Discover scheduling strategies with gene expression programming for dynamic flexible job shop scheduling problem

Authors:
Li Nie;Yuewei Bai;Xiaogang Wang;Kai Liu
Affiliations:
School of Mechanical & Electronic Engineering, Shanghai Second Polytechnic University, Shanghai, People's Republic of China;School of Mechanical & Electronic Engineering, Shanghai Second Polytechnic University, Shanghai, People's Republic of China;School of Mechanical & Electronic Engineering, Shanghai Second Polytechnic University, Shanghai, People's Republic of China;School of Mechanical & Electronic Engineering, Shanghai Second Polytechnic University, Shanghai, People's Republic of China
Venue:
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part II
Year:
2012

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Abstract

In this paper, an intelligent approach based on gene expression programming (GEP) is proposed to discover scheduling strategies for dynamic flexible job shop scheduling problem (DFJSP). In the approach, an indirect encoding and decoding scheme is designed in which the concept of automatically defined functions (ADF) is introduced. In the evaluation of the proposed GEP-based approach, simulation experiments are conducted with respect to the objective of minimizing mean tardiness. The results show that GEP-based approach can automatically find more efficient scheduling strategies for DFJSP under a big range of processing conditions.