Optimal Project Portfolio Research Articles

Project portfolio selection with relationship considerations: A random walk with restart based on influencing factors

Selecting an optimal project portfolio (PP) is a critical yet challenging task for organizations in an increasingly diverse and complex environment. The dynamic and holistic impacts of diversity and complexity on PP strategic goals help to reflect the complex realities of project portfolio selection (PPS). However, traditional research on PPS fails to adequately consider the diversity of influencing factors and the complexity of relationships among projects, among influencing factors, as well as between projects and influencing factors. To address the gaps, a novel PPS methodology was proposed by integrating a two-layer network model with a random walk with restart algorithm. First, a two-layer network containing a project layer and an influencing factor layer was constructed to portray the impacts of factors on the implementation of PP. Second, a random walk with restart was employed to capture the changes of project contribution to the goal under the complex synergy relationships among projects, among influencing factors, and between influencing factors and projects. Finally, a detailed case study verified the viability of the proposed method. The main findings of this study were in two aspects: (1) it was necessary to consider complex relationships for PPS under uncertain and dynamic environments. (2) the proposed method enabled better alignment of selected PP with strategic goals by incorporating these complex interdependencies. This study theoretically facilitates the PPS with the consideration of integrated three complex relationships that exist in PP. Practically, this study offers actionable tools directly applied to improve strategic decision-making in complex project environments.

A simheuristic for project portfolio optimization combining individual project risk, scheduling effects, interruptions, and project risk correlations

This paper introduces a simheuristic method to the Project Portfolio Selection Problem, designed to maximize the net present value of the portfolio while considering uncertain costs, schedules, interruptions, and inter-project risk correlations. The novel approach combines techniques from Monte Carlo simulation, critical path analysis, queuing theory, and optimization, integrating baseline schedules, project-level uncertainties, budgetary constraints, and risk correlations in a single model. A computational experiment is conducted on a realistic set of ten candidate projects and validated respect to the deterministic version of the problem, demonstrating its ability to select near optimal portfolio proposals with varying combinations of risk and net present value. The findings highlight the significant impact of factors such as contingency reserve allocation policies, operational interruptions, and project risk correlations on portfolio decisions, constituting a helpful framework for the decision-makers at portfolio level.

Optimization of renewable energy project portfolio selection using hybrid AIS-AFS algorithm in an international case study

In the coming decade, as restrictions on fossil fuel usage become more stringent, investment in renewable energy projects presents an increasingly appealing opportunity. Evaluating investment attractiveness involves considering both profitability and investment risk. This study proposes a multi-objective mathematical model for identifying the optimal Renewable Energy Project Portfolio (REPP), aiming to maximize net present value while minimizing investment risk. The key innovation of this model is its incorporation of project lifetime and workforce employment considerations to discern the best REPP. To optimize the objective functions of this mathematical model, a hybrid meta-heuristic algorithm combining Artificial Immune System (AIS) and Artificial Fish Swarm (AFS) algorithms is introduced. Genuine data from a varied spectrum of renewable energy projects spanning 20 countries has been meticulously collected. The proposed model is optimized using this dataset, considering portfolio sizes of 3, 5, 10, and 15. The numerical results indicate that, at a specific investment risk threshold, the proposed hybrid algorithm outperforms both AIS and AFS in terms of profitability. Furthermore, the assessment of the geographical distribution of selected projects reveals a deliberate effort to avoid concentration in any specific region, demonstrating a commitment to identifying optimal investment opportunities globally. This research advances the understanding of renewable energy project portfolio optimization, providing valuable insights for investors, policymakers, and sustainable development practitioners.

Optimizing Enterprise Marketing Project Portfolios Using Fuzzy Ant Colony Optimization

Enterprise marketing project portfolio optimization is important for business competitiveness, but involves multiple uncertain factors. An integrated model using fuzzy rules was proposed in this paper to enhance ant colony optimization. The fuzzy ant colony algorithm effectively handled ambiguity in project costs, returns, and risks when selecting an optimal portfolio of marketing initiatives. Experiments demonstrated the algorithm efficiency in converging towards high-quality solutions. Case studies indicated the model helped boost customer loyalty and profits through tailored marketing strategies, outperforming conventional approaches. The fuzzy optimization provides an effective decision-making framework for enterprises to maximize marketing effectiveness.

