decideXpert: Collaborative system using AHP-TOPSIS and fuzzy techniques for multicriteria group decision-making

In the evolving field of decision support systems, there is a critical need for tools that can address complex multi-criteria decision-making (MCDA) challenges with flexibility and a user-centric approach. This paper presents decideXpert, a platform that integrates both traditional and fuzzy version...

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Bibliographic Details
Main Authors: Abdelghani Saoud, Mohamed Lachgar, Mohamed Hanine, Roa El Dhimni, Kawtar El Azizi, Hajar Machmoum
Format: Article
Language:English
Published: Elsevier 2025-02-01
Series:SoftwareX
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Online Access:http://www.sciencedirect.com/science/article/pii/S2352711024003972
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Summary:In the evolving field of decision support systems, there is a critical need for tools that can address complex multi-criteria decision-making (MCDA) challenges with flexibility and a user-centric approach. This paper presents decideXpert, a platform that integrates both traditional and fuzzy versions of the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Unlike many current systems that rely on standalone methods or lack user-friendly interfaces, decideXpert provides a cohesive, single-page application. It offers a guided experience that is immediately usable without the need for programming skills. Built with a robust and scalable technology stack – Java and Spring Boot for the backend, Angular for the frontend, and MySQL for data management – decideXpert ensures cross-platform compatibility and an intuitive user experience. Its integration of fuzzy logic enhances decision-making by effectively managing subjective judgments and uncertainties. Additionally, the platform enables collaborative decision-making by allowing multiple stakeholders to contribute their expertise, ensuring a more comprehensive evaluation process. This paper explores the platform’s architecture and key functionalities, demonstrating its practical utility through case studies that highlight its adaptability in complex decision-making contexts.
ISSN:2352-7110