انتخاب سبد پروژه

Timely project completion within the predicted budget is one of the primary goals of project-oriented organizations. The inherent risks associated with projects have made achieving these goals challenging for most organizations. In many cases, the interdependence of risks across different projects leads to undesirable consequences, further exacerbating this challenge. To address such challenges, this article introduces a novel formulation for the two-objective problem of project portfolio selection and scheduling. The first objective is to maximize the profit of the project portfolio by accounting for the aggregated cost impacts of risks on projects' activities. The second objective aims to minimize the implementation time of the project portfolio.

To evaluate the expected impacts of risks and their interactions, the objective functions are developed based on the Bayesian Networks approach. Current mathematical formulations for the integrated problem of project portfolio selection and scheduling exhibit significant limitations, including the absence of incorporating risk factors and their interdependencies within and across projects on the duration and cost of activities. As a solution approach, the simulation-based approximation approach is developed to establish linear modelling of the problem. Since this approach provides multi-objective mathematical modelling, augmented -constrained programming is employed to solve the presented model. For the validation of the formulations, the proposed model and solution approach are applied to a sample instance.

The results of the model implementation, while confirming the validity of the proposed approach, demonstrated that selecting projects with fewer interference effects with other projects can yield more favourable outcomes for project-oriented organizations.

Project portfolio selection/scheduling is a critical decision-making problem for every organization, particularly project-based organizations, as it significantly affects organizational performance. This paper investigated a novel approach to the joint problem of project selection and scheduling by assessing the aggregated individual and intensifying the risk impacts on the duration and cost of the projects' activities. Due to the uncertain environment of projects, the duration and cost estimation of the activities is not a straightforward task.

This study provided a new bi-objective mathematical model for the project selection and scheduling problem.

To achieve continuous growth in a project-oriented organization, managing projects individually does not work well. By combining multi-criteria decision-making methods (MCDM) and strategic analysis, the present research examines the challenges of selecting and managing the project portfolio in the Nano Development Headquarters, emphasizing the importance of optimal use of limited resources and reducing investment risk. Its main goal is to develop a model for evaluating and prioritizing projects based on the greatest synergies and values and improving the effectiveness of decisions in line with strategic goals.

This research uses a hybrid approach that includes the implementation of a scientific framework with five steps. At first, using the SWOT analysis, three key strategies were formulated to manage the portfolio of nano projects: portfolio of multidisciplinary cooperation, portfolio of commercialization of nano products and portfolio of connection between nano and everyday life. The projects were categorized using the concepts of synergies and the benefits map. Then, using the Electre method, the projects were prioritized based on 10 criteria and included in the final basket. Finally, a balanced portfolio of projects was selected and a roadmap for better management of this portfolio was presented.

The research showed that among the 32 initial projects, 7 projects were removed and 3 other projects were also abandoned due to non-compliance with the key criteria. According to the SWOT matrix, four options were presented and three strategies were developed based on that. The categorization of projects was done based on the concepts of synergy and proximity of projects to strategic goals and the intended value created through the logical map of benefits. The final grouping of the projects in three categories: pearls, oysters, and white elephants was done with the opinion of experts, and with the update of the final basket, the definite priority of each project in each basket was included in the portfolio roadmap. With this approach, an optimal combination of projects in portfolios was created, and the order of projects was determined. The presented model was able to create an optimal combination of projects and determine their execution order by considering the primary criteria, risk analysis, probability of success, earnings, and return on investment (ROI). Also, the roadmap of the project portfolio was drawn to help better management and achieve strategic goals. The final finding from the data analysis is that initially, basket 2 (D5, B3, A3, E5, E1, E2, D2, A1, C2) started with project D5 and at the end of the first three years of basket 1 (B6, A4, E4, B2, A5, D1, B5, E3, E6) with project B6 will start simultaneously with projects E5 and E1 and the start time of basket 3 (E7, D7, E8, C1) in the middle of the second schedule with project E7 with the implementation of projects D2, A1, E4, and B2. The maximum execution time for basket 1 is ten, for basket 2 eight, and for basket 3 six years.

Research limitations/implications: 

Access to data, experts' lack of familiarity with project portfolio management concepts, and the unpredictability of nanotechnology and changing criteria are some of the limitations of this research. The research is specifically focused on the field of nanotechnology, but it can be easily extended to other fields with personalization. In this research, the concepts of project portfolio management, synergy of projects, and logical map of benefits have been used for writing. It is suggested that in future research, aspects of uncertainty in the decision-making process allocation of resources and project schedule should be addressed with modelling.

Practical implications: 

By providing a comprehensive model for project portfolio management, managers can make better decisions in selecting and prioritizing projects. This helps to increase efficiency and reduce risks associated with projects. It also helps companies get closer to their business goals and grow and develop their business by creating synergies.

Social implications:

 In addition to the positive effects on improving public health, the environment, and increasing the quality of life, the correct selection of the portfolio of nano projects will help project-oriented companies to achieve higher standards in the fields of safety, health, and environment (SHE) by using project portfolio management. At a larger level, it helps government and policymakers make more informed decisions to invest in and support nanotechnology projects that directly lead to economic and social development.

Originality/value: 

This research is added to the literature on project management because it provides a comprehensive and new framework for selecting the project portfolio from different angles and can be a valuable resource for researchers and managers. Overall, this research provides a useful framework for organizations seeking to optimize their project portfolios.