RETRACTED: A comparative study of fuzzy multi-objective investment project portfolio selection and optimization based on financial return and different risk measurements

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.

Optimal Project Portfolio Selection Considering Cascading Failure Among Projects

A novel model is proposed for optimal project portfolio selection (PPS). Different from previous PPS methods that consider the interdependence, interaction, or synergy relationships among projects, this article considers the cascading failure process caused by these relationships. Specifically, we first construct a PP network (PPN) and determine the initial load and capacity of the projects (nodes). Afterward, we simulate the cascading failure process by attacking one or more projects in the PP and distribute the load of the failed projects to nonfailed neighboring projects. The cascading failure process ends until no project fails. Finally, we propose a new indicator to measure and compare each PPN's cascading failure results, based on which the optimal PP is selected. A numerical experiment is conducted to depict the PPS process. The results of this study provide guiding implications for project portfolio management decision-makers, helping them consider PPS problems from a new perspective and make more reasonable decisions.

Project portfolio selection and scheduling incorporating dynamic synergy

PurposeSelecting and scheduling optimal project portfolio simultaneously is a complex decision-making problem faced by organizations to realize the strategy. However, dynamic synergy relationships among projects complicate this problem. This study aims at constructing a project portfolio selection and scheduling (PPSS) model while quantifying the dynamic synergetic effects to provide decision support for managing PPSS problems.Design/methodology/approachThis study develops a mathematical model for PPSS with the objective of maximal project portfolio benefits (PPBs). To make the results align with the strategy, comprehensive PPBs are divided into financial and non-financial aspects based on the balanced scorecard. Then, synergy benefits evolve dynamically in the time horizon, and system dynamics is employed to quantify them. Lastly, a case example is conducted to verify the applicability of the proposed model.FindingsThe proposed model is an applicable model for PPSS while incorporating dynamic synergy. It can help project managers obtain the results that which project should be selected and when it should start while achieving optimal PPBs.Originality/valueThis study complements prior PPSS research in two aspects. First, financial and non-financial PPBs are designed as new criteria for PPSS, making the results follow the strategy. Second, this study illuminates the dynamic characteristic of synergy and quantifies the synergetic effect. The proposed model provides insights into managing a PPSS effectively.

Research on Timing Sequence Update Strategy Decision of Project Portfolio Based on Coupling Benefits in Strategic Period

The Chinese government’s substantial investment in water restoration has created numerous lucrative opportunities for commercial environmental restoration enterprises. Accordingly, this research study seeks to address the primary challenge faced by enterprise managers: selecting projects that fulfill both strategic imperatives and maximize economic returns. To tackle this issue, we segmented the overarching strategic period into multiple phases and studied the project portfolio from a holistic strategic period perspective. We introduced a decision model for the dynamic, sequential updating of the portfolio throughout the strategic period, emphasizing the combined benefits at each phase. This model guides the strategic selection of projects at any decision-making stage to optimize the benefits across the entire strategic duration. The multi-agent Nash Q-learning algorithm was employed for portfolio construction and updating strategies. This approach yields an optimal project portfolio for each phase of the strategy. Unlike traditional methods that predominantly focus on cumulative returns and find it challenging to accommodate strategic shifts, our proposed technique prioritizes intertwined strategic returns. It promotes project choices in tune with strategic contexts and supports ongoing adjustments to project strategies, providing invaluable guidance for decision makers. A comparison of our proposed method with other optimization strategies validated its superior performance. Furthermore, the case study described in this study confirms that our method promotes project choices in tune with strategic contexts and supports ongoing adjustments to project strategies, providing invaluable guidance for decision makers.

