Energy-efficient dynamic scheduling for a flexible flow shop using an improved particle swarm optimization

Due to increasing energy requirements and associated environmental impacts, nowadays manufacturing companies are facing the emergent challenges to meet the demand of sustainable manufacturing. Most existing research on reducing energy consumption in production scheduling problems has focused on static scheduling models. However, there exist many unexpected disruptions like new job arrivals and machine breakdown in a real-world production scheduling. In this paper, it is proposed an approach to address the dynamic scheduling problem reducing energy consumption and makespan for a flexible flow shop scheduling. Since the problem is strongly NP-hard, a novel algorithm based on an improved particle swarm optimization is adopted to search for the Pareto optimal solution in dynamic flexible flow shop scheduling problems. Finally, numerical experiments are carried out to evaluate the performance and efficiency of the proposed approach.This research was carried out as a part of the CASES project which is supported by the 7th European Community Framework Programme under the grant agreement No. 294931 and the grant TIN2013-46511-C2-1-P by the Spanish Government. This research was also supported by National Natural Science Foundation of China (No. 51175262), Jiangsu Province Science Foundation for Excellent Youths (No. BK2012032).Tang, D.; Dai, M.; Salido Gregorio, MA.; Giret Boggino, AS. (2016). Energy-efficient dynamic scheduling for a flexible flow shop using an improved particle swarm optimization. Computers in Industry. 81:82-95. doi:10.1016/j.compind.2015.10.001S82958