An enhanced group teaching optimization algorithm for multi-product disassembly line balancing problems

Big data have been widely studied by numerous scholars and enterprises due to its great power in making highly reliable decisions for various complex systems. Remanufacturing systems have recently received much attention, because they play significant roles in end-of-life product recovery, environment protection and resource conservation. Disassembly is treated as a critical step in remanufacturing systems. In practice, it is difficult to know the accurate data of end-of-life products such as disassembly time because of their various usage processes, leading to the great difficulty of making effective and reliable decisions. Thus, it is necessary to model the disassembly process with stochastic programming method where the past collected data are fitted into stochastic distributions of parameters by applying big data technology. Additionally, designing and applying highly efficient intelligent optimization algorithms to handle a variety of complex problems in the disassembly process are urgently needed. To achieve the global optimization of disassembling multiple products simultaneously, this work studies a stochastic multi-product disassembly line balancing problem with maximal disassembly profit while meeting disassembly time requirements. Moreover, a chance-constrained programming model is correspondingly formulated, and then, an enhanced group teaching optimization algorithm incorporating a stochastic simulation method is developed by considering this model’s features. Via performing simulation experiments on real-life cases and comparing it with five popularly known approaches, we verify the excellent performance of the designed method in solving the studied problem.