The selection of project portfolio is the main problem in project-based organizations. In project selection, optimized decision to maximizing utilization of limited resources and achieving organizational goals by considering restrictions are important. Therefore, to facilitate project portfolio selection in project-based organizations, an efficient model is proposed that is able to consider limitations, goals, and priorities of the organization in selecting the optimal combination of projects. The proposed model is a hybrid and flexible one which, including four main steps. First, with initial screening, to save time and money by using non-compensatory method, some of the poor candidate options are deleted. Then, due to multi-criteria nature of the problem, qualitative and quantitative criteria, and uncertainty of data in real world, the projects are ranked using fuzzy analytic hierarchy processes and uncertain judgments of decision-makers. To generate project baskets, integer linear programming (ILP), backpack model, is employed modifying the coefficients of the objective function of each project to preserve multi-criteria rating of projects, and then evaluate and rank the baskets. To validate the proposed model, we have carried out selection of project portfolio in the oil refining industry.

Project portfolio selection and risk response selection are two issues that have been considered disjointedly by the researchers. In this study, an integrated mathematical model is presented for the above-mentioned problems. A situation is noticed in which, in the stage of selecting the project portfolio, some of the proposed projects are facing risks, and some actions can be planned to mitigate these risks. With regard to the fact that implementing these responses requires resources and changes the risk of the portfolio, it is essential to consider the selection of responses at the stage of portfolio selection. A bi-objective mathematical model is proposed, whose first objective is to maximize the profit earned from selected projects, and its second objective is to minimize portfolio risk. Profit variance is considered as a measure of portfolio risk. A numerical example, illustrates the model application and the difference between the integrated and non-integrative approaches. Non-dominated Sorting Genetic Algorithm (NSGA-II) is applied to solve the model.

In this paper, a mathematical model is proposed for project portfolio selection and resource availability cost problem to scheduling activities in order to maximize net present value of the selected projects preserving precedence and resource constraints. Since the developed model belongs to NP-hard problems list, so a genetic based meta-heuristic algorithm is proposed to tackle the developed model. In the proposed algorithm beside common operators of genetic algorithms such as crossover & mutation, some intelligent operators are utilized for local search in computed resources and shifting the activities with negative cash flows. The key parameters of the algorithm are calibrated using Taguchi method to accelerate convergence of the proposed algorithm. Then, the algorithm is used to solve 90 test problems consisting 30 small-scale, 30 middle-scale and 30 large scale problems to examine the algorithm’s performance. It is observed that, in small problems, the obtained solutions from the proposed genetic algorithm have been comparably better than the local optimum solutions stemmed from Lingo software. On the other hand, for the middle and large size problems which there is no local optimum available within the limited CPU time, robustness of the proposed algorithm is appropriate.

Considering that the active companies in the field of oil, gas, petrochemical and other energies are project-based and also the increase of gas applicants who have taken policy of replacing the gas instead of other fossil fuels, have imposed certain condition on organizations and project managers in the gas company. One of the most important problem in the issue of project management is project portfolio selection which is defined one of the most important activities in many organization such as gas organization. In this study at first the effective indicators on projects are extracted by using the literature and interviews with the experts of gas industry then the mathematical robust multi objective Model is Provided by considering the uncertainty and unreliability in some parameters of model. This model is solved by using non-dominate sorting genetic algorithm for 20 degree of risk-taking decision gama (I C t / IB t ).At the end for helping in decision making the TOPSIS technique is used for providing a specific answer in pareto front .

The branches are more efficient to develop. This paper presents a model for evaluating the performance of the software companies that provide similar services to customers in various profound. Various parameters of the model to cover the interests of stakeholders, customers and ... To provide performance measurement systems in the Charter of the method used. Performance evaluation and assessment of various aspects such as the interests of employees, customers, shareholders and ... Is to be calculated. The performance of these branches according to the criteria developed by inconclusive data model of fuzzy data envelopment analysis to measure.

Vast number of organizational projects and complexity of decision making process can cause particular challenges for project management and its leadership. In order to use organization assets and opportunities efficiently, it is necessary that manager implement a comprehensive multidimensional project portfolio management system that considers economical, social and technical details of the projects. Resource constraint compels managers to select operational proposal projects. Thus managers can maximize organizational utility due to project portfolio’s resource constraint. This study considers the interactive effects of project portfolio evaluation and sharing of organizational project resources with respect to its evaluation and choice of the projects.. In this two-step model, first a branch and bound algorithm with resource interaction was utilized to screen maximal portfolio and in the next step each portfolio were evaluated based on artificial neural networks to rank the end project portfolios. Also the ANN scores are strongly correlated with the DEA and COLS efficiency scores.

Selection of right sets of projects is considerably critical for organizations to successfully achieve their competitive advantages and corporate strategies. Due to limited resources and dynamic changes in business environment, this kind of selection is quite challenging for organizations. Beside one hundred selection tools and techniques, academics and practitioners have studied and recommended complex selection frameworks to facilitate the selection of right projects. In this study a new model was developed for project portfolio evaluation and selection. The model is as follow: evaluation of individual projects based on paired comparison, construction and evaluation of portfolios with artificial neural networks, classification and ranking of portfolio of organizational projects. Finaly the results were compared with DEA and COLS model. The entire methodology was illustrated via an example in oil & gas field.

Given the complexity of the project implementation and resource constraints, the project portfolio selection is important for organization-s. Hence, many researchers have attempted to provide methods for portfolio selection and often obtained interesting results. But most of them have not considered the interaction between projects. Considering the interactions between projects lead to complexity of portfolio selection problem and if these interactions be ignored, the decision making process maybe produce the undesired final results. In this paper, the portfolio selection problem with considering interactions between the projects is formulated. The portfolio selection problem with regard the interactions between projects using optimization algorithms particle swarm optimization (PSO) andchaotic particle swarm optimization (CPSO) was investigated. Interactions at the projects selection, final solution and its fitness show the important effects. It was shown that the PSO and CPSO methods are better in comparison with the genetic algorithm technique used before in such problems.