Cooperative Ant Colony Algorithm for Flexible Manufacturing Systems

  • Asmaa Kamal
  • Iman BadrEmail author
  • Adel Darwish
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 803)


An ant colony optimization algorithm that accounts for the special requirements of Flexible Manufacturing Systems (FMS) is presented in this paper. The scheduling problem of FMS is conceived as a classical job shop scheduling problem (JSSP). The objective is to minimize the time taken for all jobs to finish execution (i.e. the makespan). The proposed algorithm accounts for the dynamicity of the FMS environment and automates the schedule update by incorporating newly arrived jobs to the existing schedule. The Ant Colony algorithm is applied to solve different discrete optimization problems by artificial ants, using indirect communication to make all routing decisions by reacting to their dynamically changing environment through cooperation between ants and updating their pheromone trails. The effectiveness of the algorithm proposed in this paper is investigated by examining numerical results, and the computational experiments have been executed based on the JSSP data benchmarks.


Flexible manufacturing systems Job shop schedule problem Re-scheduling Ant colony system 


  1. 1.
    Dorigo, M., Maniesso, V., Colorni, A.: Distributed Optimization by ant colonies. In: Proceedings of ECAL91—European Conference on Artificial Life- Elsevier, Paris, France, pp. 134–142 (1991)Google Scholar
  2. 2.
    Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evolut Comput 1(1), 53–66 (1997)CrossRefGoogle Scholar
  3. 3.
    Sycara, K., Roth, S., Sadeh, N., Fox, M.: Distributed constrained heuristic search. IEEE Trans. Syst. Man Cybern. 21(6), 1446–1461 (1991)CrossRefGoogle Scholar
  4. 4.
    Lawrynowicz, A.: Integration of production planning and scheduling using an export system and a genetic algorithm. J. Oper. Res. Soc. 59(4), 455–463 (2008)CrossRefGoogle Scholar
  5. 5.
    Badr, I.: An agent-based scheduling framework for flexible manufacturing systems. World Acad. Sci. Eng. Technol. Int. J. Mech. Aerosp. Ind. Mechatron. Manuf. Eng. 2(4), (2008)Google Scholar
  6. 6.
    Chen, J.C., Wu, C.-C., Chen, C.-W., Chen, K.-H.: Flexible Job Shop Scheduling with Parallel Machines Using Genetic Algorithm and Grouping Genetic Algorithm. Elsevier Ltd, Amsterdam (2012)CrossRefGoogle Scholar
  7. 7.
    Muthiah, A., Rajkumar, R., Rajkumar, A.: Hybridization of artificial bee colony algorithm with particle swarm optimization algorithm for flexible job shop scheduling. In: Proceedings of 2016 International Conference on Energy Efficient Technologies for Sustainability (ICEETS). IEEE Xplore Digital Library (2016).
  8. 8.
    Nouri, H.E., Driss, O.B., Ghédira, K.: A classification schema for the job shop scheduling problem with transportation resources: state-of-the-art review. In: Artificial Intelligence Perspectives in Intelligent Systems, Advances in Intelligent Systems and Computing, vol. 464. Springer, Switzerland (2016).
  9. 9.
    Nouri, H.E., Driss, O.B., Ghédira, K.: Simultaneous scheduling of machines and transport robots in flexible job shop environment using hybrid metaheuristics based on clustered holonic multi-agent model. Comput. Ind. Eng. 102, 488–501 (2016)CrossRefGoogle Scholar
  10. 10.
    Sahin, C., Demirtas, M., Erol, R., Baykasoğlu, A., Kaplanoğlu, V.: A multi-agent based approach to dynamic scheduling with flexible processing capabilities. J. Intell. Manuf., pp. 1–19 (2015)Google Scholar
  11. 11.
    Nakandhrakumar, R.S., Seralathan, S., Azarudeen, A., Narendran, V.: Optimization of job shop scheduling problem using tabu search optimization technique. Int. J. Innov. Res. Sci. Eng. Technol. 3(3), 1241–1244 (2014)Google Scholar
  12. 12.
    Gao, K.Z., Suganthan, P.N., Chua, T.J., Chong, C.S., Cai, T.X., Pan, Q.K.: A two-stage artificial bee colony algorithm scheduling flexible job-shop scheduling problem with new job insertion. Expert Syst. Appl. 42(21), 7652–7663 (2015)CrossRefGoogle Scholar
  13. 13.
    Kamal, A., Badr, I., Darwish, A.: A study on job scheduling problem for flexible manufacturing system based on ant colony system. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 5(10), 6–14 (2015)Google Scholar
  14. 14.
    Aryo, D.: ACO-Dynamic-JSSP, (2011)
  15. 15.
    Behnke, D., Geiger, M.J.: Test instances for the flexible job shop scheduling problem with work centers. Helmut-Schmidt-Universität der Bundeswehr Hamburg, Lehrstuhl für Betriebswirtschaftslehre, Insbes (2012)Google Scholar
  16. 16.
    Lawrence, S.: Supplement to Resource Constrained Project Scheduling: An Experimental Investigation of Heuristic Scheduling Techniques, Graduate School of Industrial Administration, Carnegie-Mellon University, Pittsburgh PA (1984)Google Scholar
  17. 17.
    Applegate, D., Cook, W.: A computational study of the job-shop scheduling problem. ORSA J. Comput. 3(2), 149–156 (1991)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Mathematics Department, Faculty of ScienceHelwan UniversityHelwanEgypt

Personalised recommendations