Using parallel metaheuristics to solve a parallel U-shaped robotic mixed-model assembly line balancing and sequencing problem

Automation has become a major part of assembly lines because of the rapid technological advancement by industry 4.0. Two major assembly line branches are mixed-model assembly line sequencing and robotic assembly line balancing, which deal with the continuous production of various versions of a product and the use of robots to increase assembly line production rates and efficiency. This study examines the sequencing and balancing of an integrated mixed-model robotic U-shaped assembly line in order to improve assembly line productivity. Regarding the limited energy resources and increased price of energy, minimization of robots’ energy consumption is considered as an objective function. Besides that, makespan and tardiness minimization are considered as two other objective functions. The problem is solved by a parallel multi-objective dragonfly algorithm and non-dominated sorting genetic algorithm II, which utilizes the strengths and covers the weaknesses of the two algorithms. Computational results indicated the superiority of the proposed algorithm against multi-objective dragonfly algorithm, non-dominated sorting genetic algorithm II, and multi-objective particle swarm optimization. An experimental comparison between the parallel U-shaped layout and non-U-shaped parallel and U-shaped non-parallel layouts shows that the proposed layout increases the efficiency of assembly line and reduces robots idle time and energy consumption.