Scheduling Flexible Assembly Lines Using Differential Evolution
This paper investigates the performance of Differential Evolution (DE) in solving a Flexible Assembly Line (FAL) scheduling problem. Using a mathematical model developed in literature, the DE algorithm is implemented with the objectives of minimizing the sum of Earliness/Tardiness (E/T) penalties and maximizing the balance of the FAL. Experimental results have shown that DE is capable of solving the FAL scheduling problem effectively. Furthermore, a comparison with similar work in literature which employs Genetic Algorithm (GA) shows that DE produces a better solution.
KeywordsSchedule Problem Differential Evolution Differential Evolution Algorithm Machine Type Schedule Status
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- 3.Das, S., Konar, A., Chakraborty, U.K.: Two improved differential evolution schemes for faster global search. In: GECCO 2005, pp. 991–998 (2005) Google Scholar
- 5.Guo, Z.X., et al.: A genetic-algorithm-based-optimization model for solving the flexible assembly line balancing problem with work sharing and workstation revisiting. IEEE Transactions on Systems, Manufacturing and Cybernetics. Part C, Applicationsand Reviews 38, 218–228 (2008)CrossRefGoogle Scholar
- 6.Marccarthy, B.L.: Addressing the gap in scheduling research: a review of optimization and heuristic methods in production scheduling. International Journal of Production Research 36, 59–79 (2008)Google Scholar