A digital illustration depicting the future of e-tendering, with AI algorithms and data streams converging to select the optimal tenderer.

E-Tendering Revolution: How AI and Fuzzy Logic Are Reshaping Government Contracts

Theo Raines in Tech & Innovation September 2025 • 4 min read.

"Unlocking Efficiency, Fairness, and Transparency in the Digital Age of Public Procurement"

In an era defined by rapid technological advancements, the evolution of e-government has become a critical priority. As digital platforms transform traditional systems, the need for efficient, transparent, and accountable processes is more pressing than ever. One area ripe for transformation is government e-tendering, a process that is now embracing cutting-edge technologies to modernize its operations.

This article delves into a pioneering approach that merges the power of Genetic Algorithms (GA) and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) with Intuitionistic Fuzzy Information. This innovative hybrid system is designed to enhance the efficiency and fairness of government e-tendering by accurately assessing tenderer attributes within a complex, multi-faceted environment.

We'll explore how this innovative solution not only streamlines the selection process but also promotes a level of transparency and accountability that is essential in the digital age. By examining the core components and practical applications of this system, we aim to illuminate the transformative potential of advanced technologies in shaping the future of public procurement.

The Building Blocks: GA, TOPSIS, and Intuitionistic Fuzzy Sets

A digital illustration depicting the future of e-tendering, with AI algorithms and data streams converging to select the optimal tenderer.

The success of this e-tendering system hinges on three key components: Genetic Algorithms (GA), Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and Intuitionistic Fuzzy Sets (FNIFSs). Each of these elements plays a unique role in creating a comprehensive and effective system for evaluating and selecting tenderers.

Genetic Algorithms (GA) are used to obtain the optimal weights of evaluation criteria, reflecting the degree of importance for each criterion. GA's ability to automatically adjust search directions enables a superior ability for global optimization. TOPSIS then steps in to identify the optimal tenderer from the pool of candidates. Finally, Intuitionistic Fuzzy Sets (FNIFSs) come into play to express the tenderers' attributes, allowing for a more refined and objective assessment.

Genetic Algorithms (GA): Employed to determine the optimal weights of evaluation criteria, which reflect the importance of each criterion.
Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS): Utilized to identify the optimal tenderer based on closeness to an ideal solution.
Intuitionistic Fuzzy Sets (FNIFSs): Applied to express the attributes of the tenderers, ensuring a refined and objective evaluation.

By combining these components, the system provides a robust and adaptable framework for government e-tendering. The use of FNIFSs allows for a nuanced understanding of tenderer attributes, while GA and TOPSIS facilitate efficient and fair decision-making. This synergistic approach is a key to the system's effectiveness.

The Future of E-Tendering: Efficiency, Fairness, and Beyond

The integration of AI and advanced decision-making tools into government e-tendering marks a significant step towards a more efficient, fair, and transparent procurement process. By embracing technologies like GA, TOPSIS, and FNIFSs, governments can enhance the way they select and manage contracts, and ultimately deliver better services to their citizens. The ongoing development and implementation of such systems promise to create a more streamlined and accountable government ecosystem, driving positive change and innovation in the public sector.

About this Article -

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This article is based on research published under:

DOI-LINK: 10.1371/journal.pone.0130767, Alternate LINK

Title: Combined Approach For Government E-Tendering Using Ga And Topsis With Intuitionistic Fuzzy Information

Subject: Multidisciplinary

Journal: PLOS ONE

Publisher: Public Library of Science (PLoS)

Authors: Yan Wang, Chengyu Xi, Shuai Zhang, Wenyu Zhang, Dejian Yu

Published: 2015-07-06

Everything You Need To Know

How do Genetic Algorithms (GA) contribute to improving government e-tendering processes?

Genetic Algorithms (GA) play a crucial role in optimizing the weights of evaluation criteria in e-tendering. By autonomously adjusting search directions, GA ensures that each criterion's importance is accurately reflected, leading to superior global optimization and a more precise assessment of tenderers. GA's adaptability ensures the e-tendering process remains dynamic and responsive to evolving priorities.

What is the role of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) in government e-tendering?

The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is employed to pinpoint the optimal tenderer from the available pool of candidates. TOPSIS achieves this by evaluating how closely each tenderer aligns with an ideal solution, thereby facilitating the selection of the most suitable option. TOPSIS contributes to improved decision-making by providing a systematic approach to compare and rank competing proposals.

What is the significance of using Intuitionistic Fuzzy Sets (FNIFSs) in evaluating tenderers?

Intuitionistic Fuzzy Sets (FNIFSs) are utilized to articulate the attributes of tenderers with greater nuance. FNIFSs enable a more refined and objective assessment, capturing the inherent uncertainties and complexities associated with tenderer characteristics. This approach allows for a more comprehensive understanding of tenderer capabilities.

How does the integration of Genetic Algorithms (GA), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Intuitionistic Fuzzy Sets (FNIFSs) enhance fairness and transparency in government e-tendering?

The synergistic combination of Genetic Algorithms (GA), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Intuitionistic Fuzzy Sets (FNIFSs) establishes a robust framework that significantly bolsters fairness and transparency in government e-tendering. Genetic Algorithms (GA) ensure optimal weighting of evaluation criteria, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) objectively identifies the best tenderer, and Intuitionistic Fuzzy Sets (FNIFSs) enable a nuanced understanding of tenderer attributes. The result is a more accountable and equitable procurement process that minimizes bias and promotes trust.

Beyond efficiency and fairness, what are the broader implications of integrating technologies like Genetic Algorithms (GA), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Intuitionistic Fuzzy Sets (FNIFSs) into government e-tendering processes?

The integration of technologies like Genetic Algorithms (GA), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Intuitionistic Fuzzy Sets (FNIFSs) into government e-tendering not only enhances efficiency and fairness but also fosters a more streamlined and accountable government ecosystem. By optimizing contract selection and management, these technologies facilitate better service delivery to citizens and drive positive change and innovation within the public sector. The enhanced transparency and objectivity reduce opportunities for corruption and improve public trust. This represents a fundamental shift towards data-driven decision-making in governance, promoting responsible resource allocation and sustainable development.