A Multistep Iterative Ranking Learning Method for Optimal Project Portfolio Planning of Smart Grid

Optimal project portfolio planning is a typical nonconvex, multiobjective, highly constrained, multitemporal coupling, and combinatorial optimization problem. This paper proposes a novel multistep iterative ranking learning method (MIRL) to solve this complex combinatorial optimization problem from massive infrastructure projects of smart grid. The optimal project portfolio planning problem of power grid is formulated as the optimization process of massive project priority sorting with an improved knapsack model. The proposed method dynamically optimizes the best infrastructure project combination for each round to maximize the economic, social, and security benefits without exceeding the annual investment limit. A pairwise-based ranking learning algorithm is used to mine the priority sorting law from massive historical combination data of power grid to initialize candidate project portfolio. In order to approach the optimal portfolio planning solution with the constraint satisfactions of project construction duration and electric load supplies, a heuristic greedy strategy is designed to search the solution dynamically for selecting the project having highest construction benefits iteratively. The effectiveness of the proposed method is proved by experiments with real-world project data of Hunan power grid in China, and experimental results show that the proposed MIRL can outperform other methods on investment efficiency, calculation time, and rationality of project construction period schedule.

Multi-criteria decision making and project portfolio management for transport infrastructure investment in Thailand

Transport project selection is always critical in government planning due to limited resources and diverse demands. Project portfolio management (PPM) was proposed in this study to suggest an optimal set of projects under predetermined constraints. With multiple conflicting criteria, comprehensive steps of multi-criteria decision making (MCDM) procedures were proposed in this study. The process begins with a step of pre-screening the projects and selecting the criteria for project evaluation. Sequential steps of: 1) weight determination process using the proportion method; 2) MCDM process using preference ranking organisation method for enrichment evaluations (PROMETHEE) II; 3) constraint consideration process using PROMETHEE V, was proposed to evaluate and select an optimal project portfolio. The study showed that decision making could be successfully done for the transport infrastructure investment with minimal complex calculation steps and questionnaire input from experts.

A bi-level multi-follower optimization model for R&D project portfolio: an application to a pharmaceutical holding company.

The online version contains supplementary material available at 10.1007/s10479-022-05052-0.

The Optimal Project Portfolio: Career Consequences of Content and Collaboration Specialization

Research on careers often focuses on understanding the sequence of jobs that people move through. In project-based organizations, though, different career trajectories tend to reflect differences in the kinds of projects that people work on over time. In this paper we explore two aspects of the project portfolios that people assemble – variety in the content that they involve and variety in the collaborators worked with. Drawing on theories of human and social capital and careers, we propose that increased diversification in both the kinds of projects that an employee works on and the collaborators that they work with are likely to lead to faster promotion. We also suggest that the effects of increased content diversity and collaborator diversity are likely to offset each other, so that the benefits of content diversity are less when employees work with a greater variety of collaborators. We explore this question using project data from a services organization. We leverage variation in client demand as an exogenous source of variation in project portfolios to generate instruments for project variety. We find that increased diversification in content and collaborators is associated with increased promotion and that their effects do offset each other. We also find strong non-linearities in the effects, as increases in content diversity show strongly diminishing returns, while returns from collaborator diversity are highest for those who have the most collaborators.

Sustainable project portfolio selection and optimization with considerations of outsourcing decisions, financing options and staff assignment under interval type-2 fuzzy uncertainty

Sustainable project portfolio selection and optimization with considerations of outsourcing decisions, financing options and staff assignment under interval type-2 fuzzy uncertainty

The method of forming an optimal portfolio of investment projects

The method of forming an optimal portfolio of investment projects

Method for solving the multi-criteria non-Markov problem of project portfolio optimization

