Futuristic government building with a network overlay, symbolizing AI-driven transparency in e-tendering.

Smarter Government: How AI and Fuzzy Logic are Upgrading E-Tendering

Beau Callahan in Tech & Innovation October 2026 • 3 min read.

"Discover how combining Genetic Algorithms and TOPSIS with fuzzy logic can make government procurement fairer and more efficient."

In today's rapidly evolving digital landscape, governments are under increasing pressure to modernize their traditional systems. Electronic methods offer enhanced effectiveness and efficiency, and E-tendering has emerged as a game-changing solution, providing efficiency, fairness, transparency, and accountability. Embracing and advancing government E-tendering (GeT) is no longer an option but a necessity for progressive administrations.

E-tendering leverages web-based bid evaluations, a critical component that has often been overlooked in existing literature. This article introduces a hybrid approach for web-based bid evaluation in GeT, designed to identify the best tenderer in an efficient and fair manner. This method addresses the complexities of evaluating tenderer attributes, which are often expressed as fuzzy number intuitionistic fuzzy sets (FNIFSs).

Combining genetic algorithms (GA) with the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), this approach ensures accuracy by using FNIFSs to describe tenderers' attributes. GA is used to determine the optimal weights for evaluation criteria automatically, while TOPSIS identifies the optimal tenderer.

The Power of AI in Government Procurement

Futuristic government building with a network overlay, symbolizing AI-driven transparency in e-tendering.

Tendering, traditionally viewed as one of the most impartial methods for awarding government contracts, aims to secure the best possible outcome for public funds. E-tendering enhances conventional tendering by integrating the Internet, computers, and certification technologies, making the tendering process electronic and information-driven.

E-tendering offers significant advantages:

Expands enterprises’ integration into the international supply chain.
Reduces costs for both tenderers and tenderees, conserving social resources and streamlining tendering processes.
Standardizes market practices, reduces corruption, and fosters a transparent tendering environment.

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Counterpoints

Limits of AI and E-Procurement

The European Commission notes that it is often not possible to determine why an AI system made a decision, prediction, or took a particular action. E-procurement implementation also involves challenges related to IT infrastructure, organizational adoption, and behavioural and relational impacts. Research on public-sector e-procurement adoption examines barriers and actionable strategies for overcoming them. These limitations help explain why digital tendering does not automatically deliver transparency, efficiency, or accountability.

Comparison

Electronic and Traditional Tendering

Comparisons of electronic and traditional tendering commonly examine differences in medium, transparency, cost, and accessibility. E-tendering is described as offering efficiency, transparency, cost-effectiveness, accessibility, and security by using technology to streamline procurement activities. Traditional tendering remains the benchmark against which these claimed benefits, as well as the practical disadvantages of digital systems, are assessed. The comparison therefore concerns more than replacing paper with software: it also involves how organizations manage access, costs, transparency, and process efficiency.

An effective GeT system requires significant functionality, particularly in web-based bid evaluation. This is where the integration of advanced algorithms like GA and TOPSIS comes into play, providing a robust framework for assessing and selecting the best candidates.

The Future of Fair Government

By combining the strengths of GA and TOPSIS with the nuanced capabilities of FNIFSs, government E-tendering can achieve new levels of efficiency and fairness. This innovative approach not only streamlines the procurement process but also promotes transparency and accountability, ensuring that public funds are used wisely and effectively. As technology advances, expect to see even more sophisticated applications of AI and fuzzy logic in transforming governmental operations.

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Real-World Impact

Trustworthy AI and Public Values

The OECD AI Principles promote AI that is innovative and trustworthy. They also emphasize respect for human rights and democratic values. In e-tendering, these principles place the human and institutional consequences of AI alongside its technical capabilities. Real-world impact therefore depends on deploying AI in ways that preserve trust and align with public values.

