Solving the rotating seru production problem with dynamic multi-objective evolutionary algorithms

Today's volatile market conditions in electronic industries have lead to a new production system, seru (which is the Japanese pronunciation for cell), and has been widely implemented in hundreds of Japanese and other Asia companies. In particular, the rotating seru has been widely implemented, where workers are fully cross-trained with the same skill level but may be different on the proficiency of performing tasks. The rotating seru production problem, which determines the rotating sequence of workers as well as the assembling sequence of jobs, is difficult to solve due to conflicting objectives and dynamic release of customer demands. To solve this problem, we propose a dynamic multi-objective NSGA-II based memetic algorithm. Moreover, to preserve desirable population diversity and improve the searching efficiency, we propose different problem-specific evolutionary strategies. Finally, we test the performance of our proposed memetic algorithm with other state-of-the-art multi-objective evolutionary algorithms and demonstrate the effectiveness of our proposed algorithm.