A mixed-flow human–machine collaborative disassembly line balancing problem is introduced, considering the various recycling methods for waste products and the relationship between the attributes of each product part and the corresponding disassembly operator. The problem aims to optimize the number of workstations, balance the idle time, and minimize the disassembly cost. To address this, an Improved Artificial Fish Swarming Algorithm (IAFSA) was designed based on the combination of the problem characteristics, and the IAFSA algorithm was applied to a mixed-flow television (TV) disassembly example and compared with two different algorithms. The solution shows that the proposed algorithm optimizes the proposed algorithm by 14.3%, 52.3%, and 9.8%, respectively, on the three objectives. Finally, the performance of the three algorithms is compared using Non-dominant rate (NR) and Generation distance (GD) metrics.

This paper addresses the multi-operator human–robot collaborative disassembly line balancing problem aimed at minimizing the number of workstations, workstation idle time, and disassembly costs, considering the diversity of end-of-life products and the characteristics of their components. A hybrid artificial fish swarm algorithm (HAFSA) is designed in accordance with the problem characteristics and applied to a disassembly case of a hybrid refrigerator. Comparative experiments with the non-dominated sorting genetic algorithm II (NSGA-II) and teaching–learning-based optimization (TLBO) algorithms demonstrate the superiority of the proposed algorithm. Finally, the performance of the three algorithms is evaluated based on non-dominated rate (NR), generational distance (GD), and inverted generational distance (IGD) metrics.

This paper mainly explains the alignment design of the secondary highway in Huaning County, which is a route with a total length of 3075.05 m and a design speed of 60 km/h starting from Chaoyangan and ending at Lijiadun. There are three plane curves with radii of 600 m, 250 m and 200 m, respectively, and five variable slope points, with the former longitudinal slope of each point being −2.05%, 2.27%, −3.81% and −1.32%, respectively. The design process mainly includes road alignment selection, plan design and longitudinal section design. This design is mainly through the latitudinal road system to the road alignment design.