-The information technology industry cannot be imagined without large-or small-scale projects. They are implemented to develop systems enabling key business processes and improving performance and enterprise resource management. However, projects often experience various difficulties during their execution. These problems are usually related to the three objectives of the project -costs, quality and deadline. A way these challenges can be solved is project risk management. However, not always the main problems and their influencing factors can be easily identified. Usually there is a need for a more profound analysis of the problem situation. In this paper, we propose the use of a Bayesian Network concept for quantitative risk management in agile projects. The Bayesian Network is explored using a case study focusing on a project that faces difficulties during the software delivery process. We explain why an agile risk analysis is needed and assess the potential risk factors, which may occur during the project. Thereafter, we design the Bayesian Network to capture the actual problem situation and make suggestions how to improve the delivery process based on the measures to be taken to reduce the occurrence of project risks.
In software development projects, managers still have to face a variety of organisational and technical limitations despite the development of technology and approaches to improve the project management process. Projects, Human Resources and Costs are planned for a specific period of time. However, in the progression of project execution, there is a need to make various decisions and to dynamically adjust the work plan during the project in order to conform to its evolution. Thus, there is a need for a method that employs the latest technology to support the project management decision-making process.The aim and the expected result of the article are to identify and collect available information in the scientific literature to answer the following questions: (1) Which challenges of project management have been addressed using genetic algorithms? (2) What are the opportunities and limitations of genetic algorithms in the project management decision-making process? (3) What are the potential solutions to the identified genetic algorithm problems?
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