To improve the disassembly efficiency of a U-shaped disassembly line and reduce the potentially harmful effects on the environment and human health, we study the multi-product U-shaped disassembly line balancing problem with a fixed number of stations (MUDLBPF). Firstly, we formulate a mathematical model aimed at minimizing cycle time, balancing loads, and reducing hazard indicators. Secondly, a multi-objective variable neighborhood search (MOVNS) algorithm is proposed. A multi-segment encoding method is proposed to maintain the independence of different products. Considering the characteristics of multiple products, a two-stage decoding method is presented. The method includes product assignment and task assignment. To optimize decoding efficiency, a minimum deviation method is put forward to generate feasible solutions. A segmented neighborhood structure containing seven operators is developed to improve the search efficiency. Finally, numerical experiments are performed and the results show that the MOVNS can solve the MUDLBPF effectively and efficiently.

Smart manufacturing in the “Industry 4.0” strategy promotes the deep integration of manufacturing and information technologies, which makes the manufacturing system a ubiquitous environment. However, the real-time scheduling of such a manufacturing system is a challenge faced by many decision makers. To deal with this challenge, this study focuses on the real-time hybrid flow shop scheduling problem (HFSP). First, the characteristic of the hybrid flow shop in a smart manufacturing environment is analyzed, and its scheduling problem is described. Second, a real-time scheduling approach for the HFSP is proposed. The core module is to employ gene expression programming to construct a new and efficient scheduling rule according to the real-time status in the hybrid flow shop. With the scheduling rule, the priorities of the waiting job are calculated, and the job with the highest priority will be scheduled at this decision time point. A group of experiments are performed to prove the performance of the proposed approach. The numerical experiments show that the real-time scheduling approach outperforms other single-scheduling rules and the back-propagation neural network method in optimizing most objectives for different size instances. Therefore, the contribution of this study is the proposal of a real-time scheduling approach, which is an effective approach for real-time hybrid flow shop scheduling in a smart manufacturing environment.