Project Management

AI-based Genetic Algorithms Applied to Projects

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Technology offers an incredible opportunity to improve project performance. This blog shares the latest research and how organizations are implementing AI into their project methodology. Come with an open mind, increase your knowledge, share your concerns, and become a project manager with new skills to offer an organization.

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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.

 

 

Posted on: May 20, 2024 12:00 AM | Permalink

Comments (10)

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Aaron Porter IT Project Manager| Blade HQ Pleasant Grove, Ut, USA
The reference article is five years old. Is there any subsequent data supporting these statements, or information on how it can be applied?

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Sameh Hafez Technical Office Manager| Hammam Industries 6th October, Gz, Egypt
interesting topic and i think the future will be better.

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Paul Boudreau President| Stonemeadow Consulting Kanata, Ontario, Canada
@Aaron. Thanks for the comment. Generally, articles five years old or less are encouraged for academic research. If you have access to a university library, you can look for additional peer-reviewed articles. Google Scholar tends to work, too. There are also a few good textbooks about how genetic algorithms function. OpenAI is currently using GA to further its AI development, although it is unlikely to reveal that publicly. I have an article coming up later that discusses Q* development and will explain it further.

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Mark Geres Director| PM by Design Canada, Inc. Cantley, Quebec, Canada
AI Topic Question

AI is often described in project management social media posts as primarily algorithm-based.

My understanding has been that AI makes use of BOTH ’heuristics’ and ‘algorithms’ and not exclusively algorithms.

Is my premise accurate?

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Paul Boudreau President| Stonemeadow Consulting Kanata, Ontario, Canada
@Mark. Yes, your premise is accurate. AI systems often make use of both heuristics and algorithms, although they serve different purposes.

Algorithms are step-by-step procedures or formulas for solving a problem or accomplishing a task. They are typically precise and deterministic, providing a clear set of instructions to achieve a desired outcome. In AI, algorithms are commonly used for tasks like sorting data, searching through possibilities, and making calculations.

Heuristics, on the other hand, are general problem-solving strategies or rules of thumb that might not always guarantee an optimal solution but can lead to satisfactory results in a reasonable amount of time. Heuristics are often used in AI for tasks where finding an exact solution may be computationally infeasible or too time-consuming. They allow AI systems to make educated guesses or approximate solutions based on available information.

BTW. I copied this from CHATGPT

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Paul Boudreau President| Stonemeadow Consulting Kanata, Ontario, Canada
@Mark. It might be confusing because the algorithm learn from the data using various learning techniques. It is normally not a single algorithm but layers that have complementary objectives.

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Abolfazl Yousefi Darestani Manager, Quality and Continuous Improvement| Hörmann-TNR Industrial Doors Newmarket, Ontario, Canada
Thank you for sharing!

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Kwiyuh Michael Wepngong Financial Management Specialist | US Peace Corps / Cameroon Yaounde, Centre, Cameroon
Thanks for highlighting this to us

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Rebecca Winston Idaho Falls, Id, USA
While I agree with the article, it did not highlight various ethical, security, and bias issues that are attached to AI, especially publicly available data using AI. Even AI developed specifically for an organization can be problematic. An example: Many organizations use subcontractors to develop the tools to be used internally, but the developers often want to keep control of the algorithms. One ends up being tied to the developer and issues of bias and security become opaque to the user organization. Strongly urge individuals using AI for project management, whether scheduling, cost estimating, reviewing performance factors such as EV to become knowledgeable users.

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Oscar Angel Rojas Alania Project Manager| Eng. Oscar Rojas Peru
Thank you Paul for your post. The latest AI's developments show us that this paper goes in the right direction.

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