The hybrid firefly algorithm with the fuzzy movement method for solving a complex scheduling problem

Main Article Content

Ajchara Phu-ang

Abstract

This paper proposes a hybrid algorithm that applied the firefly algorithm (FA) with the new idea labeled as the fuzzy movement method for solving a complex scheduling problem called the flexible job shop scheduling. The step of the proposed algorithm is similar to the original FA, which is based on the concept of flashing behavior to attract the other fireflies. In order to improve the efficiency of the FA algorithm for the FJSP, the proposed algorithm introduces three new ideas. In the first idea, the genetic algorithm (GA) is used to generate the high quality of the initial population. Next, the self-adaptive roulette wheel selection which embedded in the GA introduced to increase the diversity of the machine selection process. Finally, the fuzzy movement method is presented to enhance the work balancing ability between the high workload machines and the low workload machines. The proposed algorithm has been evaluated with a benchmark data set and compared to the other algorithm. The experimental results demonstrate that the proposed algorithm can effectively solve the flexible job shop scheduling problem.

Article Details

How to Cite
[1]
A. Phu-ang, “The hybrid firefly algorithm with the fuzzy movement method for solving a complex scheduling problem ”, ECTI-CIT, vol. 15, no. 2, pp. 208 - 219, Apr. 2021.
Section
Research Article

References

[1] Tung Kuan Liu, Yeh Peng Chen and Jyh-Horng Chou, “Solving Distributed and Flexible Job-Shop Scheduling Problems for a Real-WorldFastener Manufacturer,” IEEE Access, Vol. 2, pp.1598-1606, 2014.

[2] Tiahnua Jiang and Chao Zhang, “Application of Grey Wolf Optimization for Solving Combinatorial Problems: Job Shop and Flexible Job Shop Scheduling Cases,” IEEE Access, Vol. 6, pp. 26231 - 26240, 2018.

[3] Yuan Yuan and Hua Xu, “Multi objective Flexible Job Shop Scheduling Using Memetic Algorithms,” IEEE Transactions on automation science and engineering, Vol. 12, No.1, pp. 336 - 353, 2015.

[4] Hao-Chin Chang, Yeh-Peng Chen, Tung-Kuan Liu, and Jyh-horng chou, “Solving the Flexible Job Shop Scheduling Problem with Makespan Optimization by Using a Hybrid Taguchi-Genetic Algorithm,” IEEE Access, Vol. 3, pp. 1740 - 1454, 2015.

[5] Thitipong Jamrus, Chen-Fu Chien, Mitsuo Gen, and Kanchana Sethanan, “Hybrid Particle Swarm Optimization Combined with Genetic Operators for Flexible Job-Shop Scheduling Under Uncertain Processing Time for Semiconductor Manufacturing,” IEEE Transactions on Semiconductor Manufacturing, Vol. 31, No.1, pp. 32 - 41, 2018.

[6] Yi Lu, Jiacheng Lu and Tianhua Jiang, “Energy-Conscious Scheduling Problem in a Flexible Job Shop Using a Discrete Water Wave Optimization Algorithm,” IEEE Access, Vol. 7, pp. 101561- 101574, 2019.

[7] Xin-She Yang, “Firefly Algorithms for Multimodal Optimization,” International Symposium on Stochastic Algorithms: SAGA 2009: Stochastic Algorithms: Foundations and Applications, Vol. 5792., pp 169-178, 2019.

[8] Sinha, Pankaj and Chandwani, Abhishek and Sinha, Tanmay, “Algorithm of construction of Optimum Portfolio of stocks using Genetic Algorithm,” Munich Personal RePEc Archive, pp. 1-34, 2013.

[9] Martin Hellmann, Fuzzy Logic Introduction, TU-Berlin, 2001.

[10] Raul Rojas, Fuzzy Logic, Neural Networks, Springer-Verlag, Berlin, 1996, ch. 11.

[11] http://people.idsia.ch/~monaldo/fjsp.html

[12] Jie Gaoa, Linyan Sun, Mitsuo Gen, “A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems,” Computers & Operations Research, Vol. 35, pp. 2892 – 2907, 2008.

[13] Yuan Yuan and Hua Xu, “Flexible job shop scheduling using hybrid differential evolution algorithms,” Computers & Industrial Engineering, Vol. 65, pp.246-260, 2013.