A Hybrid Optimization Algorithm for Enhancing Transportation and Logistics Scheduling in IoT-Enabled Supply Chains

Jihad, AA ORCID: 0000-0002-3191-2665, Abdalkafor, AS ORCID: 0000-0001-5004-2282, Yassen, ET ORCID: 0000-0002-6980-6606 and Aldhaibani, OA ORCID: 0000-0003-0235-2862 (2026) A Hybrid Optimization Algorithm for Enhancing Transportation and Logistics Scheduling in IoT-Enabled Supply Chains. Sensors, 26 (3). ISSN 1424-8220

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Abstract

IoT-integrated supply chains play an important role in managing the movement of products and distribution, which relies on the processing of real-time data gathered using sensors and IoT-connected vehicles to make informed decisions that reduce logistical expenses. However, the optimization of transportation and logistics scheduling is still one of the most difficult tasks, which requires balancing demand and vehicle capacity, as well as delivery time in varying circumstances. This research assesses the performance capabilities and utility of four optimization algorithms, differential evolution (DE), a genetic algorithm (GA), simulated annealing (SA), and prism refraction search (PRS), which are applicable in IoT-integrated logistical processes. Notably, on the basis of the unique characteristics possessed by the four algorithms, a combination approach referred to as Bidirectional PRS-SA Optimization (Bi-PRS-SA) was formulated. This method ideally exploits the strengths of global and local searches within the search space. Furthermore, the research aims to discuss the proposed conceptual framework for integrating the proposed strategy into an overall IoT framework that would initiate dynamic supply chain management through the adaptation of the proposed strategy. Results show that the proposed strategy is better than the existing strategies of DE, GAs, SA, and PRS in terms of an overall range of 15–25%. Statistical validation via the Wilcoxon signed-rank test confirms these improvements are significant (p < 0.05). The findings suggest that the Bi-PRS-SA framework offers a robust and scalable solution for real-time logistics management in IoT-enabled environments.

Item Type:	Article
Uncontrolled Keywords:	IoT-enabled supply chain; hybrid optimization algorithm; operational efficiency; prism refraction search (PRS); transportation and logistics scheduling; 4605 Data Management and Data Science; 46 Information and Computing Sciences; 0301 Analytical Chemistry; 0502 Environmental Science and Management; 0602 Ecology; 0805 Distributed Computing; 0906 Electrical and Electronic Engineering; Analytical Chemistry; 3103 Ecology; 4008 Electrical engineering; 4009 Electronics, sensors and digital hardware; 4104 Environmental management; 4606 Distributed computing and systems software
Subjects:	H Social Sciences > HE Transportation and Communications Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Computer Science and Mathematics
Publisher:	MDPI AG
Date of acceptance:	29 January 2026
Date of first compliant Open Access:	4 March 2026
Date Deposited:	04 Mar 2026 11:14
Last Modified:	04 Mar 2026 11:14
DOI or ID number:	10.3390/s26030932
URI:	https://researchonline.ljmu.ac.uk/id/eprint/28183

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