mathematics Article Cooperative Co-Evolution Algorithm with an MRF-Based Decomposition Strategy for Stochastic Flexible Job Shop Scheduling Lu Sun 1 , Lin Lin 2,3,4,*, Haojie Li 2,4 and Mitsuo Gen 3,5 1 School of Software, Dalian University of Technology, Dalian 116620, China;
[email protected] 2 DUT-RU Inter. School of Information Science & Engineering, Dalian University of Technology, Dalian 116620, China;
[email protected] 3 Fuzzy Logic Systems Institute, Fukuoka 820-0067, Japan; gen@flsi.or.jp 4 Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian University of Technology, Dalian 116620, China 5 Department of Management Engineering, Tokyo University of Science, Tokyo 163-8001, Japan * Correspondence:
[email protected] Received: 30 January 2019; Accepted: 22 March 2019; Published: 28 March 2019 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.