Cooperative Co-Evolution Algorithm with an MRF-Based Decomposition Strategy for Stochastic Flexible Job Shop Scheduling

Lu Sun, Lin Lin, Haojie Li and Mitsuo Gen
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Lu Sun: School of Software, Dalian University of Technology, Dalian 116620, China
Lin Lin: DUT-RU Inter. School of Information Science & Engineering, Dalian University of Technology, Dalian 116620, China
Haojie Li: DUT-RU Inter. School of Information Science & Engineering, Dalian University of Technology, Dalian 116620, China
Mitsuo Gen: Fuzzy Logic Systems Institute, 820-0067 Fukuoka, Japan

Mathematics, 2019, vol. 7, issue 4, 1-20

Abstract: Flexible job shop scheduling is an important issue in the integration of research area and real-world applications. The traditional flexible scheduling problem always assumes that the processing time of each operation is fixed value and given in advance. However, the stochastic factors in the real-world applications cannot be ignored, especially for the processing times. We proposed a hybrid cooperative co-evolution algorithm with a Markov random field (MRF)-based decomposition strategy (hCEA-MRF) for solving the stochastic flexible scheduling problem with the objective to minimize the expectation and variance of makespan. First, an improved cooperative co-evolution algorithm which is good at preserving of evolutionary information is adopted in hCEA-MRF. Second, a MRF-based decomposition strategy is designed for decomposing all decision variables based on the learned network structure and the parameters of MRF. Then, a self-adaptive parameter strategy is adopted to overcome the status where the parameters cannot be accurately estimated when facing the stochastic factors. Finally, numerical experiments demonstrate the effectiveness and efficiency of the proposed algorithm and show the superiority compared with the state-of-the-art from the literature.

Keywords: MRF-based decomposition strategy; stochastic scheduling; flexible job shop scheduling; cooperative co-evolution algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2019
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