Project Portfolio Selection Model Research Articles

Project Risk Response Decision Making Under Uncertain Project Interdependencies

Project portfolio (PP) risk management entails making risk response decisions (RRDs) on single projects within PPs (SPPPs). However, the presence of uncertain project interdependencies (UPIs) increases the difficulty of RRDs. This study provides a decision model to make robust RRDs for SPPPs under UPIs, using a four-stage interval optimization model based on a two-tier risk-project network. A two-tier network is first constructed where the upper nodes denote the risks and the lower nodes denote the alternative projects. Next, a PP selection model is developed to obtain the probability of the projects being selected through simulation, in which the project interdependencies (PIs) are measured by the additional returns from the PP. Polytopic fuzzy linguistic term sets are formed to explore the strength of the relationships between the risks, and between the projects and risks. Third, the gravity model and the decision-making trial and evaluation laboratory are applied to prioritize the risks. Fourth, an interval optimization model under the uncertain response budget is constructed to yield the RRD outcome. The proposed model is validated through the RRDs for a Chinese construction firm. The results inform that, first, the mean utility of a decision maker (DM) considering UPIs fares better than those ignoring PIs and those considering PIs with certainty, second, the DM's mean utility considering PIs with certainty is still better than those ignoring PIs, and third, relying on the minimum budget threshold is more effective for reducing the uncertainty and improving the mean utility of a DM.

Goal programming and genetic algorithm in multiple objective optimization model for project portfolio selection: a review

Many scientific fields, such as engineering, data analytics, and deep learning, focus on optimization. Optimization problems are classified into two types based on the number of optimized objective functions: single objective and multi objective optimization problems. In this paper, a comparative review since 2000 using one of the deterministic and stochastic modelling approaches called goal programming (GP) and genetic algorithm (GA) in multi-objective optimization problem is discussed. This study gives a prime review of the application of GP and GA in various criteria of project portfolio selection problem. GP is a method for solving large-scale multi-objective optimization problems to assist decision makers in finding solutions that satisfy several competing goals. GA on the other hand are global meta-heuristic search algorithms that are used to provide approximation or optimal solutions to large-scale optimization problems. Of the 23 articles considered in this review showed that, from more than 100 projects, GA proved near optimal, feasible solution and efficient frontier in projects ranking, projects interaction and a preferred decision support tool of project portfolio selection. In addition, the two models select projects on risk-based approach, but GA proved to be more effective in terms of number of projects proposed, central processing unit (CPU) time and accuracy. The review concludes that, in multi-objective optimization model for project portfolio selection problems on a large-scale, very large or complex problems and less CPU time, GA is more effective than GP in multi-objective optimization problems. The review also showed gaps in previous studies of GP and GA application on project portfolio selection problem (PPSP). This review will aid scholars and demanding practitioners in gaining a broader understanding of goal programming and genetic algorithms in the context of project portfolio selection problems.

An Optimal Electronic Project Portfolio under Conditions of Uncertainty and Interactions between Projects

Today, organizations are faced with a multitude of projects and investment opportunities, which has made the decision to choose an organizational project due to the complexity of project evaluation and also limited resources face many challenges. Many studies have been performed to present and evaluate the performance of models and algorithms of project portfolio optimization. However, in terms of interactions between projects, many of these studies have been overlooked. This article deals with the issue of electronic project portfolio selection by considering the interactions between projects. For this purpose, a new one-objective linear programming model of integers zero and one, based on gray theory and in conditions of uncertainty, has been developed. In this regard, two multi-criteria utility objective functions have been developed to investigate the conditions for interactions. Finally, the performance of the proposed model is evaluated with a numerical example and the results of its implementation are presented. The results show that the organizational project portfolio selection model has a better performance compared to ignoring these effects by considering the interactions between projects. In fact, the proposed model illustrates the fact that considering the interactions in the project portfolio selection process will create synergies and prevent the selection of projects that are competitive with each other.

