project portfolio

Diversifying investment portfolios by incorporating a variety of assets is a well-established strategy for mitigating risk and enhancing returns. Traditionally, mathematical models for portfolio optimization have primarily focused on stock investments within the capital market. However, this study extends the scope of portfolio optimization to encompass both project and stock investments. This is a critical advancement as investors increasingly grapple with allocating budgets across these two asset types simultaneously. Therefore, this paper proposes a novel mixed portfolio optimization model that uses the Mean-SemiVariance-SemiEntropy approach. By incorporating project investments alongside traditional stocks, the proposed model offers more efficient portfolios that can lead to improved return/risk ratios for investors seeking to optimize their overall financial strategy.

Design/methodology/approach:

 An attempt has been made to bridge the gap between the distinct spaces of projects and stocks to facilitate their joint analysis. Subsequently, a Mean-SemiVariance-SemiEntropy approach has been employed to develop a model within a probabilistic framework. For validating this model, a numerical experiment involving three projects and five stocks from the capital market has been tackled, considering the preferences of an investor. Finally, the optimization problem has been solved using three metaheuristic algorithms: Genetic Algorithm (GA), Imperialist Competitive Algorithm (ICA), and Gray Wolfs Optimization (GWO).

The results obtained by solving the model using the above-mentioned metaheuristic algorithms demonstrated that despite the high speed of the GWO algorithm, the solutions provided by the GWO algorithm were not satisfactory compared to the GA and ICA algorithms. On the other hand, the acceptable speed with nondominated solutions was the advantage of the ICA algorithm over the GA algorithm. The evaluation of various performance metrics also revealed that the ICA algorithm outperformed the GA and GWO algorithms in this problem. Also, the inclusion of semi-entropy as a risk assessment metric led to an improvement in the return on the investment portfolios.

Research limitations/implications: 

Incorporating investor constraints and preferences, such as cardinality and boundary constraints, into the model forms an NP-hard problem. Consequently, exact solution methods are replaced by non-exact methods, such as metaheuristic algorithms. Given the diversity of project contracts, this study concentrated solely on projects with cost-plus contracts, where the entire project or a portion can be selected for partnership. Similar to the Markowitz model, the projects' returns such as stocks' returns were assumed to be normally distributed.Practical implications: This study significantly enhanced diversification, increased potential returns, and reduced risk for investors by introducing a novel mixed project-and-stock portfolio optimization model. The proposed approach can be implemented by a wide range of investors and managers of investment units in various organizations, bringing a new perspective to investment management.

Social implications: 

The far-reaching implications of this study extend beyond the realm of investment management, permeating social, economic, and political areas. The innovative mixed project-and-stock portfolio problem has the potential to positively transform society using fostering innovation, stimulating economic growth, and enhancing financial knowledge. This paper can foster economic growth and job creation by providing new investment opportunities and increasing investment in productive ventures. In summary, this study has taken a significant step towards improving social and economic well-being by introducing an innovative model for resolving investment challenges.

Originality/value:

 The innovation and strength of this research lies in incorporating projects as a new asset class into the traditional portfolio model. This goes beyond simply adding a new asset to an investment portfolio, as the nature of the projects introduces new complexities to the portfolio management process. For this purpose, this study employs a probabilistic approach based on historical data. In addition, the simultaneous use of two risk measures, i.e., semi-variance and semi-entropy, significantly improves the performance of the model by focusing on different risk aspects. This provides a more comprehensive picture of the risks associated with the portfolio and helps investors make more informed and wise decisions.

As project portfolio strategic planning (PPSP) is a strategic action in project-based organizations, its execution needs to be followed by structured stages. Via identifying the effective elements on (PPSP), especially the missed elements in previous models, the present study aimed to propose a step-based framework for execution.

This research utilized thematic analysis to identify the related themes from the literature. The themes were then analyzed by forming a thematic network. Next, by conducting interviews with 12 selected experts, using the snowball method, the obtained findings were reviewed and verified by the Delphi process. A framework for PPSP was developed based on the organizing themes and again verified by experts, repeating the Delphi process. The process was eventually completed by presenting a framework.

This study identified 12 basic themes, 4 organizing themes, and one global theme for PPSP. Based on the organizing themes, key elements of PPSP included: 1) Necessity of scenario-based approach in PPSP, 2) Determining divergent visions for future of portfolios, 3) Drawing the portfolio changes path from past to present and future in the form of a roadmap, and 4) Monitoring and analyzing both changes in scenarios and portfolio status simultaneously as a basis for portfolio preplanning.

Based on the key elements of PPSP and other findings of this research a "conceptual", "process-based" and "descriptive-prescriptive" framework for PPSP was offered and its conduction was explained in six steps: 1) Documenting project portfolio history, 2) Documenting project portfolio current status, 3) Determining project portfolio visions in different scenarios, 4) Preparing a roadmap to move from current status towards vision status, 5) Assessing realization of scenarios, and 6) Monitoring, analyzing, and preplanning the portfolio.

The aim of this study was to design a new fuzzy expert system for project portfolio risk management in ongoing projects of a first mobile holding.

