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

"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.

AI Search Multiple angles on this topic

Procurement at Scale

Available 2026 procurement trackers show the scale and fragmentation of public tendering. TenderPipe reports 241,043 open tenders across 171 countries, drawing on 329 official sources, alongside 4,627,950 contract award records updated daily. In the United States, ProcureTap reports 197K+ tracked bids across all 50 states, 605 agencies, and $133B in awarded value.

From Procedure to Platform

Federal award procurement requires recipients and subrecipients to maintain and use documented procedures for acquiring property or services. E-tendering moves that procurement process online through a procurement platform, while reviews describe digital systems as a way to streamline and automate tendering. The literature also identifies the need for universal standards and recognizes that digital adoption does not remove implementation limitations.

Origins in the 1960s and 1970s

Electronic procurement is not solely a recent internet-era development. The Springer account traces its origins to the 1960s and 1970s, when material requirements planning systems helped supply-management professionals and facilitated purchasing tasks. In this broad interpretation, e-procurement means using information technology to improve internal and external information flows and support strategic decisions.

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.
AI Search Multiple angles on this topic

Mapping the Digital Evidence

A 2025 scoping review mapped 48 peer-reviewed articles and policy documents published between 2000 and 2025. It identifies eProcurement platforms, blockchain systems, and AI-driven analytics among the technologies applied to tendering, contract management, and auditing. Related review work frames e-tendering as part of the broader transformation of public management toward a more efficient and modern state.

Adoption Is Not Automatic

Research on public-sector e-procurement identifies implementation and operation as persistent concerns rather than assuming that digitization solves procurement problems. The literature examines board-level IT infrastructure issues as well as behavioral and relational impacts among early and late adopters. A 2024 study of e-tendering in construction likewise presents challenges alongside transparency, efficiency, and competitiveness benefits.

Digital Versus Manual Tendering

The comparison sources describe e-tendering as an online, centralized alternative to manual tendering. Traditional processes are associated with paperwork, delays, and limited visibility, while electronic systems are presented as improving efficiency, transparency, cost-effectiveness, accessibility, and security. The sources also compare the two approaches across medium, transparency, cost, and accessibility.

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.

AI Search Multiple angles on this topic

A Qualified Transformation

Taken together, the available discussion suggests that e-tendering is best understood as a process transformation rather than a standalone technology. Its potential benefits depend on how well digital platforms, governance rules, data practices, and institutional capabilities work together. AI and fuzzy logic may extend these systems, but their value should be assessed against transparency, accountability, and practical procurement outcomes.

Toward Adaptive Procurement

The next stage of e-tendering may involve more adaptive decision-support tools, including AI-assisted analysis and fuzzy methods for handling incomplete or ambiguous bid information. Such developments remain forward-looking and should be treated as possibilities rather than guaranteed outcomes. Progress will likely depend on trustworthy data, explainable recommendations, interoperable platforms, and clear public-sector oversight.

Beyond the Platform

A systematic review describes public e-procurement implementation challenges as insufficiently understood despite substantial prior research. Broader procurement analysis places e-procurement alongside sustainability, data analytics, and AI as trends intended to improve efficiency, transparency, and compliance. This framing indicates that technology adoption must be addressed together with institutional, regulatory, and operational conditions.

People Still Govern the Process

Digital tendering changes how procurement staff and suppliers exchange information, evaluate bids, and manage contracts, but it does not eliminate human judgment. Users still need suitable training, confidence in the platform, and clear procedures for handling exceptions or disputes. The practical impact will therefore depend not only on automation, but also on whether institutions and market participants can use these systems effectively.

A separate public-procurement proposal pairs fuzzy TOPSIS with fuzzy Extent Analysis for bidder evaluation and selection; it is a related approach, not a result from this paper.[1]

What the method identifies among candidate tenderers

Main outcomeIdentification of the optimal tenderer[2]
Times cited24[2]

Limits the paper identifies for applying the method

  • The authors say practical implementation remains difficult and identify reducing that difficulty as future work.[3]
  • The paper points to gaps in rules, interoperability, and administrative oversight as problems affecting government e-tendering systems.[3]
  • The authors caution that standard genetic algorithms have limitations when problems become overly complex.[3]
  • A score function may not provide enough information to distinguish alternatives in some cases, motivating use of an accuracy function as well.[3]

What evidence would clarify next

  • How well does the approach work when experts provide fuzzy ratings for tenderers across criteria and as overall assessments?[3]
  • Can the method operate effectively where e-tendering systems face weak interoperability, missing rules, or unsuitable oversight?[2] [3]
  • When a score does not distinguish alternatives adequately, how does the accuracy function affect the comparison?[3]

About this Article -

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

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

1

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.

2

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.

3

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.

4

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.

5

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.

Newsletter Subscribe

Subscribe to get the latest articles and insights directly in your inbox.