Differential Evolution Based Hyper-heuristic for the Flexible Job-Shop Scheduling Problem with Fuzzy Processing Time
In this paper, a differential evolution based hyper-heuristic (DEHH) algorithm is proposed to solve the flexible job-shop scheduling problem with fuzzy processing time (FJSPF). In the DEHH scheme, five simple and effective heuristic rules are designed to construct a set of low-level heuristics, and differential evolution is employed as the high-level strategy to manipulate the low-level heuristics to operate on the solution domain. Additionally, an efficient hybrid machine assignment scheme is proposed to decode a solution to a feasible schedule. The effectiveness of the DEHH is evaluated on two typical benchmark sets and the computational results indicate the superiority of the proposed hyper-heuristic scheme over the state-of-the-art algorithms.
KeywordsDifferential evolution Hyper-heuristic Flexible job-shop scheduling Fuzzy processing time Solution decoding
This research is part of a project supported by the Zhejiang Provincial Natural Science Foundation of China (Grant nos. LQ15F030002 and LY15F020014), the National Natural Science Foundation of China (Grant nos. 61503331, 71671160 and 61603169), the National Undergraduate Training Programs for Innovation and Entrepreneurship (201611482012) and the Zhejiang Key Laboratory of Solid State Drive and Data Security (Grant No. 2015E10003).
- 5.Chen, H.X., Ihlow, J., Lehmann, C.: A genetic algorithm for flexible job-shop scheduling. In: 1999 IEEE International Conference on Robotics and Automation, Detroit, MI, USA, pp. 1120–1125. IEEE (1999)Google Scholar
- 25.Anwar, K., Khader, A.T., Al-Betar, M.A., Awadallah, M.A.: Harmony search-based hyper-heuristic for examination timetabling. In: 2013 IEEE 9th International Colloquium on Signal Processing and its Applications, Kuala Lumpur, Malaysia, pp. 176–181. IEEE (2013)Google Scholar