To do this; To identify the risks from the Dematel method developed in Excel, to evaluate and determine the most important risks from the fuzzy FMEA methods in Excel and the AHP method in Expert Choice software; And to select the optimal risk response, a proposed mathematical model is used and solved in Gamz software.

The research findings showed that there are 23 risks in the studied projects, 8 of which were of high importance and impact. Also, risk prioritization and determination of their impact level was performed using the integrated AHP-FMEA method. Findings obtained from solving and implementing the mathematical model in Gamz software also showed that the proposed model of efficiency and capability for selecting optimal risk response strategies is established.

Originality/Value:

The general findings indicate that the proposed new fuzzy expert system also has the necessary capability and efficiency to manage the portfolio risk in the study holding.

Many organizations and companies are looking for solutions to create regular and formal processes for project management, which leads to more organization of the organization in conducting current projects in terms of time, budget and quality level. Description of tasks and goals can be based on the needs and characteristics of a variety of organizations, from setup and deployment project management office was expected. The most important expectation from the project management office is to create regular and integrated processes for project management in the form of a project management unit's methodology. The present research also using the research done to identify and explain the dimensions which is the organizations guide for the use of project management offices. Dimensions are identified as requirements for the implementation of the project management office project model. In this research, the importance and necessity of the project management office firstly state and after express theoretical and conceptual models project management office, project management office structure will be described. Then the conceptual model of the research presents and the elements of the conceptual model will be explained. Research data was collected through library and online studies and for confirmation of the model, a questionnaire of the Likert spectrum was surveyed from the organization's experts. In order to analyze the data, One of the military research organizations has been selected as the case study. Using Spss software, first the assumption of normal variables was tested using the Kolmogorov-Smirnov test and then using the T-Student test, the status of each of the components of the project management office was evaluated. The approved components of the project management office presented with the three main components of planning (project portfolio), delivery (Plan and project) and the center of excellence and in the form of 17 sub-components.

The project portfolio problem proceeds to choose a subset among the proposed projects in organization. One of the purposes of project portfolio management is the reduction of the available risks in portfolio selection process. In real world, the project risks are rarely independent, and generally have a degree of interaction. By considering this interaction the more accurate evaluation of portfolio selection can be achieved. The purpose of this study is to provide a framework for selecting project portfolios by considering the triple interaction between projects for the first time using the correlation coefficient and the theory of cross-information for the risk factors affecting them.

 First, this research identifies the most critical risks based on the literature review, project management body of knowledge and the relevant experts' opinions. Then, a questionnaire was distributed among 30 experts to quantify the identified risk criteria. The identified risks that are included 11 risks are in the categories of “technical”, “external”, “organizational” and “project management”. Then theses risks are placed in to the “cost”, “time” and “quality” classification. By considering the individual project risk, the projects which their risks were higher than the allowable limit, were not able to compete in the portfolio. Also, the pairwise and triple-wise interaction of the projects based on effective criteria caused that the projects which provoke risks synergies in the portfolio to be prevented from being together. By analyzing risks, the pairwise interaction effects between projects are calculated using Spearman's correlation coefficient and for the first time the triple interaction effects between projects are calculated using theory of cross-information to discover more precisely the effect of factors on each other. Finally, the modeling in the form of a case study based on integer linear programming along with two goals, “maximize IRR” and “maximize the number of projects in the portfolio”, are accurately assessed to evaluate the research validity.

The problem of project portfolio is the selection of a subset of proposed projects according to the strategic goals of the organization and the associated constraints. This paper provides a framework for selecting project portfolios by considering the triple interaction between projects for the first time. The results show that considering pairwise and triple-wise interaction instead of individual pairwise interaction comprise different answers. This result leads to discovering the relationship which can not be identified just with the pairwise interaction.

In this research the risk criteria are considered generally, while, in specific projects it can be used specific criteria. Using the meta-heuristic methods when the problem dimensions are increased is another area for future research. In addition, fuzzy methods and stochastic subject in order to quantify the risk criteria could be considered. Besides, this paper considers risks with negative aspect for project portfolio selection, while, in future research suggested that the risks with aspect of the positive are considered as well.

 One of the most important application of this paper is for project-oriented companies that are involved in doing multiple projects simultaneously. The results of this paper are useful for these companies for risk reduction in selecting and performing their projects.

This paper considers the most important risks affecting project execution. These risks include external risks of organization as well as internal and technical risks of organization. So, the paper helps organizations for the most appropriate selection of projects that could have benefits for organization and also society.

 In the literature, there are various methods that researchers have considered for risk management. Most of these studies don’t consider interactions between projects or simply they considered only pairwise interaction. The main purpose of this study is to consider a higher level of interactions that is triple interactions that not considered in the literature. The results show that considering triple-wise interaction instead of individual pairwise interaction comprise different and better results.

