stocks. This is a result that holds for every sample sequence. Thus bull markets and bear markets can not fool the investor into over-committing or under-committing his capital to the risky alternatives available to him. The goal is accomplished by a choice of portfolio which is robust with respect to futures that may differ drastically from the past.     

The 0–1 knapsack problem with multiple choice constraints

In this paper we consider the 0–1 knapsack problem with multiple choice constraints appended. Such a problem may arise in a capital budgeting context where only one project may be selected from a particular group of projects. Thus the problem is to choose one project from each group such that the budgetary constraint is satisfied and the maximum return is realized. We formulate two branch and bound algorithms which use two different relaxations as the primary bounding relaxations. In addition, theoretical results are given for a simple reduction in the number of variables in the problem.     

项目网络中考虑支配与扩散关系的组合决策

单纯侧重项目自身属性而不考虑项目关联性以及由项目衍生而来的技术、经验/信息扩散对项目组合决策时的影响,易导致决策偏差,低估具有潜在技术先导性项目的价值。对此,引用复杂网络理论,以项目关联性的视角,将项目间支配和扩散关系分别抽象为有向加权网络,运用K-shell分解方法构建项目组合网络中基于支配关系的项目影响力模型以及技术、经验/信息在项目间扩散传播的模型。然后,基于PageRank算法,综合考虑项目间支配与扩散关系,建立了项目优先级排序决策模型。最后,通过算例分析说明了该模型与算法的可行性与有效性,为企业项目组合决策提供了有益的参考。     

Portfolio-optimization models for small investors

Since 2010, the client base of online-trading service providers has grown significantly. Such companies enable small investors to access the stock market at advantageous rates. Because small investors buy and sell stocks in moderate amounts, they should consider fixed transaction costs, integral transaction units, and dividends when selecting their portfolio. In this paper, we consider the small investor’s problem of investing capital in stocks in a way that maximizes the expected portfolio return and guarantees that the portfolio risk does not exceed a prescribed risk level. Portfolio-optimization models known from the literature are in general designed for institutional investors and do not consider the specific constraints of small investors. We therefore extend four well-known portfolio-optimization models to make them applicable for small investors. We consider one nonlinear model that uses variance as a risk measure and three linear models that use the mean absolute deviation from the portfolio return, the maximum loss, and the conditional value-at-risk as risk measures. We extend all models to consider piecewise-constant transaction costs, integral transaction units, and dividends. In an out-of-sample experiment based on Swiss stock-market data and the cost structure of the online-trading service provider Swissquote, we apply both the basic models and the extended models; the former represent the perspective of an institutional investor, and the latter the perspective of a small investor. The basic models compute portfolios that yield on average a slightly higher return than the portfolios computed with the extended models. However, all generated portfolios yield on average a higher return than the Swiss performance index. There are considerable differences between the four risk measures with respect to the mean realized portfolio return and the standard deviation of the realized portfolio return.     

Managing Cost Overrun Risk in Project Funding Allocation

This paper discusses decision making of project funding allocation under uncertain project costs. Because project costs are uncertain and funding allocations may not necessarily match the costs required, each project is inherently subject to a cost overrun risk (COR). In this paper, a model is proposed in which project cost is treated as a factor with a probability density function. The decision maker then allocates the total funding to the projects while minimizing a weighted sum of mean and variance of the COR of the project portfolio. Some properties of project COR are derived and interpreted. Optimal funding allocation, in relationship to factors such as various project sizes and riskiness, project interdependency, and the decision maker’s risk preference, is analyzed. The proposed funding allocation model can be integrated with project selection decision-making and provides a basis for more effective project control.