Cloud-Based Integrated Process Planning and Scheduling Optimisation via Asynchronous Islands

  • Shuai ZhaoEmail author
  • Haitao Mei
  • Piotr Dziurzanski
  • Michal Przewozniczek
  • Leandro Soares Indrusiak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11819)


In this paper, we present Optimisation as a Service (OaaS) for an integrated process planning and scheduling in smart factories based on a distributed multi-criteria genetic algorithm (GA). In contrast to the traditional distributed GA following the island model, the proposed islands are executed asynchronously and exchange solutions at time points depending solely on the optimisation progress at each island. Several solutions’ exchange strategies are proposed, implemented in Amazon Elastic Container Service for Kubernetes (Amazon EKS) and evaluated using a real-world manufacturing problem.


Optimisation as a Service Multi-objective Genetic Algorithm Island model Amazon EKS Integrated process planning and scheduling 



The authors acknowledge the support of the EU H2020 SAFIRE project (Ref. 723634).


  1. 1.
    Dziurzanski, P., Zhao, S., Swan, J., Indrusiak, L.S., Scholze, S., Krone, K.: Solving the multi-objective flexible job-shop scheduling problem with alternative recipes for a chemical production process. In: Kaufmann, P., Castillo, P.A. (eds.) EvoApplications 2019. LNCS, vol. 11454, pp. 33–48. Springer, Cham (2019). Scholar
  2. 2.
    Méndez, C.A., et al.: State-of-the-art review of optimization methods for short-term scheduling of batch processes. Comput. Chem. Eng. 30(6–7), 913–946 (2006)CrossRefGoogle Scholar
  3. 3.
    Lemaignan, S., Siadat, A., Dantan, J., Semenenko, A.: MASON: a proposal for an ontology of manufacturing domain. In: IEEE Workshop on Distributed Intelligent Systems: Collective Intelligence and Its Applications (DIS 2006), pp. 195–200, June 2006Google Scholar
  4. 4.
    Dziurzanski, P., Swan, J., Indrusiak, L.S., Ramos, J.: Implementing digital twins of smart factories with interval algebra. In: 2019 IEEE International Conference on Industrial Technology, ICIT 2019 (2019)Google Scholar
  5. 5.
    Di Martino, S., Ferrucci, F., Maggio, V., Sarro, F.: Towards migrating genetic algorithms for test data generation to the cloud (2012)Google Scholar
  6. 6.
    Zhao, S., Dziurzanski, P., Przewozniczek, M., Komarnicki, M., Indrusiak, L.S.: Cloud-based dynamic distributed optimisation of integrated process planning and scheduling in smart factories. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO 2019. ACM, New York (2019)Google Scholar
  7. 7.
    Thierens, D.: Scalability problems of simple genetic algorithms. Evol. Comput. 7(4), 331–352 (1999). Scholar
  8. 8.
    Leclerc, G., Auerbach, J.E., Iacca, G., Floreano, D.: The seamless peer and cloud evolution framework. In: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, pp. 821–828. ACM (2016)Google Scholar
  9. 9.
    Ma, N., Liu, X.F., Zhan, Z.H., Zhong, J.H., Zhang, J.: Load balance aware distributed differential evolution for computationally expensive optimization problems. In: 2017 GECCO Proceedings Companion, pp. 209–210. ACM (2017)Google Scholar
  10. 10.
    Melab, N., Mezmaz, M., Talbi, E.: Parallel hybrid multi-objective island model in peer-to-peer environment. In: 19th IEEE International Parallel and Distributed Processing Symposium. pp. 9–pp, April 2005Google Scholar
  11. 11.
    Enterprise Application Container Platform. Accessed 19 Apr 2019
  12. 12.
    Salza, P., Ferrucci, F., Sarro, F.: Develop, deploy and execute parallel genetic algorithms in the cloud. In: 2016 GECCO Proceedings Companion, pp. 121–122. ACM (2016)Google Scholar
  13. 13.
    Kubernetes: Production-Grade Container Orchestration. Accessed 19 Apr 2019
  14. 14.
    García-Valdez, J.M., Merelo-Guervós, J.J.: A modern, event-based architecture for distributed evolutionary algorithms. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2018, pp. 233–234. ACM, New York (2018)Google Scholar
  15. 15.
    Nogueras, R., Cotta, C.: An analysis of migration strategies in island-based multimemetic algorithms. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds.) PPSN 2014. LNCS, vol. 8672, pp. 731–740. Springer, Cham (2014). Scholar
  16. 16.
    Ishibuchi, H., Masuda, H., Tanigaki, Y., Nojima, Y.: Modified distance calculation in generational distance and inverted generational distance. In: Gaspar-Cunha, A., Henggeler Antunes, C., Coello, C.C. (eds.) EMO 2015. LNCS, vol. 9019, pp. 110–125. Springer, Cham (2015). Scholar
  17. 17.
    Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Schoenauer, M., et al. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 849–858. Springer, Heidelberg (2000). Scholar
  18. 18.
    Li, M., Yang, S., Liu, X.: Diversity comparison of pareto front approximations in many-objective optimization. IEEE Trans. Cybern. 44(12), 2568–2584 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Shuai Zhao
    • 1
    Email author
  • Haitao Mei
    • 2
  • Piotr Dziurzanski
    • 1
  • Michal Przewozniczek
    • 1
  • Leandro Soares Indrusiak
    • 1
  1. 1.Department of Computer ScienceUniversity of YorkYorkUK
  2. 2.IBM YorkYorkUK

Personalised recommendations