Differential Evolution Based Hyper-heuristic for the Flexible Job-Shop Scheduling Problem with Fuzzy Processing Time

Lin, J, Luo, D, Li, X, Gao, K and Liu, Y 2017, 'Differential Evolution Based Hyper-heuristic for the Flexible Job-Shop Scheduling Problem with Fuzzy Processing Time', in Proceedings of the 11th International Conference on Simulated Evolution and Learning (SEAL'17), Shenzhen, China, 10 - 13 November 2017, pp. 75-86.


Document type: Conference Paper
Collection: Conference Papers

Title Differential Evolution Based Hyper-heuristic for the Flexible Job-Shop Scheduling Problem with Fuzzy Processing Time
Author(s) Lin, J
Luo, D
Li, X
Gao, K
Liu, Y
Year 2017
Conference name SEAL'17
Conference location Shenzhen, China
Conference dates 10 - 13 November 2017
Proceedings title Proceedings of the 11th International Conference on Simulated Evolution and Learning (SEAL'17)
Publisher Springer
Place of publication Germany
Start page 75
End page 86
Total pages 12
Abstract 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.
Subjects Optimisation
Neural, Evolutionary and Fuzzy Computation
Keyword(s) Differential evolution
Hyper-heuristic
Flexible job-shop scheduling
Fuzzy processing time
Solution decoding
Copyright notice © Springer International Publishing AG 2017
ISBN 9783319687599
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