Optimal project portfolio selection with reinvestment strategy considering sustainability in an uncertain environment: a multi-objective optimization approach

PurposeThis study aims to develop a model for project portfolio selection considering organizational goals such as budgets, sustainability cash flow and reinvestment strategy under an uncertain environment.Design/methodology/approachA multi-objective mathematical programming model is proposed for project selection, which takes the social, environmental and financial aspects into account as the objectives of the project portfolio selection problem. The project evaluation and selection process in one of the large capitals in the Middle East with numerous urban construction projects was considered as a real case study, in which the subjects of environmental and social sustainability are of great importance. Then, the most significant criteria for project evaluation and selection based on sustainability were identified and ranked using the fuzzy best-worst method (BWM).FindingsThe criterion of “defining clear and real objectives” was ranked first, “project investment return period” was ranked second, “minimum changes in the predicted range” was ranked third, and the other ten sustainability indicators were ranked as well. Next, the presented mathematical programming model was solved using the augmented e-constraint method. The sensitivity analysis indicated that increasing the amount of investments in projects would increase their net present value. Also, increased investment had no effect on sustainability, while decreased investment caused sustainability to not being optimal.Originality/valueThis study focuses on the impact of the amount of investments on projects, and the associated costs of sustainable projects. Further to the authors' knowledge, there has been no relevant study taking uncertainty into account. Also, very few studies proposed a mathematical programming model for the project portfolio selection problem. Moreover, this research uses the brainstorming and Delphi method to identify the sustainability indicators influencing the organization and screens the evaluation indicators. Furthermore, the weights of the evaluation indicators are determined using the fuzzy BWM based on the consistency of opinions.

Research of Project Portfolio Selection Model Based on Organizational Strategy

Based on the theory of the existing research of the project portfolio management, the paper analyzes the key factors of the project portfolio selection, and evaluates them using the fuzzy comprehensive evaluation method. Finally, the paper builds a project portfolio choice integer programming model based on 0/1 integer programming, which is based on the organizations strategies.

Balancing strategic contributions and financial returns: a project portfolio selection model under uncertainty

This paper constructs a project portfolio selection model from the strategic perspective. Two goals are proposed for the portfolio to achieve, i.e., strategic contributions and financial returns. The uncertainties involved are addressed with fuzzy real options. Then, a modified multi-objective genetic algorithm is designed to determine the portfolios. Finally, a real case is provided to validate the model’s effectiveness. The results demonstrate that the proposed algorithm can optimize two objectives simultaneously and keep the plausible Pareto-optimal set which wins over the single-objective model solutions in achieving the shared value.

A fuzzy weighted average approach for selecting portfolio of new product development projects

New product portfolio selection is a multi-criteria decision making problem including both qualitative and quantitative criteria. Determining the exact values for these criteria is often difficult or even impossible taking into account uncertainty and complexity associated with new product development projects. To assist managers in making portfolio selection decisions, this study proposes a new project portfolio selection model that uses a fuzzy weighted average approach for ranking new product projects and artificial neural networks for estimating project performance. New product development projects are evaluated according to criteria related to marketing, project team, project performance, risk, and strategy. The use of neural networks enables more precise evaluation of project performance criteria and provides additional information in portfolio selection. A case study of the evaluation of new product projects illustrates the usefulness of the proposed approach.

R&D project evaluation and project portfolio selection by a new interval type-2 fuzzy optimization approach

In today's ever-changing and highly uncertain environment, organizations depend on research and development (R&D) activities to adapt intensive growth of technology. One of the most important and trickiest tasks of any developing firms is to define new projects. This process gets difficult when it comes to choosing an appropriate portfolio from a set of candidate projects. Since organizations are faced with limited resources of R&D and budget constraints, they have to choose a project portfolio that mitigates the corresponding risk and enhances the overall value of portfolio. Therefore, the purpose of this study is to introduce a practical model to select the best and the most proper project portfolio while considering project investment capital, return rate, and risk. The ever-changing and highly uncertain environment of projects is addressed by utilizing interval type-2 fuzzy sets (IT2FSs). In this paper, a new model of R&D project evaluation is first introduced. This model includes a new risk-return index. This model is then extended in project portfolio selection, and as a result, a new model of R&D project portfolio selection is proposed under uncertainty. Constraints and limitations of R&D project portfolio selection are comprehensively addressed. In this model, lower semi-variance is applied to consider risk of proposed projects. Therefore, this paper offers a new model that applies IT2FSs to handle uncertainty, uses semi-variance to assess risk, synchronously considers risk and return in its selection process, and addresses the considerations and limits of real-world problems. Eventually, to verify the proposed model, a numerical example of the existing literature is solved with the model, and the results are compared. The first proposed model is used to prioritize proposed R&D projects of a gas and oil development holding firm as a real case study. To illustrate further, a practical example is also provided to demonstrate the applicability of the proposed project portfolio selection model.

