A Study on Solving Single Stage Batch Process Scheduling Problems with an Evolutionary Algorithm Featuring Bacterial Mutations
The short term scheduling of batch processes is an active research field of chemical engineering, that has been addressed by many different techniques over the last decades. These approaches, however, are unable to solve long-term scheduling problems due their size, and the vast number of discrete decisions they entail. Evolutionary algorithms already proved to be efficient for some classes of large scheduling problems, and recently, the utilization of bacterial mutations has shown promising results on other fields.
In this paper, an evolutionary algorithm featuring bacterial mutation is introduced to solve a case study of a single stage product scheduling problem. The solution performance of the algorithm was compared to a method from the literature. The results indicate that the proposed approach can find the optimal solution under relatively short execution times.
KeywordsSingle stage product scheduling Bacterial Evolutionary Algorithm
This research was supported by the ÚNKP-17-4 New National Excellence Program of the Ministry of Human Capacities. This research was supported by the EFOP-3.6.1-16-2016-00017; “Internationalization, initiatives to establish a new source of researchers and graduates, and development of knowledge and technological transfer as instruments of intelligent specializations at Szechenyi University” grant.
- 1.Balázs, K., Horváth, Z., Kóczy, L.T.: Different chromosome-based evolutionary approaches for the permutation flow shop problem. Acta Polytech. Hung. 9(2), 115–138 (2012)Google Scholar
- 4.Földesi, P., Botzheim, J., Kóczy, L.T.: Eugenic bacterial memetic algorithm for fuzzy road transport traveling salesman problem. Int. J. Innov. Comput. Inf. Control 7(5), 2775–2798 (2011)Google Scholar
- 7.Hegyhati, M., Friedler, F.: Overview of Industrial Batch Process Scheduling. Chem. Eng. Trans. 21, 895–900 (2010)Google Scholar
- 12.Nawa, N.E., Furuhashi, T.: A study on the effect of transfer of genes for the bacterial evolutionary algorithm. In: 1998 Proceedings of the Second International Conference on Knowledge-Based Intelligent Electronic Systems, KES 1998, vol. 3, pp. 585–590. IEEE (1998)Google Scholar
- 14.Nawa, N.E., Hashiyama, T., Furuhashi, T., Uchikawa, Y.: A study on fuzzy rules discovery using pseudo-bacterial genetic algorithm with adaptive operator. In: 1997 IEEE International Conference on Evolutionary Computation, pp. 589–593. IEEE (1997)Google Scholar
- 16.Ralphs, T., Shinano, Y., Berthold, T., Koch, T.: Parallel solvers for mixed integer linear optimization. Technical report 16T–014-R3, 16–74, ISE, Lehigh University and Zuse Institute Berlin (ZIB) (2016)Google Scholar
- 19.Smidla, J., Heckl, I.: S-graph based parallel algorithm to the scheduling of multipurpose batch plants. Chem. Eng. Trans. 21(1994), 937–942 (2010)Google Scholar