Illustration of AI in government procurement.

Decoding E-Tendering: How AI and Fuzzy Logic Are Revolutionizing Government Procurement

Kai Mendoza in Tech & Innovation December 2025 • 4 min read.

"From Efficiency Boosts to Combating Corruption: Exploring the Cutting-Edge Technology Transforming Public Sector Contracts."

Government e-tendering (GeT) is undergoing a significant transformation, fueled by the integration of advanced technologies. This evolution is critical for enhancing traditional government systems, making them more efficient and accountable. As digital platforms become integral to public services, the need for secure, transparent, and efficient procurement processes has never been greater.

This paper delves into a groundbreaking approach that combines Genetic Algorithms (GA) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), enhanced with Intuitionistic Fuzzy Information. This hybrid model is designed to optimize the selection of tenderers in GeT systems. The use of Fuzzy Logic allows for the nuanced handling of tenderer attributes, leading to more accurate and fair evaluations.

The significance of this research lies in its potential to enhance the efficiency and fairness of public procurement. By leveraging AI and advanced analytical techniques, governments can make better decisions, reduce costs, and increase transparency. This article provides a comprehensive understanding of the techniques and methods employed.

Unpacking the Power of GA and TOPSIS in E-Tendering

Illustration of AI in government procurement.

The core of this innovative system is the integration of Genetic Algorithms (GA) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). GA is used to automatically determine the optimal weights of the evaluation criteria for tenderers. TOPSIS then employs these weights to identify the best tenderer. This ensures that the selection process is data-driven and objective. Both of these techniques use a system called Fuzzy logic.

Fuzzy logic, at the heart of the system, addresses the uncertainty inherent in evaluating tenderer attributes. Unlike traditional methods, fuzzy logic uses 'fuzzy sets' to represent criteria, allowing for a more nuanced and realistic evaluation. Attributes are expressed as Fuzzy Number Intuitionistic Fuzzy Sets (FNIFSs), which handle the vagueness often present in tenderer data. This allows experts to provide more accurate and detailed evaluations, making the system adaptable.

Genetic Algorithms (GA): These algorithms automatically determine the weights for different evaluation criteria, enhancing the objectivity of the assessment.
Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS): TOPSIS uses the weights provided by GA to pinpoint the optimal tenderer, ensuring data-driven and transparent decisions.
Fuzzy Logic Integration: By using fuzzy sets, the system can handle vague or imprecise data, such as subjective expert opinions, leading to more realistic and effective evaluations.

The practical application of this system involves several key steps: First, a decision matrix is constructed, incorporating tenderer attributes. This matrix is then weighted using the weights obtained from the GA. Next, the system identifies both a positive ideal solution (PIT), representing the best possible outcome, and a negative ideal solution (NIT), representing the worst. The closeness of each tenderer to the PIT and their distance from the NIT are then calculated to determine the most suitable choice. The system ranks each tenderer according to their closeness coefficient, and the tenderer with the highest score is selected.

The Future of Public Procurement: Smarter, Faster, and Fairer

The integration of AI and advanced analytical methods into government e-tendering presents a promising future for public sector procurement. The move towards more efficient, transparent, and data-driven processes not only streamlines operations but also reduces costs and promotes fairness. As these technologies continue to evolve, GeT systems are poised to become even more sophisticated, contributing to better governance and more effective public services.

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Everything You Need To Know

How is the integration of Artificial Intelligence and Fuzzy Logic improving government e-tendering (GeT)?

The integration of Artificial Intelligence and Fuzzy Logic is revolutionizing government e-tendering by making it more efficient, transparent, and secure. Specifically, techniques such as Genetic Algorithms (GA) are used to optimize the weighting of evaluation criteria, while Fuzzy Logic allows for a more nuanced handling of tenderer attributes, leading to more accurate and fairer evaluations. This helps reduce costs, improve decision-making, and enhance the overall integrity of public procurement.

What role do Genetic Algorithms (GA) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) play in optimizing the selection of tenderers?

Genetic Algorithms (GA) are used to automatically determine the optimal weights of the evaluation criteria for tenderers, making the selection process more data-driven and objective. Then, Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) employs these weights to identify the best tenderer, ensuring a transparent and fair decision-making process. This hybrid model enhances the efficiency and fairness of public procurement by leveraging AI and advanced analytical techniques.

How does Fuzzy Logic address the uncertainty inherent in evaluating tenderer attributes?

Fuzzy Logic addresses uncertainty by using 'fuzzy sets' to represent evaluation criteria. Unlike traditional methods, Fuzzy Logic allows for a more nuanced and realistic evaluation of tenderer attributes. These attributes are expressed as Fuzzy Number Intuitionistic Fuzzy Sets (FNIFSs), which can handle the vagueness often present in tenderer data. This enables experts to provide more accurate and detailed evaluations, making the system more adaptable and effective.

Can you explain the practical steps involved in applying the integrated system of Genetic Algorithms (GA) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) with Fuzzy Logic?

The practical application involves several key steps. First, a decision matrix is constructed, incorporating tenderer attributes. This matrix is then weighted using the weights obtained from the Genetic Algorithms (GA). Next, the system identifies both a positive ideal solution (PIT), representing the best possible outcome, and a negative ideal solution (NIT), representing the worst. The closeness of each tenderer to the PIT and their distance from the NIT are then calculated to determine the most suitable choice. The system ranks each tenderer according to their closeness coefficient, and the tenderer with the highest score is selected, ensuring a data-driven and objective selection process.

What are the potential future implications of integrating AI and advanced analytical methods, like Genetic Algorithms (GA), Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and Fuzzy Logic, into government e-tendering?

The integration of AI and advanced analytical methods presents a promising future for public sector procurement. By moving towards more efficient, transparent, and data-driven processes, operations are streamlined, costs are reduced, and fairness is promoted. As technologies like Genetic Algorithms (GA), Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and Fuzzy Logic continue to evolve, government e-tendering systems are poised to become even more sophisticated, contributing to better governance and more effective public services. This will lead to smarter, faster, and fairer procurement processes, ultimately improving public trust and resource allocation.