A New Optimization Model for Project Portfolio Selection Under Interval-Valued Fuzzy Environment

Selecting the right projects is the primary objective of project-oriented organizations. The main concern of this research is to propose a new optimizing model for project evaluation and project portfolio selection under interval-valued fuzzy (IVF) environment. Projects involvement in uncertainties and complexities is notable, and managers have to make decisions under uncertain environments. In order to assist top managers in project selection under these circumstances, investment capitals and net cash flows of the projects in this paper are presented as IVF numbers instead of crisp or classical fuzzy numbers. Using IVF sets enables the proposed model to consider uncertainty more practically which is achieved through addressing vagueness and lack of information intuitively. A new compound index that simultaneously takes risk and return into account is also presented. This approach illustrates both risk level and return level of project and then calculates the risk of unit return of project. Risk is measured by lower semi-variance of projects’ returns which is a direct, clear and widely accepted downside risk measure. The presented model is first proposed for project evaluation and comparison; then, it is extended for project portfolio selection problem. Finally, the proposed optimization model is exemplified by evaluating the candidate projects and selecting a portfolio of project in real case study of a holding company in developing countries. Moreover, a numerical example is presented to illustrate the capability of model in large-size problems.

An extended model for project portfolio selection with project divisibility and interdependency

In this paper, we develop an extended model for the project portfolio selection problem over a planning horizon with multiple time periods. The model incorporates the factors of project divisibility and interdependency at the same time for real-life applications. The project divisibility is considered as a strategy, not an unfortunate event as in the literature, in choosing the best execution schedule for the projects, and the classical concept of “project interdependencies” among fully executed projects is then extended to the portions of executed projects. Additional constraints of reinvestment consideration, setup cost, cardinality restriction, precedence relationship and scheduling are also included in the model. For efficient computations, an equivalent mixed integer linear programming representation of the proposed model is derived. Numerical examples under four scenarios are presented to highlight the characteristics of the proposed model. In particular, the positive effects of project divisibility are shown for the first time.

Expanded model of the project portfolio selection problem with divisibility, time profile factors and cardinality constraints

This paper develops a new model for project portfolio selection over a planning horizon with multiple time periods. The model considers the divisibility of projects and combines reinvestment, set-up cost, cardinality constraints and precedence relationship in the scheduling, simultaneously. For efficient computation, an equivalent mixed integer linear programming representation is provided. One numerical example with three scenarios is given to highlight the capability and characteristics of the proposed model.

A scenario based project portfolio selection

Article history: Received March 25, 2015 Received in revised format June 1 2015 Accepted June 17 2015 Available online June 18 2015 One of the primary assumptions in many project portfolio selection is the availability of all parameters. However, in real-world cases, many parameters are under uncertainty and the exact values are unknown in advance. This paper presents a scenario based mathematical model for project portfolio selection when parameters are under uncertainty. The problem considers two objective functions where the first one maximizes the net present value while the second objective function is the minimization of the positive deviations from the allocation of resources. The second objective function is looking for project resource leveling. The resulted model is formulated as mixed integer programming and the problem is analyzed under different conditions. Growing Science Ltd. All rights reserved. 5 © 201

Project portfolio selection under uncertainty with outsourcing opportunities

The paper presents a stochastic optimization model for project portfolio selection under uncertainty about the real efforts required for the execution of the work packages contained in the projects. As a subproblem, the assignment of the work to human resources and the distribution of work over time is addressed. The available workforce is assumed as multi-skilled. Required efforts are modeled as random variables. The recourse action for the case where the available capacities of the internal human resources do not suffice to cover the actual work times consists in delegating parts of the work to external human resources. The staffing-and-scheduling subproblem is solved by means of a Frank-Wolfe type algorithm. To solve the upper-level problem of project portfolio determination, a modification of the Variable Neighborhood Search (VNS) algorithm is applied. Experimental results for a benchmark of synthetically generated test instances and for an illustrative example from the E-Commerce Competence Center Austria are provided.

An Integer Linear Programming Model for Project Portfolio Selection in a Community

Project portfolio selection involves decision making and it plays a crucial role in any organization. Therefore selecting not just the right projects but also the right mix of projects for the portfolio is considered as one of the most important tasks for organisations to ensure the achievement of the corporate strategy within limited resources and capabilities of the organization. Prioritizing and selecting optimal project portfolio can be very challenging especially with a large number of projects with multiple constraints and interdependences. In an ideal world with unlimited budget the project selection process would be very straightforward. However, this is not the case in life situations. In this work, an attempt is made to address this challenge. An integer linear programming model for project selection was developed and applied in a selected organization in Nigeria. The model seeks to optimize the mix of the projects to be undertaken while keeping the total cost and project interdependency as constraints. The analysis of the results showed that a total of 11 projects out of 16 were eligible for selection in the period under review. The total cost of the selected project was 92,840,000 Naira, which was about 90% of the total budget. Ordinarily, apart from not prioritizing and obtaining an optimal project mix, the community would have spread its entire resources on the 16 projects with some of them being abandoned later. The model can also be used to plan an optimal mix of project portfolio for a future date within the limitations of a given set of constraints and interdependence.

