An Improved NSGA-II for Solving Reentrant Flexible Assembly Job Shop Scheduling Problem

In the wafer manufacturing process of micro electro mechanical systems (MEMS), there are reentrant flow, parallel machines, and assembly operation. Therefore, this study models its scheduling problem as a reentrant flexible assembly job shop scheduling problem. First, a mathematical model is formulated to minimize the total tardiness and the total energy consumption. Second, an improved non-dominated sorting genetic algorithm II (INSGA-II) is proposed to solve this NP-hard problem. An encoding and decoding method are designed according to the problem characteristics. A rule-based initialization strategy is developed to improve the quality of the initialized population. Specific crossover, mutation and selection operators are designed. Finally, numerical experiments are carried out, and the result shows that the proposed algorithm can effectively solve the problem.