# Genetic Algorithm Combined with Tabu Search in a Holonic Multiagent Model for Flexible Job Shop Scheduling Problem

### Houssem Eddine Nouri, Olfa Belkahla Driss, Khaled Ghédira

#### Abstract

The Flexible Job Shop scheduling Problem (FJSP) is an extension of the classical Job Shop scheduling Problem (JSP) presenting an additional difficulty caused by the operation assignment problem on one machine out of a set of alternative machines. The FJSP is an NP-hard problem composed by two complementary problems, which are the assignment and the scheduling problems. In this paper, we propose a combination of a genetic algorithm with a tabu search in a holonic multiagent model for the FJSP. In fact, firstly, a scheduler agent applies a genetic algorithm for a global exploration of the search space. Then, secondly, a local search technique is used by a set of cluster agents to guide the research in promising regions of the search space and to improve the quality of the final population. To evaluate our approach, numerical tests are made based on two sets of well known benchmark instances in the literature of the FJSP: Kacem and Brandimarte. The experimental results show that our approach is efficient in comparison to other approaches.

#### References

- Andresen, D., Kota, S., Tera, M., and Bower, T. (2002). An ip-level network monitor and scheduling system for clusters. In PDPTA, pages 789-795. CSREA Press.
- Azzouz, A., Ennigrou, M., Jlifi, B., and Ghédira, K. (2012). Combining tabu search and genetic algorithm in a multi-agent system for solving flexible job shop problem. In Proceedings of the 2012 11th Mexican International Conference on Artificial Intelligence, MICAI'12, pages 83-88, Washington, DC, USA. IEEE Computer Society.
- Bellifemine, F., Poggi, A., and Rimassa, G. (1999). Jade - a fipa-compliant agent framework. In In Proceedings of the fourth International Conference and Exhibition on The Practical Application of Intelligent Agents and Multi-Agent Technology, pages 97-108.
- Botti, V. and Giret, A. (2008). ANEMONA: A Multiagent Methodology for Holonic Manufacturing Systems. Springer Series in Advanced Manufacturing. Springer-Verlag.
- Bozejko, W., Uchronski, M., and Wodecki, M. (2010a). The new golf neighborhood for the flexible job shop problem. In In Proceedings of the International Conference on Computational Science, pages 289-296.
- Bozejko, W., Uchronski, M., and Wodecki, M. (2010b). Parallel hybrid metaheuristics for the flexible job shop problem. Computers and Industrial Engineering, 59(2):323-333.
- Brandimarte, P. (1993). Routing and scheduling in a flexible job shop by tabu search. Annals of Operations Research, 41(3):157-183.
- Calabrese, M. (2011). Hierarchical-granularity holonic modelling. Doctoral thesis, Universita degli Studi di Milano, Milano, Italy.
- Chen, H., Ihlow, J., and Lehmann, C. (1999). A genetic algorithm for flexible job-shop scheduling. In In Proceedings of the IEEE International Conference on Robotics and Automation, pages 1120-1125.
- Dauzère-Pérès, S. and Paulli, J. (1997). An integrated approach for modeling and solving the general multiprocessor job-shop scheduling problem using tabu search. Annals of Operations Research, 70(0):281-306.
- Ennigrou, M. and Ghédira, K. (2004). Approche multiagents basée sur la recherche tabou pour le job shop flexible. In In 14ème Congrès Francophone AFRIFAFIA de Reconnaissance des Formes et Intelligence Artificielle RFIA, pages 28-30.
- Ennigrou, M. and Ghédira, K. (2008). New local diversification techniques for the flexible job shop problem with a multi-agent approach. Autonomous Agents and Multi-Agent Systems, 17(2):270-287.
- Ferber, J. (1999). Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1st edition.
- Gao, J., Sun, L., and Gen, M. (2008). A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems. Computers and Operations Research, 35(9):2892-2907.
- Garey, M. R., Johnson, D. S., and Sethi, R. (1976). The complexity of flow shop and job shop scheduling. Mathematics of Operations Research, 1(2):117-129.
- Giret, A. and Botti, V. (2004). Holons and agents. Journal of Intelligent Manufacturing, 15(5):645-659.
- Glover, F., Kelly, J. P., and Laguna, M. (1995). Genetic algorithms and tabu search: Hybrids for optimization. Computers & Operations Research, 22(1):111-134.
- Henchiri, A. and Ennigrou, M. (2013). Particle swarm optimization combined with tabu search in a multi-agent model for flexible job shop problem. In In Proceedings of the 4th International Conference on Swarm Intelligence, Advances in Swarm Intelligence, pages 385-394.
- Ho, N. B., Tay, J. C., and Lai, E. M. K. (2007). An effective architecture for learning and evolving flexible job-shop schedules. European Journal of Operational Research, 179(2):316-333.
