Abstract
Flexible job shop scheduling problem (FJSP) is an extended formulation of the classical job shop scheduling problem, endowing great significance in the modern manufacturing system. The FJSP defines an operation that can be processed by any machine from a given set, which is a strong constrained NP-hard problem and intractable to be solved. In this paper, three recent proposed meta-heuristic optimization algorithms have been employed in solving the FJSP aiming to minimize the makespan, including moth-flame optimization (MFO), teaching-learning-based optimization (TLBO) and Rao-2 algorithm. Two featured FJSP cases are carried out and compared to evaluate the effectiveness and efficiency of the three algorithms, also associated with other classical algorithm counterparts. Numerical studies results demonstrate that the three algorithms can achieve significant improvement for solving FJSP, and MFO method appears to be the most competitive solver for the given cases.
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Acknowledgment
This research work is supported by the National Key Research and Development Project under Grant 2018YFB1700500, and Science and Technology Project of Shenzhen (JSGG20170823140127645).
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Yang, D., Zhou, X., Yang, Z., Zhang, Y. (2020). Recent Bio-inspired Algorithms for Solving Flexible Job Shop Scheduling Problem: A Comparative Study. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1159. Springer, Singapore. https://doi.org/10.1007/978-981-15-3425-6_31
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DOI: https://doi.org/10.1007/978-981-15-3425-6_31
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