A Decision Support Model for Real-Time Quality Control and Logistics Tracking under Spherical Fuzzy Aczel-Alsina Aggregation Information with Z-Numbers

Authors

  • Shahzaib Ashraf Institute of Mathematics, Khwaja Fareed University of Engineering & Information Technology, Rahim Yar Khan 64200, Pakistan Author https://orcid.org/0000-0002-8616-8829
  • Maria Akram Institute of Mathematics, Khwaja Fareed University of Engineering & Information Technology, Rahim Yar Khan 64200, Pakistan Author https://orcid.org/0009-0004-3363-3805
  • Vladimir Simic Sustainability Competence Centre, Széchenyi István University, Egyetem tér 1, 9026 Győr, Hungary Author https://orcid.org/0009-0000-4941-7059
  • Hafiz Muhammad Athar Farid Department of Computer Science, University of Huddersfield, Huddersfield, United Kingdom Author https://orcid.org/0000-0002-8318-0750

DOI:

https://doi.org/10.31181/jopi41202676

Keywords:

Quality Control, Logistics Tracking, Spherical Fuzzy Z-Numbers, Decision-Making, Aczel-Alsina Aggregation Operators

Abstract

This research presents a new method for real-time logistics monitoring and quality control in supply chain management using spherical fuzzy Z-number Aczel–Alsina (AA) aggregation. A method based on quantum cognitive theory is employed to improve the accuracy and efficiency of logistics tracking. The proposed method provides a robust framework for decision-making when dealing with dynamic and unpredictable real-time logistics data. The results demonstrate improvements in supply chain transparency and logistics tracking accuracy, which are important for timely and informed logistics management decisions. This research employs a multi-attribute group decision-making (MAGDM) approach to address this complex problem. The main aim of this paper is to propose and explore spherical fuzzy Z-number sets (SFZNSs). Compared with existing fuzzy set structures, SFZNSs provide a more flexible framework for representing higher levels of uncertainty. We introduce an innovative distance measure for spherical fuzzy Z-numbers and derive unknown weight information using spherical fuzzy entropy calculations. Additionally, aggregation operators based on the AA t-norm and t-conorm are introduced to address MAGDM problems within the spherical fuzzy Z-number environment. The proposed method is particularly useful when the weights of the criteria and decision makers are unknown. By integrating AA operations within the spherical fuzzy Z-number framework, the proposed methodology provides a flexible and robust approach to information aggregation, enabling complex and uncertain information to be effectively represented and processed in decision-making problems.

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Published

2026-10-05

Issue

Vol. 4 No. 1 (2026): Journal of Operations Intelligence

Section

Articles

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Copyright (c) 2026 Shahzaib Ashraf, Maria Akram, Vladimir Simic, Hafiz Muhammad Athar Farid (Author)

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Ashraf, S., Akram, M., Simic, V., & Farid, H. M. A. (2026). A Decision Support Model for Real-Time Quality Control and Logistics Tracking under Spherical Fuzzy Aczel-Alsina Aggregation Information with Z-Numbers. Journal of Operations Intelligence, 4(1), 95-128. https://doi.org/10.31181/jopi41202676