Application of an evolutionary algorithm-based ensemble model to job-shop scheduling
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In this paper, a novel evolutionary algorithm is applied to tackle job-shop scheduling tasks in manufacturing environments. Specifically, a modified micro genetic algorithm (MmGA) is used as the building block to formulate an ensemble model to undertake multi-objective optimisation problems in job-shop scheduling. The MmGA ensemble is able to approximate the optimal solution under the Pareto optimality principle. To evaluate the effectiveness of the MmGA ensemble, a case study based on real requirements is conducted. The results positively indicate the effectiveness of the MmGA ensemble in undertaking job-shop scheduling problems.
KeywordsMulti-objective optimisation Evolutionary algorithm Ensemble model Job-shop scheduling
The financial support of Collaborative Research in Engineering, Science and Technology (CREST) (Grant No. P05C2-14) is highly appreciated.
- Chakaravarthy, G., Marimuthu, S., Ponnambalam, S., & Kanagaraj, G. (2014). Improved sheep flock heredity algorithm and artificial bee colony algorithm for scheduling m-machine flow shops lot streaming with equal size sub-lot problems. International Journal of Production Research, 52(5), 1509–1527.CrossRefGoogle Scholar
- Chen, Y. (2011). Fuzzy skyhook surface control using micro-genetic algorithm for vehicle suspension ride comfort. In M. Kppen, G. Schaefer, & A. Abraham (Eds.), Intelligent computational optimization in engineering, studies in computational intelligence (Vol. 366, pp. 357–394). Berlin/Heidelberg: Springer.CrossRefGoogle Scholar
- Coello, C. A. C., Lamont, G. B., & Van Veldhuisen, D. A. (2007). Evolutionary algorithms for solving multi-objective problems. Genetic and evolutionary computation series (2nd ed.). London: Springer.Google Scholar
- Goldberg, D. E. (1989). Sizing populations for serial and parallel genetic algorithms. In Proceedings of the third international conference on genetic algorithms (pp. 70–79). San Francisco, CA: Morgan Kaufmann Publishers Inc.Google Scholar
- Hanoun, S., Nahavandi, S., Creighton, D., & Kull, H. (2012). Solving a multiobjective job shop scheduling problem using pareto archived cuckoo search. In IEEE 17th conference on emerging technologies factory automation, 2012 (ETFA 2012) (pp. 1–8).Google Scholar
- Hanoun, S., Nahavandi, S., & Kull, H. (2011). Pareto archived simulated annealing for single machine job shop scheduling with multiple objectives. In The sixth international multi-conference on computing in the global information technology (ICCGI 2011) (pp. 99–104). LuxembourgGoogle Scholar
- Kiani, M., & Yıldız, A. R. (2015). A comparative study of non-traditional methods for vehicle crashworthiness and NVH optimization. Archives of Computational Methods in Engineering. doi: 10.1007/s11831-015-9155-y.
- Pareto, V. (1971). Manual of political economy (A. S. Schwier, Trans.). New York: Augustus M. Kelley Publishers.Google Scholar
- Rohaninejad, M., Kheirkhah, A., Fattahi, P., & Vahedi-Nouri, B. (2015). A hybrid multi-objective genetic algorithm based on the electre method for a capacitated flexible job shop scheduling problem. The International Journal of Advanced Manufacturing Technology, 77(1–4), 51–66.CrossRefGoogle Scholar
- Sapp, B., Weiss, D., & Taskar, B. (2011). Parsing human motion with stretchable models. In IEEE conference on computer vision and pattern recognition, 2011 (CVPR 2011) (pp. 1281–1288). Providence.Google Scholar
- Tan, C. J., Lim, C. P., Cheah, Y. N., & Tan, S. C. (2013). Classification and optimization of product review information using soft computing models. In International symposium on affective engineering, 2013 (ISAE 2013) (pp. 115–120). Kitakyushu: Japan Society of Kansei Engineering.Google Scholar
- Tan, C. J., Samer, H., Lim, C. P., Creighton, D., & Nahavandi, S. (2015). A multi-objective evolutionary algorithm-based decision support system: A case study on job-shop scheduling in manufacturing. In: 2015 9th Annual IEEE international systems conference (SysCon) (pp. 170–174).Google Scholar
- Yıldız, A. R. (2013). Comparison of evolutionary-based optimization algorithms for structural design optimization. Engineering Applications of Artificial Intelligence, 26(1), 327–333. doi: 10.1016/j.engappai.2012.05.014.
- Zhang, J., Yang, J., & Zhou, Y. (2016). Robust scheduling for multi-objective flexible job-shop problems with flexible workdays. Engineering Optimization. doi: 10.1080/0305215X.2016.1145216.