IMOEA/D to optimize job release problem for a reentrant hybrid flow shop

In a reentrant production system, an effective job release policy is helpful to increase output and keep the work in process (WIP) in an acceptable level. This study mainly focuses on the job release problem in a reentrant hybrid flow shop (RHFS-JRP). First, to increase output and reduce jobs' waiting time, a mathematical model is formulated to minimize the difference between the actual output and the target output and the total waiting time of jobs jointly. Second, an improved multi-objective evolutionary algorithm based on decomposition (IMOEA/D) is proposed to solve the RHFS-JRP. Third, to generate job release plans effectively, a dual chromosome encoding method indicating release quantities and release intervals is proposed. Fourth, the WIP control mechanism based decoding algorithm is proposed to reduce the total waiting time of jobs without affecting output. Fifth, to avoid falling into the local optimum and ensure the diversity of the population, an adaptive neighborhood updating strategy is proposed. Finally, numerical experiments are performed and the results show that the IMOEA/D can solve the RHFS-JRP effectively.