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Top1. Introduction
With the highly competitive and fast-moving economy, managing effective portfolio selection activities is important to maintain and achieve competitiveness for any organization. Project portfolio selection is resource-intensive. Hence, there are costs and benefits related to each project that must be considered. Typically, organizations have insufficient resources to support all of the projects. Thus, selecting a subset of projects as a portfolio is the goal for organizations, which is normally based on one or more criteria depending on organizations’ or decision makers’ perspectives.
Given these uncertainties and constraints, decision-makers must build a portfolio of suitable projects with multiple criteria that represent corporate objectives. Risk minimization, return maximization, and market-share maximization are common objectives for such selections. Although financial considerations are important for business, society often has other expectations. Despite addressing sustainability in project portfolio selection problems, sustainability criteria are still not addressed comprehensively. Especially, the criteria have not been adapted for the environment of project management yet (Mohagheghi et al., 2019). As a result, there are uncertainties in the financial environment since the conflicting objectives can be confronted by decision-makers. These can raise the complexity of the problem of project portfolio selection in the future. Finding the ways to choose the best feasible set of projects under multiple objectives, resource limitations, and inherent uncertainties subject to the decision makers’ perspectives is very challenging.
This study attempts to overcome the above-mentioned drawbacks by presenting the most favorable choice of sustainable project portfolio investment under uncertain conditions to comply with all financial returns, financial risks, and environmental sustainability constraints. Through fuzzy chance-constrained programming, the obtained portfolio can be optimized with a specified confidence level imposed by decision-makers. Then, the investment return, investment risk, and sustainability are simultaneously evaluated by fuzzy multi-objective chance-constrained portfolio optimization. Consequently, there are four key contributions to this study.
First, an integrated algorithm is developed to optimize multi-objective portfolio problems in an uncertain environment. Second, the approach considers all investment issues from financial and risk aspects regarding sustainability. The financial return is measured by the net present value of an investment portfolio, which is one of the most important indicators in the financial investment study. The credibility and credibilistic risk indexes are important risk indexes to measure the risk of uncertainty. While the credibility index measures the possibility to occur, the credibilistic risk index, on the other hand, measures the possibility of the obtained result deviating from the expected outcome. Further, environmental sustainability is evaluated by disability-adjusted life years (DALYs), which is the impact on human health. Third, uncertainty in the proposed optimization model is defuzzified by the fuzzy chance-constrained programming. Even though it has been applied in various fields, it has not been investigated in the investment portfolio optimization problems. Finally, since there are a large number of candidate projects and constraints, the efficacy of typical solvers would not be sufficient for such problems. As a result, a meta-heuristic algorithm (Genetic Algorithm [GA]) is introduced to optimize this problem. The outcome of this study as a decision support tool can help decision-makers optimize and evaluate portfolio investments by deciding to invest in the most suitable set of projects at the most suitable time under a limited budget with inherent restrictions and uncertain conditions.
The rest of the paper is organized as follows. Section two provides the literature review that consists of multi-objective optimization, project portfolio selection, and risk, uncertainty, and sustainability in the project portfolio selection. Section three presents the methodologies used in this study. Section four provides an explanation of our project portfolio problem with problem formulation and a numerical example. Section five presents and discusses the results. Section six demonstrates the decision support tool for managing the sustainable project portfolio investment selection and optimization. Finally, section seven provides the conclusions, limitations, and recommendations for further study.