AI-based Genetic Algorithms Applied to Projects

 

Genetic algorithms are a software representation of the theory of evolution and can be useful in solving many significant and different issues in project management. Humans reproduce, and the offspring tend to look similar and have similar—but not identical—traits to their parents. There is no typical pattern to this process. Offspring might receive 80 percent of their genes from one parent and 20 percent from the other, or they may receive 65 percent from one and 35 percent from the other. In a further twist of nature, a random percentage can be included, known as a mutation. As with the development of any species, those most able to adapt to the environment survive. This survival concept is known as a fitness factor and is important in the project setting because it is used to represent the project objectives. The genetic algorithm simulates evolution by creating all possible combinations of a solution until the one closest to the desired result is found. 

How is this used in project management? In projects, the objectives are known, typically the scope, budget, and scheduled end date. Given the desired result, the algorithm searches for all possible combinations to achieve the objective. The value is that a genetic algorithm is not constrained by human bias, knowledge, or experience. The algorithm churns through all possible combinations of potential decisions, including solutions that a human project manager may never consider.

A review of research on using genetic algorithms in project management reveals that project scheduling is the most significant opportunity (Ancveire & Poļaka, 2019). The studies describe results for resolving scheduling conflicts, optimizing resource leveling, developing a scheduling strategy, improving critical path planning, and determining solutions to resource constraints.

This is an example where AI-based algorithms can solve numerous project issues. Collaboration is required between project managers and software developers to find ways to unleash the power of genetic algorithms to significantly improve project performance. These types of algorithms can be more complex to understand but are an example of another wave of machine learning that is available to solve project issues.

 Reference

Ancveire, I., & Poļaka, I. (2019). Application of Genetic Algorithms for Decision-Making in Project Management: A Literature Review. Information Technology & Management Science, 22, 22–31.