Improved MOEA/D with local search for solving multi-stage distributed reentrant hybrid flow shop scheduling problem

Steel tubes are known as “the vessels of industry” and widely used in oil exploration, drilling, aerospace and other fields. This study focuses on cold-drawn seamless steel tubes, aiming to reduce the high logistics cost and improve the low efficiency in production. This study models three stages of heat treatment, cold drawing, and finishing as a multi-stage distributed reentrant hybrid flow shop scheduling problem (MSDRHFSP). First, a multi-objective optimization model is established to minimize makespan and transferring cost. Second, an improved multi-objective evolutionary algorithm based on decomposition with local search (IMOEA/D-LS) is proposed. The balanced decoding method according to the similarity degree of the jobs (BDMASDJ) is developed to trade off the two objectives. Crossover and mutation operators suitable for MSDRHFSP are designed, and a local search operator is performed. Finally, comprehensive experiments are conducted and the results show that IMOEA/D-LS can solve the MSDRHFSP effectively.