A majority of companies are involved in the planning and execution of projects. The number of projects that companies need to evaluate has significantly increased in recent years. This trend has various causes, such as the digitalization of corporate processes, diversification, or strategic positioning in the face of ever-changing market conditions. The characterization of projects into mandatory and optional, as well as the evaluation of these projects, can be conducted based on various mixed criteria, which may include both cost and benefit criteria. The limited resources of companies necessitate a critical assessment of projects. They must be ranked based on realistic and plausible criteria regarding their benefits and objectives of the company. In the literature, various approaches to project evaluation exist. Examples include financial assessment, the utilization of evaluation models considering risks, or even multi-criteria models that incorporate different aspects of projects into the evaluation process. We propose a robust, scalable, and easily calibrated multi-criteria evaluation model for project evaluation and ranking, encompassing evaluation criteria such as financial criteria measured by Net Present Value (NPV), Risk, Classification, Priority, Strategy, and Sustainability. To achieve this goal, the Technique for Order of Preference by Similarity to Ideal Solution with multi type projects multi mixed cost and benefit criteria (TOPSIS-MTPMMCBC) is employed. The model is adapted to evaluate and rank optional and mandatory projects. An important feature of the projects in this study is that the criteria values of the projects can have negative or positive values. Particularly noteworthy is the increasing significance of sustainability as a key criterion for businesses, driven by political mandates. Consequently, a decision based on the criterion of sustainability will be important in the future and is implemented in the proposed model. The proposed research can be adapted to use a variety of Key Performance Indicators (KPIs) as multiple decision criteria. An objective calculation of the criteria weights for sample dataset was carried out using the CRITIC method, followed by a sensitivity analysis. Subsequently, the optimization of the project portfolio was carried out by combining an integer programming model with the proposed TOPSIS-MTPMMCBC method. An initial solution of project evaluation and ranking is conducted to demonstrate the applications.

Considering the acceleration in the development of alternative energy sources due to climate change and the net zero carbon commitments made in this direction, there are different assessments of how the capacity of the refining industry will change in the next two decades. Refinery companies are trying to adapt to altering conditions while also trying to determine their investment strategies. Project portfolio selection problem is one of the relevant issues to be considered in line with these changes. In this article, research has been undertaken to determine which criteria refinery companies take into consideration while selecting their project portfolios. Based on the identified criteria, it is also aimed to carry out a study that will guide sector practitioners in project selection. For this purpose, interviews were conducted with industry experts. The criteria were accredited by applying categorical content analysis to the data obtained and their importance weights were identified accordingly. The most deterministic criteria were abstracted from the findings and applied to a multi-criteria decision-making (MCDM) framework, namely fuzzy MULTIMOORA to suggest a decision support tool that ranks the projects against themselves. Some of the prominent outcomes of the study are also discussed, along with the previous studies and comparative results of the proposed decision support tool.

This study aims to design a new model for selecting most fitting new product development projects in a pool of projects. To catch the best model, we assume new products will be introduced to the competitive markets. Also, we suppose the revenue yielded by completed projects can be reinvested on implementation of other projects. Other sources of financing are borrowing loans from banks and initial capital of the firm. These limited resources determine most evaluated projects to be performed. Several types of interactions among different projects are considered to make the chosen projects more like a portfolio. In addition, some numerical examples from the real world are provided to demonstrate the applicability of the proposed model. These examples show how the particular considerations in the suggested model affect the results.

One of the most critical factors used to evaluate the efficiency of the portfolio selection process is the ability of the model to measure interdependencies among projects. Varieties of interactions among projects lead to several kinds of synergies in the whole portfolio, such as re-sources and knowledge interdependencies. There are few studies focused on project portfolio selection accompanied by modeling and estimating the impact of synergies between projects. Hence, this paper presents a model to select the best project portfolio applying a particular model to measure the effects of several types of interdependencies between paired projects. Then, the Promethee II method is used to prioritize projects. Then, the portfolio selection model, which is a non-linear integer model, is solved to find the best set of projects. Finally, numerical examples are addressed to illustrate the method results and validity.

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.

Over the past four decades, portfolio selection has been one of the most important con-cerns of researchers, project managers, project-oriented companies, and public agencies around the world. Although numerous studies have been done in this field, still there is a room for more improvement in both theory and practice. One of the yet unspoiled topics in this field is improving and balancing the efficiency of sub-portfolios while paying attention to portfolio optimization. This study employs data-mining tools to categorize projects into sub-portfolios and rank them. Multiple Criteria Decision Making (MCDM) methods are also used to weigh the criteria on which the ranking process is based. Finally, a novel multi-objective model is designed to optimize the efficiency of sub-portfolios and the gain of the main portfolio. The model is solved by NSGA II algorithm. This study introduces a hybrid framework by which project portfolio selection process can be carried out regarding strategic alignment, cost, and risk.

Portfolio management has become a strategic activity in response to increasing organizational complexity, requiring models capable of reconciling multiple criteria, limited resources, and uncertainty. In this context, Portfolio Decision Analysis (PDA) has emerged as a well-established field within Operations Research dedicated to supporting such decision-making processes. This study presents a systematic literature review conducted in accordance with the PRISMA protocol, aiming to analyze the evolution of mathematical models applied to portfolio management. The search resulted in a final sample of 152 articles published between 1994 and 2025. The findings reveal a significant increase in publications from 2014 onward, with applications predominantly concentrated in project management and new product development (NPD), and a predominance of financial and technical-operational decision criteria. The results also indicate a methodological transition from classical optimization techniques to hybrid approaches that integrate multicriteria decision-making methods, fuzzy logic, mathematical programming, and, more recently, machine learning. The main research gaps identified include the need for broader empirical validation, the expansion of applications to different contexts, and the development of new hybrid approaches. This study provides an integrated overview of the methodological evolution of Portfolio Decision Analysis, offering a valuable reference for future research.

Projects are essential mediums for businesses, organizations and industries to reach their strategic goal, however managing multiple projects simultaneously is a complex task. There have been a number of studies conducted on developing and introducing frameworks and models to help optimize project portfolio management. This study aims to identify these frameworks and analyze them to gain an understanding of the core components of these frameworks and to introduce the recent models and approaches used to optimize project portfolios. The methodology followed the guidelines of systematic literature reviews, by identifying a substantial number of papers, screening them and analyzing them until the research goals were met. The findings included 46 of relevant articles proposing models in project portfolio context, the data of the articles were recorded and presented and the models proposed were analyzed. Bibliographical findings were provided and by analyzing the models their core components were identified and using the core components a figure presenting the key components was illustrated.

Portfolio selection is a business process which has helped organisations identify an area of com-petitive advantage and it is a major concern to industrial players in the banking sectors. In order to enhance bank portfolio selection, cost, profitability, time and location are important parameters that decision-makers often consider. This study implements a fuzzy-TOPSIS (Technique for Or-der Preference by Similarity to Ideal Solution) framework to evaluate three potential portfolios (automated teller machine gallery, quick service point and branch) for a bank using the infor-mation from three decision-makers. An illustrative example of real bank information is used to demonstrate the proposed framework applicability. The complex proportional assessment of al-ternatives (COPRAS) method is also used as an evaluation technique and the results are com-pared, which yields that the results from the ranking order of fuzzy-TOPSIS and COPRAS were different. However, there is a consistency between the aggregation of intuition-based, fuzzy-TOPSIS and COPRAS ranks and fuzzy-TOPSIS ranking results. The presented framework is an easy-to-apply tool that improves portfolio selection decision in the banking system.