An improved multi-objective evolutionary algorithm based on decomposition for solving re-entrant hybrid flow shop scheduling problem with batch processing machines

Abstract

Cold-drawn seamless steel pipes play a pivotal role in special industries. However, scheduling plan, as the core of the production, is manually made, which leads to the issue of low production efficiency. The change in processing time or job families when the jobs reenter and the appropriate buffer size of batch processing machines are not considered in the current research. The energy consumption is not considered as an important objective as well. These gaps motivate this study. The production process of cold-drawn seamless steel pipes can be defined as a re-entrant hybrid flow shop scheduling problem with batch processing machines. The model is formulated to minimize the makespan and the energy consumption of the batch processing machines. An improved multi-objective evolutionary algorithm based on decomposition is proposed to address this problem. To make an energy-efficient production scheduling, the buffer size of batch processing machines is discussed and the batching method is developed. Besides, a greedy selection strategy is proposed to choose a machine for cold-drawn operation considering the influence of the long processing time. The results of experiments show that the appropriate buffer size is determined to be the same as the capacity of batch processing machines. The proposed greedy selection strategy can select suitable machines for jobs. The results prove the effectiveness of the batching constraint and the balanced decoding algorithm, and the proposed algorithm can effectively solve the re-entrant hybrid flow shop scheduling problem with batch processing machines.