Multi-Station Multi-Robot Welding System Planning and Scheduling Based on STNSGA-D: An Industrial Case Study

Automatic welding system is ideally suited for high-volume production of modern manufacturing. The optimization of workpiece mass manufacturing processes on assembly welding lines is a significant practice in the automotive industry to improve production efficiency. Considering the highly coupled characteristics of multiple sub-problems (including task allocation, robot assignment, welding sequence planning, and dual-function robot scheduling) as well as numerous constraints for workpieces and production line configuration, a new unified optimization model for multi-station multi-robot welding system planning and scheduling (MSMRWS-PS) is established. The objectives are to simultaneously minimize the completion time of single workpiece, the completion time difference between adjacent workpieces and the path length of robot movement. After modeling, a multi-objective evolutionary algorithm based on similarity taboo non-dominated sorting rule and individual population density (STNSGA-D) is proposed. A two-layer encoding method is designed to decompose the coupled problem of welding task allocation and welding sequence planning. In cooperation with the two-layer encoding, an encoding scheme combining dominant coding and hidden coding are devised for robot assignment and dual-function robot scheduling. Finally, STNSGA-D is compared with four state-of-the-art multi-objective evolutionary algorithms (MOEAs) on a set of workpiece test instances. The experimental results show that the comprehensive performance of STNSGA-D is superior to the comparison algorithms. The model and solution proposed in this paper can improve the efficiency of mass production in factories, and has important practical significance for optimizing the high-coupling welding systems.