Abstract
Manufacturing Control Problems are still often solved by manual scheduling, that means only out of the workers experience. Modern algorithms, such as Ant Colony Optimization, have proved their capacity to solve this kind of problems. Nevertheless, they are only used exceptionally in real world. There are two main reasons for that. Firstly, an ant-based scheduling tool has to fit into the organizational structures of today’s companies, i.e. it has to be coupled with the Enterprise Resource Planning-system (ERP-system) used in the company, in order to ensure that the capacity of the colonies search is used as efficiently as possible. The second reason is the size of the real world shop floor scheduling problems. In order to be able to deal with that problem, the authors propose a continuously operating Ant Algorithm, which can easily adapt to sudden changes in the production system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bonabeau, E. Dorigo, M. Theraulaz, G.: Swarm Intelligence-From Natural to Artificial Systems, pp. 69–71, Oxford University Press, New York, NJ, 1999.
Davis, L.: Job Shop Scheduling with Genetic Algorithms. in: Grefenstette, J., Editor, Proceedings of an International Conference on Genetic Algorithms and their Applications, pp. 136–140, Hillsdale, Lawrence Erlbaum Associates, 1985.
Deneubourg, J.-L., et. al: Self-Organization Mechanisms in Ant Societies (II): Learning in Foraging and Division of Labour. Experientis Suppl. 54, pp. 177–196, 1987.
Dorigo, M.: The Ant System: Optimization by a colony of cooperating agents, in: IEEE Transactions on Systems, Man, and Cybernetics 26, (1), pp.29–41, 1996.
Fischer, M., Vogel, A., Teich, T., Fischer, J.: A new Ant Colony Algorithm for the Job Shop Scheduling Problem. in: Proceedings of the Genetic and Evolutionary Computation Conference, San Francisco, 2001.
Gambardella, L.M., Agazzi, G.: MACS-VRPTW: A Multiple Ant Colony System for Vehicle Routing Problems with Time Windows, in: Corne, D., Dorigo, M., Glover, F.(Eds.)-New Ideas in Optimization, pp.63–76, 1999.
Kaeschel, J., Meier, B., Fischer, M., Teich, T.: Real-World Applications: Evolutionary Real World Shop Floor Scheduling using Parallelization and Parameter Coevolution, in: Proceedings of the Genetic and Evolutionary Computation Conference, Las Vegas, 2000.
Merkle, D., Middendorf, M.: An Ant Algorithm with Global Pheromone Evaluation for Scheduling a Single Machine, in: Cagnoni, S., et al. (Eds.)-Real-World Applications of Evolutionary Computing, Proceedings of EvoWorkshops 2000, Edinburgh, LNCS 1803, pp.281–296.
Stuetzle, T., Hoos, H.: MAX-MIN Ant System for the Traveling Salesman Problem, 1997.
van der Zwaan, S., Marques, C.: Ant colony optimization for job shop scheduling. in: Proceedings of the Third Workshop on Genetic Algorithms and Artificial Life (GAAL 99), 1999.
World Wide Web Consortium (Eds.): Extensible Markup Language (XML), http://www.w3.org/XML, 24.03.2001.
Yamada, T., Nakano, R.: A Genetic Algorithm with Multi-Step Crossover for Job Shop Scheduling Problems. in: Proceedings of an International Conference on GAs in Engineering Systems: Innovations and Applications (GALESIA 1995), pp. 146–151, 1995.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vogel, A., Fischer, M., Jaehn, H., Teich, T. (2002). Real-World Shop Floor Scheduling by Ant Colony Optimization. In: Dorigo, M., Di Caro, G., Sampels, M. (eds) Ant Algorithms. ANTS 2002. Lecture Notes in Computer Science, vol 2463. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45724-0_26
Download citation
DOI: https://doi.org/10.1007/3-540-45724-0_26
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44146-5
Online ISBN: 978-3-540-45724-4
eBook Packages: Springer Book Archive