Cooperative Bayesian Optimization Algorithm: A Novel Approach to Simultaneous Multiple Resources Scheduling Problem

During the past several years, there has been a significant amount of research conducted simultaneous multiple resources scheduling problem (SMRSP) Intelligence manufacturing based on meta-heuristics, such as genetic algorithms (GAs), simulated annealing (SA) particle swarm optimization(PSO), has become a common tool to find satisfactory solutions within reasonable computational times in real settings. However, there are few researches considering interdependent relation during the decision activities, moreover for complex and large problems, local constraints and objectives from each managerial entity cannot be effectively represented in a single model for complex and large problems. In this paper, we propose a novel cooperative Bayesian optimization algorithm (COBOA) undertaking divide-and-conquer strategy and co-evolutionary framework. Considerable experiments are conducted and the results confirmed that COBOA outperforms recent researches for the scheduling problem in FMS.