Process planning can be an effective way to improve the energy efficiency of production processes. Aimed at reducing both energy consumption and processing time (PT), a comprehensive approach that considers feature sequencing, process selection, and physical resources allocation simultaneously is established in this paper. As the number of decision variables increase, process planning becomes a large-scale problem, and it is difficult to be addressed by simply employing a regular meta-heuristic algorithm. A cooperative co-evolutionary algorithm, which hybridizes the artificial bee colony algorithm (ABCA) and Tabu search (TS), is therefore proposed. In addition, in the proposed algorithm, a novel representation method is designed to generate feasible process plans under complex precedence. Compared with some widely used algorithms, the proposed algorithm is proven to have a good performance for handling large-scale process planning in terms of maximizing energy efficiency and production times.
Robust Structural Balance in Signed Networks Using a Multiobjective Evolutionary Algorithm
