Scheduling a Stochastic Remanufacturing Process with Disassembly, Reprocessing and Reassembly

Remanufacturing has attracted increasing interest in recent years since it plays important roles in environmental protection and energy-saving. This work presents a scheduling problem from an uncertain remanufacturing process including three subsystems, i.e., disassembly, reprocessing and reassembly ones. Disassembly and reassembly shops contain multiple workstations in parallel to disassemble end-of-life (EOL) products and reassemble the components, respectively. A reprocessing shop is a hybrid flow shop to process the components disassembled from EOL products. A stochastic programming model is established to minimize the expected makespan. In order to solve it efficiently, a learning-based shuffled frog-leaping algorithm is proposed, where a learning mechanism by using obtained searching information is developed to strengthen its exploration and exploitation abilities. Extensive experiments are performed on a set of test problems. The proposed algorithm is compared with a genetic algorithm and simulated annealing algorithm used in some existing studies. The results demonstrate that it is a more promising optimizer to solve the concerned problem than them.