Project portfolio selection model, a realistic approach

AbstractToday, more and more companies are moving towards a project‐oriented way of managing their businesses. It sets certain challenges to companies. How to make sure that the selected projects are implementing the strategy of the company? Are the scarce resources (financial, human capital) allocated to the right projects? Which projects to select, which projects to pursue and which to kill? Project portfolio management tackles these problems and has become an important topic in recent years. Nevertheless, different empirical studies highlighted the fact that these models are not used in practice. We propose an approach based on the generation of efficient portfolios by the metaheuristic SSPMO1 and on an “objective” analysis of these portfolios in order to make good recommendation to the portfolio committee or the decision makers.

Multi-objective decision analysis for competence-oriented project portfolio selection

This paper develops a multi-objective optimization model for project portfolio selection taking employee competencies and their evolution into account. The objectives can include economic gains as well as gains expressed in terms of aggregated competence increments according to pre-defined profiles. In order to determine Pareto-optimal solutions, the overall problem is decomposed into a master problem addressing the portfolio selection itself, and a slave problem dealing with a suitable assignment of personnel to the work packages of the selected projects over time. We provide an asymptotic approximation of the problem by a linearized formulation, which allows an efficient and exact solution of the slave problem. For the solution of the master problem, we compare the multi-objective metaheuristics NSGA-II and P-ACO. Experimental results both for synthetically generated test instances and for real-world test instances, based on an application case from the E-Commerce Competence Center Austria, are presented.

A new approach for selecting portfolio of new product development projects

The most decisive factor that survives enterprises under stiff competition is the development of new product (NPD), and when entering the product development stage after the fuzzy front end, a best project portfolio should be finalized in order to potentially create expected revenue and competitive advantage. However, even it reaches the end of the fuzzy front stage; the NPD project is still significantly involved with uncertainties, complexities and fuzziness. To assist R&D managers making decision in this environment, this study proposes a new approach which combines fuzzy set theory and multi-criteria group decision making method into a NPD project portfolio selection model. This model takes into account project performance, project delivery and project risk, and formulates the selection decision of NPD project portfolio as a fuzzy linear programming problem. The illustrative example shows that the model proposed can generate projects with the highest success rate under limited resources and manpower.

Competence-driven project portfolio selection, scheduling and staff assignment

This paper presents a new model for project portfolio selection, paying specific attention to competence development. The model seeks to maximize a weighted average of economic gains from projects and strategic gains from the increment of desirable competencies. As a sub-problem, scheduling and staff assignment for a candidate set of selected projects must also be optimized. We provide a nonlinear mixed-integer program formulation for the overall problem, and then propose heuristic solution techniques composed of (1) a greedy heuristic for the scheduling and staff assignment part, and (2) two (alternative) metaheuristics for the project selection part. The paper outlines experimental results on a real-world application provided by the E-Commerce Competence Center Austria and, for a slightly simplified instance, presents comparisons with the exact solution computed by CPLEX.

A multi-objective model for project portfolio selection to implement lean and Six Sigma concepts

Lean and Six Sigma methodologies play an important role in the quality and cost driven world with high competition. In this paper, we develop a unique decision support system that utilizes a multi-objective formulation for project portfolio selection problem in manufacturing companies. The model can be used to effectively implement Lean and Six Sigma concepts. An industrial case study that utilizes this model for implementing the Lean and Six Sigma initiatives is also presented.

Competence-driven project portfolio selection, scheduling and staff assignment

This paper presents a new model for project portfolio selection, paying specific attention to competence development. The model seeks to maximize a weighted average of economic gains from projects and strategic gains from the increment of desirable competencies. As a sub-problem, scheduling and staff assignment for a candidate set of selected projects must also be optimized. We provide a nonlinear mixed-integer program formulation for the overall problem, and then propose heuristic solution techniques composed of (1) a greedy heuristic for the scheduling and staff assignment part, and (2) two (alternative) metaheuristics for the project selection part. The paper outlines experimental results on a real-world application provided by the E-Commerce Competence Center Austria and, for a slightly simplified instance, presents comparisons with the exact solution computed by CPLEX.