The subject of the study in this paper is models and methods of optimization of the organization's project portfolio for the planning period, considering the effects of the previously made decisions. Project portfolio optimization is one of the responsible and complex tasks by company's top management solves. Based on the analysis of the known works in the field, the research purpose is described: to create a method that would allow solve the problem of multi-criteria project portfolio optimization for the planned period, considering the aftereffects of the previously made decisions. The research tasks are to enhance the method for solving the project portfolio optimization problem in terms of maximizing the difference between income and costs for all projects started during the planned period; to propose a method for solving the project portfolio optimization problem in terms of the social effects of projects that started during the planned period; create a method for solving the problem of project portfolio optimization for the planned period in a multi-criteria setting. There are the following results obtained in the paper. There is presents the mathematical model of the problem being solved, the problem objective functions include the difference between the receipt and expenditure of funds in time, the portfolio risks, and its implementation social effects. The mathematical model considers the provision of funds sufficiency for the implementation of projects in all periods, the required sequence of project implementation, and the mandatory inclusion of some projects in the portfolio for a given period. The problem under consideration belongs to the multi-criteria non-Markov dynamic discrete optimization problems. There is a proposed method for solving it in a multi-criteria formulation. The method is based on solving one criterion problem, and then a multi-criteria problem. The method is based on the minimax approach and implicit search. There has been developed solving method for the problem of enterprise project portfolio optimization for the planned period following the profit criterion. In contrast to the existing methods, this method considers the constraints on debt absence and the aftereffects of the previously made decisions. The method served as the basis for creating risk and social effect optimization methods. A method for enterprise project portfolio optimization of the planned period is provided, which, unlike previous, considers the criteria of profit, risks, and social effect, the constraints on debt absence, and the aftereffect of the previously made decisions. That makes it possible to improve the quality of the generated portfolio.

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.

A methodology for strategy-oriented project portfolio selection taking dynamic synergy into considerations

The selection of an optimal project portfolio from multiple project proposals to implement management strategy is always a challenge task for project managers, especially, in the selection of large-scale and complicated projects. This is particular true because project portfolio selection decisions have to be made based on complicated evolution, comprehensive strategic criteria and dynamic synergies. This paper presents a proposed methodology of system dynamic model with consideration of dynamic synergies to predict the value of strategic realization through project portfolio implementation. This method can be applied in the project portfolio selection process, which consists of three procedures: project elimination by resource constraints, project functional value determination and system dynamics approach modelling simulation. In this case, dynamic synergy considerations can help to produce more rational selection results while strategy-oriented selection can ensure that the selected project portfolio aligns with a company’s strategy. A case study is used to demonstrate the application of the proposed methodology. The results show that the proposed method can help project managers to select an optimal project portfolio with maximal strategic criteria. The proposed method can be incorporated into expert systems in the organizations to enhance the organizational objective priorities in the decision-making process.

Optimum Project Portfolio Selection for Developmental Plans Using Goal Programming

In vision-driven development plans, such as the Kuwait Mid-Range Development Plan 2015/2016–2019/2020, themes and pillars are derived from the plan's vision, and global indices are assigned by international organizations to accurately measure the performance against the vision's themes. This allows for comparison with other countries, and it also set targets for progression over time. One or more projects are assigned to the indicators of these global indices. A Multi-Criteria Mathematical Programming Technique (e.g., Goal Programming) is used with multiple goals and priorities where an Optimal Portfolio of Projects is found that satisfied the selection criteria.

Optimum Project Portfolio Selection for Developmental Plans Using Goal Programming

In vision-driven development plans, such as the Kuwait Mid-Range Development Plan 2015/2016–2019/2020, themes and pillars are derived from the plan's vision, and global indices are assigned by international organizations to accurately measure the performance against the vision's themes. This allows for comparison with other countries, and it also set targets for progression over time. One or more projects are assigned to the indicators of these global indices. A Multi-Criteria Mathematical Programming Technique (e.g., Goal Programming) is used with multiple goals and priorities where an Optimal Portfolio of Projects is found that satisfied the selection criteria.

Application of Interaction Effect Multichoice Goal Programming in Project Portfolio Analysis

How to choose the optimal project portfolio based on the enterprise objectives is very important for the survival and development of an enterprise. However, the current portfolio analysis needs to be improved in the aspects of interaction effect analysis, multichoice target planning, and method simplicity and practicability. The method of portfolio selection proposed in this study takes into consideration the complex interaction within the portfolio, which makes up for the deficiency of the existing research on portfolio selection. This paper illustrates the application of the multichoice object programming method in the analysis of the combination of two-project and three-project interaction effects by a series of examples. The results show that the method is applicable in portfolio evaluation. The method of multichoice goal programming of the interaction effect provides the basis for enterprise to measure the complex relationship within the portfolio and also provides a new method and idea for the selection of the enterprise project portfolio.