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Improved Differential Evolution Algorithm for Flexible Job Shop Scheduling Problems

Industrial Engineering, Department, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand
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Math. Comput. Appl. 2019, 24(3), 80; https://doi.org/10.3390/mca24030080
Received: 20 July 2019 / Revised: 19 August 2019 / Accepted: 5 September 2019 / Published: 6 September 2019
(This article belongs to the Special Issue Numerical and Evolutionary Optimization)
This research project aims to study and develop the differential evolution (DE) for use in solving the flexible job shop scheduling problem (FJSP). The development of algorithms were evaluated to find the solution and the best answer, and this was subsequently compared to the meta-heuristics from the literature review. For FJSP, by comparing the problem group with the makespan and the mean relative errors (MREs), it was found that for small-sized Kacem problems, value adjusting with “DE/rand/1” and exponential crossover at position 2. Moreover, value adjusting with “DE/best/2” and exponential crossover at position 2 gave an MRE of 3.25. For medium-sized Brandimarte problems, value adjusting with “DE/best/2” and exponential crossover at position 2 gave a mean relative error of 7.11. For large-sized Dauzere-Peres and Paulli problems, value adjusting with “DE/best/2” and exponential crossover at position 2 gave an MRE of 4.20. From the comparison of the DE results with other methods, it was found that the MRE was lower than that found by Girish and Jawahar with the particle swarm optimization (PSO) method (7.75), which the improved DE was 7.11. For large-sized problems, it was found that the MRE was lower than that found by Warisa (1ST-DE) method (5.08), for which the improved DE was 4.20. The results further showed that basic DE and improved DE with jump search are effective methods compared to the other meta-heuristic methods. Hence, they can be used to solve the FJSP. View Full-Text
Keywords: improved differential evolution algorithm; flexible job shop scheduling problem; local search and jump search improved differential evolution algorithm; flexible job shop scheduling problem; local search and jump search
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Sriboonchandr, P.; Kriengkorakot, N.; Kriengkorakot, P. Improved Differential Evolution Algorithm for Flexible Job Shop Scheduling Problems. Math. Comput. Appl. 2019, 24, 80.

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