An Effective Job Scheduling for Maximum Resource Utilisation with Particle Swarm Optimisation

Authors

  • Mansir Abubakar College of Computing, Informatics and Mathematics, University Technology Mara, Shah Alam, Malaysia
  • Alwatben Batoul Rashed Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia
  • Sanusi Abu Darma College of Computing and Information Science, Al-Qalam University Katsina, Nigeria
  • Mardiyya Lawal Bagiwa College of Computing and Information Science, Al-Qalam University Katsina, Nigeria

DOI:

https://doi.org/10.21834/e-bpj.v10iSI31.6939

Keywords:

job scheduling, optimization, PSO, resource utilization

Abstract

This paper focuses on optimizing job schedules using Particle Swarm Optimization (PSO). The goal is to find a schedule that minimizes the time needed to complete all tasks. The algorithm is chosen as it can achieve fast convergence due to its swarm intelligence behavior and its capability to search in a global space. The results show that PSO can minimize the time of the job schedule effectively. This is very helpful for businesses in managing their resources efficiently, thus, reducing the cost. It benefits industries that involve complex scheduling such as manufacturing and logistics. The paper highlights the capabilities of PSO in optimizing job schedules and could be applied to solve various problems across different domains.

References

Alireza Rezvanian, S. Mehdi Vahidipour, & Sadollah, A. (2023). An Overview of Ant Colony Optimization Algorithms for Dynamic Optimization Problems. IntechOpen EBooks. https://doi.org/10.5772/intechopen.111839. DOI: https://doi.org/10.5772/intechopen.111839

Banu Çaliş, & Serol Bulkan. (2013). A research survey: a review of AI solution strategies for the job shop scheduling problem. Journal of Intelligent Manufacturing, 26(5), 961–973. https://doi.org/10.1007/s10845-013-0837-8. DOI: https://doi.org/10.1007/s10845-013-0837-8

Guo, J., & Lei, D. (2021). An imperialist competitive algorithm for energy-efficient flexible job shop scheduling. In 2021 33rd Chinese Control and Decision Conference (CCDC) (pp. 5145-5150). Kunming, China. https://doi.org/10.1109/CCDC52312.2021.9601714. DOI: https://doi.org/10.1109/CCDC52312.2021.9601714

H. M. Wang, C. Liu, P. P. Li and J. Y. Shen (2022) "Cloud Task Scheduling Based on Improved Particle Swarm Optimization Algorithm," 2022 Asia Conference on Advanced Robotics, Automation, and Control Engineering (ARACE), Qingdao, China, pp. 24-29, doi: 10.1109/ARACE56528.2022.00013. DOI: https://doi.org/10.1109/ARACE56528.2022.00013

Kaliappan, S., Paranthaman, V., Kamal, M. R., Avv, S., & Muthukannan, M. (2024). A Novel approach of particle swarm and ANT colony Optimization for task scheduling in the Cloud. https://doi.org/10.1109/confluence60223.2024.10463398. DOI: https://doi.org/10.1109/Confluence60223.2024.10463398

Liu, H., Wang, G., & Gui, W. (2019). A Multi-Objective Particle Swarm Optimization for Flexible Job-Shop Scheduling Problem with Transportation Time. IEEE Access, 7, 100327-100340. [doi: 10.1109/ACCESS.2019.2930222].

Potluri, S., Hamad, A. A., Godavarthi, D., & Basa, S. S. (2023). Enhanced task scheduling using optimized particle swarm optimization algorithm in a cloud computing environment. ICST Transactions on Scalable Information Systems. https://doi.org/10.4108/eetsis.4042. DOI: https://doi.org/10.4108/eetsis.4042

Sarathambekai, M., & Umamaheswari, B. (2017). A particle swarm optimization algorithm for multiprocessor scheduling problem. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 231(7), https://doi.org/10.1177/1748301816665521.

Subramoney, S., & Nyirenda, C. N. (2020). Workflow Scheduling in a Cloud–Fog Environment Using a Hybrid Metaheuristic, https://arxiv.org/abs/2012.00176.

Zarrouk, R., Bennour, I. E., & Jemai, A. (2019). Toward a two-level PSO for the FJS problem. In 2019 IEEE 17th World Symposium on Applied Machine Intelligence and Informatics (SAMI) (pp. 75-82). Herlany, Slovakia. https://doi.org/10.1109/SAMI.2019.8782738. DOI: https://doi.org/10.1109/SAMI.2019.8782738

Zhang, Q., Zhang, B., & Liang, W. (2022). Research on flexible job shop scheduling problems based on improved discrete particle swarm optimization. International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2022). https://doi.org/10.1117/12.2645548. DOI: https://doi.org/10.1117/12.2645548

Yen, G. G., & Ivers, J. (2009). Multi-swarm optimization for dynamic job shop scheduling. Journal of Intelligent Manufacturing, 20(6), 701–713. https://doi.org/10.1108/17563780910939237. DOI: https://doi.org/10.1108/17563780910939237

Zhang, S., Li, X., Zhang, B., & Wang, S. (2020). Multi-objective optimization in flexible assembly job shop scheduling using a distributed ant colony system. European Journal of Operational Research, 283(2), 441–460. https://doi.org/10.1016/j.ejor.2019.11.016. DOI: https://doi.org/10.1016/j.ejor.2019.11.016

Downloads

Published

2025-05-31

How to Cite

Abubakar, M., Rashed, A. B., Abu Darma, S., & Bagiwa, M. L. (2025). An Effective Job Scheduling for Maximum Resource Utilisation with Particle Swarm Optimisation. Environment-Behaviour Proceedings Journal, 10(SI31), 99–106. https://doi.org/10.21834/e-bpj.v10iSI31.6939