Heuristics for solving a multi-model robotic assembly line balancing problem

Topic of balancing assembly lines is of great interest for researchers and industry practitioners due to the significant impact it has on increasing productivity and efficiency of manufacturing systems. Robots are widely applied in manufacturing industries for assembly processes. Wide literature has been reported on balancing of robotic assembly lines with single and mixed models. Researchers have extensively used heuristics and metaheuristics to solve these problems due to their NP-hard nature. However, no work has been reported on how to balance a robotic assembly line with multiple models (MuRALB) with batch production. This problem is highly relevant for large-scale assembly of products found, e.g. the automotive industry. To authors’ knowledge, this is the first attempt to solve this problem. This research proposes a novel heuristic to solve type II MuRALB problem. Type II problem deals with minimizing the cycle time for a fixed set of robots. Heuristic is implemented, and method for scheduling batched production with related setup times for a robotic assembly line is presented, and based on the analysis conducted, advantage of batching is presented. Proposed heuristic is tested on a set of new five datasets, and performance of this heuristic and batching is presented in detail.