- Hurink, J., Jurisch, B., and Thole, M. (1994). Tabu search for the job-shop scheduling problem with multi-purpose machines. Operations Research Spektrum, 15(4):205-215.
- Jia, H., Nee, A., Fuh, J., and Zhang, Y. (2003). A modified genetic algorithm for distributed scheduling problems. Journal of Intelligent Manufacturing, 14(3):351-362.
- Johnson, S. C. (1967). Hierarchical clustering schemes. Psychometrika, 32(3):241-254.
- Jones, A. and Rabelo, L. C. (1998). Survey of job shop scheduling techniques. In National Institute of Standards and Technology, Gaithersburg, MD.
- Kacem, I., Hammadi, S., and Borne, P. (2002a). Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems. IEEE Transactions on Systems, Man, and Cybernetics, 32(1):1-13.
- Kacem, I., Hammadi, S., and Borne, P. (2002b). Paretooptimality approach for flexible job-shop scheduling problems: Hybridization of evolutionary algorithms and fuzzy logic. Mathematics and Computers in Simulation, 60(3-5):245-276.
- Koestler, A. (1967). The Ghost in the Machine. Hutchinson, London, United Kingdom, 1st edition.
- Lee, K., Yamakawa, T., and Lee, K. M. (1998). A genetic algorithm for general machine scheduling problems. In In Proceedings of the second IEEE international Conference on Knowledge-Based Intelligent Electronic Systems, pages 60-66.
- Li, J., Pan, Q., Suganthan, P., and Chua, T. (2011). A hybrid tabu search algorithm with an efficient neighborhood structure for the flexible job shop scheduling problem. The International Journal of Advanced Manufacturing Technology, 52(5):683-697.
- Mastrolilli, M. and Gambardella, L. (2000). Effective neighbourhood functions for the flexible job shop problem. Journal of Scheduling, 3(1):3-20.
- Moslehi, G. and Mahnam, M. (2011). A pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search. International Journal of Production Economics, 129(1):14-22.
- Sonmez, A. I. and Baykasoglu, A. (1998). A new dynamic programming formulation of (nm) flow shop sequencing problems with due dates. International Journal of Production Research, 36(8):2269-2283.
- Thamilselvan, R. and Balasubramanie, P. (2009). Integrating genetic algorithm, tabu search approach for job shop scheduling. International Journal of Computer Science and Information Security, 2(1):1-6.
- Xia, W. and Wu, Z. (2005). An effective hybrid optimization approach for multiobjective flexible jobshop scheduling problems. Computers and Industrial Engineering, 48(2):409-425.
- Xing, L., Chen, Y., Wang, P., Zhao, Q., and Xiong, J. (2010). A knowledge-based ant colony optimization for flexible job shop scheduling problems. Applied Soft Computing, 10(3):888-896.
- Yazdani, M., Amiri, M., and Zandieh, M. (2010). Flexible job-shop scheduling with parallel variable neighborhood search algorithm. Expert Systems with Applications, 37(1):678-687.
- Zhang, C., Gu, P., and Jiang, P. (2014). Low-carbon scheduling and estimating for a flexible job shop based on carbon footprint and carbon efficiency of multi-job processing. Journal of Engineering Manufacture, 39(32):1-15.
- Zhang, G., Gao, L., and Shi, Y. (2010). A genetic algorithm and tabu search for multi objective flexible job shop scheduling problems. In International Conference on Computing, Control and Industrial Engineering, volume 1, pages 251-254.
- Zhang, G., Shao, X., Li, P., and Gao, L. (2009). An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem. Computers and Industrial Engineering, 56(4):1309- 1318.
- Zhang, G., Shi, Y., and Gao, L. (2008). A genetic algorithm and tabu search for solving flexible job shop schedules. International Symposium on Computational Intelligence and Design, 1:369-372.
- Ziaee, M. (2014). A heuristic algorithm for solving flexible job shop scheduling problem. The International Journal of Advanced Manufacturing Technology, 71(1- 4):519-528.

#### Paper Citation

#### in Harvard Style

Nouri H., Belkahla Driss O. and Ghédira K. (2015). **Genetic Algorithm Combined with Tabu Search in a Holonic Multiagent Model for Flexible Job Shop Scheduling Problem** . In *Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS,* ISBN 978-989-758-096-3, pages 573-584. DOI: 10.5220/0005348105730584

#### in Bibtex Style

@conference{iceis15,

author={Houssem Eddine Nouri and Olfa Belkahla Driss and Khaled Ghédira},

title={Genetic Algorithm Combined with Tabu Search in a Holonic Multiagent Model for Flexible Job Shop Scheduling Problem},

booktitle={Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},

year={2015},

pages={573-584},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0005348105730584},

isbn={978-989-758-096-3},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS,

TI - Genetic Algorithm Combined with Tabu Search in a Holonic Multiagent Model for Flexible Job Shop Scheduling Problem

SN - 978-989-758-096-3

AU - Nouri H.

AU - Belkahla Driss O.

AU - Ghédira K.

PY - 2015

SP - 573

EP - 584

DO - 10.5220/0005348105730584