Title: BUSINESS APPLICATIONS OF OPTIMISATION THEORY: ANT COLONY OPTIMIZATION AND SELECTED APPLICATIONS IN MANUFACTURING
Authors: Trung Minh Ngo
Abstract:

Ant Colony Optimization is a metaheuristic that was developed in the early 90s to solve optimization problems and finding for them good approximate solutions if not perfect ones, the developers of ant colony optimization algorithms took inspiration from the foraging behavior of ants in ant colonies, where although many ant species are close to blind, they have a surprisingly efficient technique in finding the shortest paths from their colony towards food sources and back. The first section of this paper introduces ant colony optimization origins and ideas, and the section after that discusses the main algorithms of ant colony optimization. The next chapter will include successful manufacturing applications for the algorithms. The last section will summarize the paper and discuss the findings. The main purpose of this paper is to discuss the ant colony optimization function and highlight how it has applications not only in theory but rather very consequential applications in business, namely, the world of manufacturing.

Keywords: Optimisation Theory, Ant Colony Optimization, Flexible Manufacturing, Virtual Cellular Manufacturing
DOI: https://doi.org/10.38193/IJRCMS.2023.5412
PDF Download
References:
–        Bianchi, L., Dorigo, M., Gambardella, L.M. and Gutjahr, W.J. (2008). A survey on metaheuristics for stochastic combinatorial optimization. Natural Computing, 8(2), pp.239–287.
–        Blum, C. (2005). Ant colony optimization: Introduction and recent trends. Physics of Life Reviews, 2(4), pp.353–373.
–        Cordon, O., Herrera, F. and Stutzle, T. (2002). a review of ant colony optimization metaheuristic: basis, models and new trends. Mathware & Soft Computing, 9(2–3).
–        Dorigo, M., Birattari, M. and Stutzle, T. (2006). Ant colony optimization. IEEE Computational Intelligence Magazine, 1(4), pp.28–39.
–        Dorigo, M. and Stützle, T. (2019). Ant Colony Optimization: Overview and Recent Advances. Handbook of Metaheuristics, pp.311–351.
–        Konak, A. and Kulturel-Konak, S. (2007). An Ant Colony Optimization Approach to the Minimum Tool Switching Instant Problem in Flexible Manufacturing System. 2007 IEEE Symposium on Computational Intelligence in Scheduling.
–        Koren, Y., Heisel, U., Jovane, F., Moriwaki, T., Pritschow, G., Ulsoy, G. and Van Brussel, H., 1999. Reconfigurable manufacturing systems. CIRP annals, 48(2), pp.527-540.
–        Mak, K.L., Peng, P., Wang, X.X. and Lau, T.L. (2007). An ant colony optimization algorithm for scheduling virtual cellular manufacturing systems. International Journal of Computer Integrated Manufacturing, 20(6), pp.524–537.
–        Maniraj, M., Pakkirisamy, V. and Jeyapaul, R. (2015). An ant colony optimization–based approach for a single-product flow-line reconfigurable manufacturing systems. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 231(7), pp.1229–1236.
–        Song, Y., Zhang, M.T., Yi, J., Zhang, L. and Zheng, L. (2007). Bottleneck Station Scheduling in Semiconductor Assembly and Test Manufacturing Using Ant Colony Optimization. IEEE Transactions on Automation Science and Engineering, 4(4), pp.569–578.
–        Tewolde, G.S. and Weihua Sheng (2008). Robot Path Integration in Manufacturing Processes: Genetic Algorithm Versus Ant Colony Optimization. IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans, 38(2), pp.278–287.
–        Tiwari, M.K., Dashora, Y., Kumar, S. and Shankar, R. (2006). Ant colony optimization to select the best process plan in an automated manufacturing environment. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 220(9), pp.1457–1472.