A Co-cooperative Evolutionary Algorithm for Flexible Scheduling Problem under Uncertainty

Flexible Manufacturing System (FMS) has the characteristics of resources non-uniqueness; the operation can be performed by any available machine in a set of machines. Due to reduce the constraints of the machine, it becomes higher flexible. But it has high complexity existing in the actual production system and making its complexity is higher. We experiment three classical evolution algorithms and two modified evolutionary algorithms with grouping mechanism, and do the experiments under the certain environment on different size of data. We found that as the data growing, the evolutionary algorithms with grouping mechanism can get a better solution with larger probability. In this paper, we propose hybrid evolutionary algorithm based on the particle swarm algorithm combining a set-based grouping and parameter adaptive adjustment mechanism. It is given a set number of available groupings, choose a grouping number and calculate adaptive value, if adaptive value becomes better. Through experiments, we conclude that the proposed hybrid evolutionary algorithm based on co-cooperation gets better solution than evolutionary algorithm and then improve robustness of the proposed algorithm.