Impact assessment based sectoral balancing in public R&D project portfolio selection

Government funding agencies spend significant amounts of R&D funds through funding programs. While allocating funds among sectors or scientific disciplines, the Decision Maker (DM) wants to maximize the total impact by supporting R&D activities in those sectors with higher scientific, social and economic return. On the other hand, the DM wants to balance the funding budget over sectors or disciplines. In this study, we incorporate the results of “sectoral impact assessments” into the public R&D project portfolio selection (RDPPS) problem. We develop a two-stage model. In the first stage, we make sectoral budget allocation decisions to maximize the total impact of the budget while ensuring a relative balance among sectors. In the second stage, we maximize the total score of supported projects under allocated sectoral budgets. We illustrate the proposed approach on an example problem. We show the value of the proposed approach by comparing our results with alternative policy options.     

Customers satisfaction in pediatric inpatient services: A multiple criteria satisfaction analysis

ObjectiveTo assess customer satisfaction determinants in a public pediatric inpatient service and propose some strategies to enhance the consumer and customer experience.MethodsWe applied a Multiple Criteria Customer Satisfaction Analysis to estimate the value functions associated with each satisfaction (sub)criterion and determine the corresponding weights. We characterized satisfaction criteria (according to the Kano's model), estimated the customers' demanding nature and the potential improvements, and proposed strategic priorities and opportunities to enhance customer satisfaction.Main findingsStrategies for satisfaction enhancement do not depend solely on the criteria with the lowest satisfaction levels and the estimated weights, each criterion's nature, the customers' demanding nature, and the technical margin for improvements.ConclusionsAreas deserving attention include clinical staff's communication skills, the non-clinical professionals' efficiency, availability, and kindness; food quality; visits' scheduling and quantity; and facilities' comfort.     

Portfolio selection methodology for a nuclear project

It is difficult to evaluate complex, publicly sensitive nuclear project proposals. Scientists, subject matter experts, politicians, and citizens often differ on scope, budget, time, and quality priorities. There are numerous qualitative factors and quantitative variables. Experts could widely disagree on criteria or neglect to discriminate between seemingly identical alternatives. An ineffective portfolio selection method could lead to safety problems, budget overruns, or outright project failures. This case study develops and examines a mixed‐method, integrated qualitative and quantitative portfolio selection model, applied to a “tritium extraction facility” project concept.     

A multiple criteria sorting method for supporting the maintenance management of medical ventilators: The case of Hospital da Luz Lisboa

Medical equipment is characterized by a constant flow of innovations, which is transforming the delivery of healthcare. This creates the need for healthcare organizations to incorporate methodologies to support the maintenance management of these equipment. Within this context, Luz Saúde, the holding company of one of the largest healthcare groups in Portugal, intends to complement the medical equipment maintenance management program of the biggest hospital in its network, Hospital da Luz Lisboa. To this aim, we develop a decision aiding tool for assessing the maintenance condition of medical ventilators and recommend a plan of action. For that, we apply a multicriteria decision aiding methodology, utilizing the Electre Tri-nC method. In interaction with the decision makers, we construct a model using twelve criteria for assigning ventilators to one of the five ordered categories representing the current condition (excellent, very good, good, adequate, and poor). Most of the medical ventilators in the analysis were considered in adequate or good maintenance conditions, which was consistent with the decision makers’ expectations. A detailed analysis of the results evidenced the robustness of the model. This study constitutes the first step toward the use of multicriteria methodologies for supporting decision processes in Hospital da Luz Lisboa.     

Estimation and model selection based inference in single and multiple threshold models

This paper evaluates the properties of a joint and sequential estimation procedure for estimating the parameters of single and multiple threshold models. We initially proceed under the assumption that the number of regimes is known á priori but subsequently relax this assumption via the introduction of a model selection based procedure that allows the estimation of both the unknown parameters and their number to be performed jointly. Theoretical properties of the resulting estimators are derived and their finite sample properties investigated.     

Selecting balanced portfolios of R&D projects with interdependencies: A Cross-Entropy based methodology

The intensive growth of technology makes firms rely on research and development (R&D) activities in order to adapt to technology changes in an ever-changing and uncertain environment. Due to R&D budget constraints and limited resources, firms are often forced to select a subset of all candidate projects by means of project portfolio selection techniques mitigating the corresponding risks and enhancing the overall value of portfolio. Projects' interdependencies and types were rarely considered in existing models of R&D portfolio selection that may result in selecting wrong projects. This flaw hinders the projects alignment with corporate objectives and strategy and leads to excessive risk and missing the promised values. In this paper, a balanced set of R&D project evaluation criteria was proposed. Next, to construct R&D project portfolio, a 0–1 nonlinear mathematical programming method for balancing portfolio values and risks was proposed, in which research projects' interdependencies, types and other constraints were all considered. Finally, a Cross-Entropy algorithm was developed to solve the proposed model and results were reported. The algorithm proved to be very effective in terms of solution quality and computational time. The proposed algorithm especially suits large scale instances while exact approaches are doomed to fail.     