Many organizations and companies are looking for solutions to create regular and formal processes for project management that leads to higher discipline in conducting current projects in terms of time, budget, and quality level. Task description and various goals can be expected based on the needs and characteristics of an organizations, from setup to the project management office deployment. The most important expectation from the project management office is to create regular and integrated processes for project management in the form of a unified project management structure. Accordingly, the present study, using the previous research, aims to identify and explain the dimensions that guide organizations in using project management offices. The identified dimensions are presented as the requirements for the implementation of the project management office project model. In this research, the importance and necessity of the project management office was firstly stated and after expressing theoretical and conceptual models of the project management office, the project management office structure was described. The conceptual model of the research was presented and the elements of the conceptual model was identified. The Likert-scale questionnaire was composed using library and online studies, and was confirmed by the organization experts. To analyze the data, a military research organizations was selected as the case study. Using SPSS software, first the assumption of normal variables was tested using the Kolmogorov-Smirnov test and then using the t-student’s test, the status of each of the components of the project management office was evaluated. The approved components of the project management office presented in three main components of planning (project portfolio), delivery (plan and project) and the center of excellence in the form of 17 sub-components.

Correct selection of projects is one of the most important issues of project-oriented organizations for strategic management and resource constraints make organizations unable to invest on all projects. Therefore, it is very important to select the portfolio of projects to be implemented by the organization or the portfolio of projects that can meet the organization's goals and strategies in terms of organizational constraints. To accomplish this, and in order to make a more precise decision, the present study has used three stages of screening to form the project portfolio. In the first stage, projects has been prioritized based on criteria for project selection and using fuzzy multi-criteria decision-making techniques. Since the highest costs of each organization are due to project risk and late completion and may also be a priority project, but it is for the organization to have a high degree of risk and costs, so in the second stage, the screening Projects was conducted based on project risk based on the PMBOK standard using fuzzy clustering. Based on clustering results, the projects were identified in three clusters. The first cluster is projects that have a reasonable risk; second cluster have a high degree of risk, which requires special attention to senior management when needed, and the third cluster is critical risk projects that, if necessary, requires specific planning. In the next step, the experts assigned projects to companies according to the scope of each company's activities. In the final stage of the screening, using zero and one goal programming and taking into account 3ideal constraints of "profit", "budget" and "risk" projects were selected in each of the subsidiary companies

The key role of public infrastructure projects in promoting GDP growth and country development achievement and also huge amount of investment needed for them, has multipled the importance of effective and efficient implementation of them. Successful management and desirable value creation of these projects aligned with public interests, depends on good governance of multiple projects level named “project portfolio”. Utilizing the principles of good governance at this level leads to portfolio performance improvement and results such as optimum projects resource allocation aligned with the organization's strategic requirements. But applying such a governance in the complex public context, faces more barriers than private sector. Identifying and analyzing these barriers, pave the way for the design of appropriate governance models. To accomplish this, 18 experts with 15 to 25 years experience in key positions influencing on infrastructure portfolios governance were interviewed whom selected by purposeful sampling. This qualitative study is a practical-developmental research done with phenomenological methodology. Required data were collected through semi-structured interviews and then analysed. According to the findings, the barriers to road infrastructure project portfolios good governance categorized in five groups including: behavioral barriers, organizational barriers, credit barriers, macro structural barriers and macro strategic barriers drawn into a pattern of three-tier structure.

In this paper, the problem of selecting and scheduling projects regarding the maximum amount of resource constraints and the net present value of the project is modeled, analyzed and solved. This study shows the condition in which some projects should be performed among all available projects. Decision makers should choose a subset of these projects based on resource constraints in order to maximize final profit. Project selection models usually do not consider project scheduling as a part of selection process. However, except in cases where only one project is active at any time during the project, prioritizing selected projects regardless of their schedule is not optimal. Project scheduling in the level of project activities, increases the complexity of search space and spreads the decision space for selecting a portfolio of projects. Hence, in this study a particle swarm optimization algorithm is developed for solving large scale problems. Finally, in order to validate the quality of the proposed meta-heuristic approach, several test problems have been solved in small and large sizes. The results show the good performance of the presented algorithm.

The purpose of gas portfolio selection is to choose a collection of projects from a number of proposal projects, so that the organization’s desired factors could be improved. In this paper such a selection encounters critical problem. Having in mind the ambiguity which exists in determining some of the parameters of the research, they are viewed in terms of fuzzy numbers. In addition, Fuzzy Robust method has been used to escalate the robustness of the responses. The results of Fuzzy Robust method indicate that the alpha is applicable and robust for all the levels of the cut. In this paper, Fuzzy Robust zero-one multi objective - multi period model (FRMOILP) is used to select gas projects portfolios in the Gas Company of Kerman Province which follows with fuzzy robust approaches for solving model. At first, small-size single-objective model is solved with Lingo software in order to show how “fuzzy robust approaches” work. Because of the NP-Hard nature of the issue, Multi Objective Differential Evolutionary Algorithm (MODE) algorithm was applied to code and solve the problem. Subsequently “multi objective tabu search” (MOTS) algorithm was compared to it in terms of performance. Finally, in order to facilitate gas projects portfolio selection process, the TOPSIS technique was exploited to prioritize Pareto solutions.