What the procurement evidence does-and does not-show

The approach was checked for feasibility using a prototype and an illustrative GeT example. That supports feasibility in the example, but does not establish how the method performs across procurement settings.^[1]

The evidence base for public procurement interventions is described as mediocre, with reliable evidence available across multiple countries and methods only for selected, typically narrow tools. Broad claims about procurement outcomes therefore go beyond what that assessment establishes.^[2]

How e-procurement findings compare

A separate analysis of firms across countries and years found that e-procurement adoption reduced firm corruption in securing public contracts. That finding concerns adoption and corruption, rather than the performance of the GA-TOPSIS method itself.^[3]

What remains unresolved about AI procurement

How do buyers and suppliers identify the functionality and consequences of AI systems when these may become apparent only during deployment?^[4]
What would help firms move beyond early digital maturity and make fuller use of AI in procurement activities?^[5]
How might findings from narrow procurement tools be extended into reliable evidence about broader interventions?^[2]

Sources

Combined Approach for Government E-Tendering Using GA and TOPSIS with Intuitionistic Fuzzy Information | PLOS One (journals.plos.org)
World Bank Document (documents1.worldbank.org)
EconPapers: E-procurement and firm corruption to secure public contracts: The moderating role of governance institutions and supranational support (econpapers.repec.org)
Guidelines for AI procurement - GOV.UKGOV.UK (www.gov.uk)
The role of artificial intelligence in the procurement process: State of the art and research agenda (re.public.polimi.it)

About this Article -

Written with AI assistance from published research, and reviewed by the Mystum team. See our About page for more information.

Everything You Need To Know

What is E-tendering, and why is it important for governments?

E-tendering is the process of conducting tenders electronically, leveraging the internet, computers, and digital certification technologies. This transforms traditional tendering by making it information-driven and more efficient. E-tendering enhances enterprises’ integration into the international supply chain, reduces costs, standardizes market practices, reduces corruption, and promotes transparency.

What role do Genetic Algorithms (GA) play in E-tendering, and why is this significant?

Genetic Algorithms (GA) are used to automatically determine the optimal weights for the evaluation criteria in E-tendering. This is significant because it ensures that the most relevant factors are given appropriate consideration during the bid evaluation process. This automated weighting enhances the accuracy and fairness of selecting the best tenderer. Without a GA, the criteria weights might be subject to human bias or error.

What is TOPSIS, and how is it used in the context of E-tendering?

TOPSIS, which stands for Technique for Order Preference by Similarity to an Ideal Solution, is used to identify the optimal tenderer based on the weighted evaluation criteria. TOPSIS works by comparing each tenderer's attributes to an ideal solution and selecting the one that is closest. This method is significant because it provides a systematic and objective way to rank tenderers, enhancing the fairness and efficiency of government procurement. Its integration with FNIFSs allows a nuanced understanding of the attributes.

What are Fuzzy Number Intuitionistic Fuzzy Sets (FNIFSs), and why are they used to describe tenderers' attributes?

Fuzzy Number Intuitionistic Fuzzy Sets (FNIFSs) are used to describe the attributes of tenderers in a way that accounts for the uncertainty and vagueness often present in real-world evaluations. FNIFSs allows decision-makers to input values that fall between 0 and 1, indicating the degree of membership and non-membership, offering a more realistic representation. This is important because it enables a more accurate and nuanced assessment of tenderers' capabilities and qualifications. Without FNIFSs, evaluations might oversimplify complex attributes, leading to less accurate and fair outcomes.

How does combining Genetic Algorithms (GA), TOPSIS, and Fuzzy Number Intuitionistic Fuzzy Sets (FNIFSs) improve government E-tendering?

The combination of Genetic Algorithms (GA) and TOPSIS with Fuzzy Number Intuitionistic Fuzzy Sets (FNIFSs) is used to optimize and enhance the web-based bid evaluation process in government E-tendering (GeT). This approach brings greater accuracy and fairness to the tendering process, streamlining procurement, promoting transparency and accountability, and ensuring that public funds are used effectively. This leads to a smarter government.