A multiobjective optimization model for project selection with probabilistic considerations

When faced with limited resources, project managers must determine which projects to fund at what levels from a pool of potential ones. This problem of project selection is inherently multiobjective since various factors, such as the available budget, the chance of success, and the efficient allocation of the project team, must be considered simultaneously. The uncertainty of the data at the time decisions are made further complicates project selection. In this paper, a multiobjective, integer-constrained optimization model with competing objectives for project selection is formulated using probability distributions to describe costs. The objectives correspond to important project criteria, such as: rank (value), managerial labor needed, and average cost. The subjective rank is determined via the Analytic Hierarchy Process. The model is applied to a data set from a US government agency that involves 84 separate projects. The results indicate improved budgetary efficiency compared to the actual project selection, thus supporting use of the model for public sector project selection. The model is unique since it integrates multiobjective optimization, Monte Carlo simulation, and the Analytic Hierarchy Process.     

A test for the weights of the global minimum variance portfolio in an elliptical model

In this paper we consider the weights of the global minimum variance portfolio (GMVP). The returns are assumed to follow a matrix elliptically contoured distribution, i.e., the returns are assumed to be neither independent nor normally distributed. A test for the general linear hypothesis is given. The distribution of the test statistic is derived under the null and the alternative hypothesis. It turns out that its distribution is invariant with respect to the type of the matrix elliptical distribution, i.e., the stochastic properties of the GMVP do not depend either on the mean vector or on the distributional assumptions imposed on asset returns. In an empirical study we analyze an international diversified portfolio.     

A Comprehensive Framework for Sustainable Project Portfolio Selection Based on Structural Equation Modeling

Project selection problems are inherently complex problems with multiple and often conflicting objectives. The complexity of project selection problems is due to the high number of projects from which a subset (portfolio) has to be chosen. Various analytical methods, ranging from the simple weighted sum to complex mathematical programming have been proposed to solving these problems. We propose an integrated approach for strategic and sustainable project portfolio selection, which is composed of two distinct but interrelated modules. In the first module, we use the strategic planning and sustainability concepts to select a set of promising projects. In the second module, we use a project portfolio selection procedure to choose among the promising projects identified in the first module. A structural equation model is used to analyze and explain the relationships among different factors in the proposed framework. More specifically, we investigate the effects of: (1) strategic level performance on sustainability, post‐implementation, and overall performance; (2) implementation performance on post‐implementation and overall performance; (3) portfolio selection performance on implementation and overall performance; and (4) post‐implementation performance on overall performance. A case study in investment banking is presented to demonstrate the applicability of the proposed model and exhibit the efficacy of the procedures and algorithms.     

A novel two-stage method for well-diversified portfolio construction based on stock return prediction using machine learning

The performance of portfolio model can be improved by introducing stock prediction based on machine learning methods. However, the prediction error is inevitable, which may bring losses to investors. To limit the losses, a common strategy is diversification, which involves buying low-correlation stocks and spreading the funds across different assets. In this paper, a diversified portfolio selection method based on stock prediction is proposed, which includes two stages. To be specific, the purpose of the first stage is to select diversified stocks with high predicted returns, where the returns are predicted by machine learning methods, i.e. random forest (RF), support vector regression (SVR), long short-term memory networks (LSTM), extreme learning machine (ELM) and back propagation neural network (BPNN), and the diversification level is measured by Pearson correlation coefficient. In the second stage, the predictive results are incorporated into a modified mean–variance (MMV) model to determine the proportion of each asset. Using China Securities 100 Index component stocks as study sample, the empirical results demonstrate that the RF+MMV model achieves better results than similar counterparts and market index in terms of return and return–risk metrics.     

Success criteria and factors for international development projects: A life‐cycle‐based framework

The paper presents a new conceptual model for not‐for‐profit international development projects that identifies different sets of success criteria and factors in the project life‐cycle phases and then provides the dynamic linkages among these criteria and factors. The model can serve as a basis to evaluate the project status and to forecast the results progressively throughout the stages. Thus, it helps the project management team and the key stakeholders prioritize their attention and scarce development resources to ensure successful project completion. Empirical data from a field survey conducted in selected Southeast Asian countries confirm the model's validity and also illustrate important managerial implications.