Robotic assembly line balancing considering the carbon footprint objective with cross-station design

Abstract

Robotic assembly lines are widely applied in the manufacturing sector to produce a wide range of products because of their efficiency and multifunctionality. The robotic assembly line balancing problem (RALBP) is a combinatorial optimization problem where the decision variables are task assignment and robot allocation. However, RALBP considering carbon footprint, which is a very significant environmental concern, has scarcely been studied in the literature and a practical “cross-station” design is never mathematically formulated. In this paper, a mixed-integer programming model is proposed to optimize the two objectives according to the Pareto principle. A particle swarm algorithm (PSO) with some improvement rules is designed to solve the problem. To examine the efficiency of the algorithm, computational experiments including five medium-sized and five large-sized datasets are conducted. The results show that the efficiency of PSO is better than that of four other classic algorithms in terms of three evaluation metrics. Further, the production manager and assembly line designer can choose the appropriate production plan and upgrade the line configuration.