Abdelaziz Foul

Papers

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About

Abdelaziz Foul is a leading researcher in the fields of decision science, artificial intelligence, and logistics optimization, with a particular focus on integrating neutrosophic logic into multi-criteria decision-making models. His most-cited work, "Multiple attribute decision-making model for artificially intelligent last-mile delivery robots selection in neutrosophic square root environment" (2024, 8 citations), introduces a novel framework that combines neutrosophic set theory with square root transformations to evaluate and select autonomous delivery robots. This contribution addresses critical challenges in urban logistics by providing a robust, uncertainty-handling tool for decision-makers, enhancing the efficiency and reliability of last-mile delivery systems. Foul’s research bridges theoretical advancements in fuzzy logic with practical applications in smart transportation and Industry 4.0, offering scalable solutions for real-world deployment. His work has been recognized for its innovative approach to handling indeterminate and inconsistent data, making him a key figure in the evolution of intelligent logistics. With a growing citation impact, Foul continues to shape how artificial intelligence and decision models can optimize complex, real-time operational choices in dynamic environments.