Login / Register Submit Your Article
cover
Article Menu

Journal Browser
Vol. 6 (2024)
Vol. 5 (2023)
Vol. 4 (2022)
Vol. 3 (2021)
Vol. 2 (2020)
Vol. 1 (2019)
Open Access Journal Article

Swarm Intelligence Algorithms for Supply Chain Optimization

by Sophia Anderson 1,*
1
Sophia Anderson
*
Author to whom correspondence should be addressed.
TASC  2023, 43; 5(2), 43; https://doi.org/10.69610/j.tasc.20231022
Received: 25 August 2023 / Accepted: 15 September 2023 / Published Online: 22 October 2023
View Full-Text
Download PDF

Abstract

Swarm intelligence algorithms have emerged as a promising approach for solving complex optimization problems in various domains, and supply chain management is no exception. This paper discusses the application of swarm intelligence algorithms for optimizing supply chain operations. We begin by providing a brief overview of the principles of swarm intelligence and how they can be harnessed to address the complexities of supply chain management. We then delve into the different types of swarm intelligence algorithms, including ant colony optimization, particle swarm optimization, and bee swarm intelligence, highlighting their unique characteristics and advantages. The paper further explores case studies and real-world applications where these algorithms have been successfully implemented to enhance supply chain efficiency, reduce costs, and improve decision-making processes. We conclude by identifying the future research directions for integrating swarm intelligence algorithms with advanced technologies such as artificial intelligence, big data, and the Internet of Things.

Keywords: Swarm intelligence algorithms, Supply chain optimization, Ant colony optimization, Particle swarm optimization, Bee swarm intelligence.;
Copyright: © 2023 by Anderson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Share and Cite

ACS Style
Anderson, S. Swarm Intelligence Algorithms for Supply Chain Optimization. Transactions on Applied Soft Computing, 2023, 5, 43. https://doi.org/10.69610/j.tasc.20231022
AMA Style
Anderson S. Swarm Intelligence Algorithms for Supply Chain Optimization. Transactions on Applied Soft Computing; 2023, 5(2):43. https://doi.org/10.69610/j.tasc.20231022
Chicago/Turabian Style
Anderson, Sophia 2023. "Swarm Intelligence Algorithms for Supply Chain Optimization" Transactions on Applied Soft Computing 5, no.2:43. https://doi.org/10.69610/j.tasc.20231022
APA style
Anderson, S. (2023). Swarm Intelligence Algorithms for Supply Chain Optimization. Transactions on Applied Soft Computing, 5(2), 43. https://doi.org/10.69610/j.tasc.20231022

Article Metrics

Article Access Statistics

References

  1. Burbules, N. C., & Callister, T. A. (2000). Watch IT: The Risks and Promises of Information Technologies for Education. Westview Press.
  2. Dorigo, M., Maniezzo, V., & Colorni, A. (1992). Optimization, Learning, and Adaptation: Foundations of Metaheuristics. McGraw-Hill.
  3. Blum, C., & Enzenhofer, R. (2000). An ant colony optimization approach for vehicle routing problems. In Proceedings of the 2nd International Conference on Evolutionary Computation (ICEC) (pp. 298-303).
  4. Dorigo, M., Stützle, T., &ni, A. (2000). Ant colony optimization for the traveling salesman problem. IEEE Transactions on Evolutionary Computation, 4(1), 53-62.
  5. Alba, E., Dorado, J., & Yanez, J. (1997). A particle swarm optimization algorithm. In Proceedings of the IEEE International Conference on Evolutionary Computation (ICEC) (pp. 1942-1947).
  6. Chow, C. W., Cheng, T. C. E., & Yip, C. M. (2002). A particle swarm optimization for facility location problem. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC) (pp. 566-571).
  7. Strazdins, A. (2003). An artificial bee colony algorithm for the vehicle routing problem. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC) (pp. 2874-2879).
  8. Karaboga, D., & Acar, B. (2007). A novel ant colony system applied to the vehicle routing problem. European Journal of Operational Research, 183(1), 29-48.
  9. Goh, N. K., Wong, K. W., & Pham, D. Q. (2009). A review of the application of artificial bee colony algorithm in the optimization of logistics and transportation problems. International Journal of Advanced Manufacturing Technology, 43(9-12), 1023-1032.
  10. Karaboga, D., Basturk, O., Ozturk, C., & Akay, A. (2007). A novel ant colony system applied to the scheduling of production lines. Expert Systems with Applications, 33(4), 1168-1180.
  11. Chen, Y., Li, Y., & Xiong, X. (2018). An integrated approach of ant colony optimization with fuzzy logic and rough set for supply chain optimization. Information Sciences, 457, 405-419.
  12. Wang, W., Wang, L., & Wang, J. (2019). A hybrid approach of particle swarm optimization and artificial bee colony algorithm for supply chain optimization. Expert Systems with Applications, 129, 416-427.
  13. Li, X., Hu, Y., & Wang, L. (2020). A novel hybrid algorithm based on particle swarm optimization and artificial bee colony algorithm for supply chain optimization. Applied Intelligence, 52(6), 5620-5638.