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.
This research aimed to study the Improved Differential Evolution (IDE) for solving the Multi-floor Facility Layout Problem (MFLP) with the target of minimize the transporting material costand maximize adjacency requirement between the facilities. The IDE algorithm had been evaluated and compared with MULTIPLE, SABLE and the basic DE algorithm by using DE/rand/1/bin and DE/rand/2/bin. For MFLP, the IDE methods were tested with 6 data sets as following:
This research aimed to study the Differential Evolution (DE) for solving the Multi-floor Facility Layout Problem. (MFLP) with the target of minimize the transporting material cost.The DE algorithm had been evaluated and would be compared with MULTIPLE and SABLE algorithm. For MFLP, the Differential Evolution algorithm (DE) methods were tested with 6 data sets as following: 11-1, 11-2, 12, 21-1, 21-2, 21-3 by using DE/rand/1/bin and DE/rand/2/bin which found that all methods able to find the optimal solution better than the MULTIPLE. DE/rand/2/bin is having more effective than SABLE which calculate the comparison in percentage ratio as followings: 3.7, 11.5, 0.1 and 21.7% of problems 11-1, 21-1, 21-2 and 21-3, respectively. DE-rand/1-bin is having more effective than SABLE which calculate the comparison in percentage ratio as followings: 3.5, 4.5 and 22.3% of problems 11-1, 21-1 and 21-3, respectively. The result showed that the further performed DE by using basic DE were effective methods comparing to the other algorithms and other metaheuristic methods. Hence, they could be used to solve MFLP.
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