Job rotation and human–robot collaboration for enhancing ergonomics in assembly lines by a genetic algorithm

TLDR

Assembly lines employ the majority of the manufacturing workforce, and repetitive tasks and heavy handling overload workers, reducing productivity, while organizational and technological strategies can mitigate these issues. This study proposes a genetic algorithm to solve assembly line balancing by incorporating job rotation and human–robot collaboration to enhance worker ergonomics. The algorithm optimizes both the cost of line implementation—considering worker numbers, skill levels, and collaborative robot equipment—and the variance of workers’ energy load, calculated from movements, physiological traits, job rotations, and robot collaboration. The tool was applied to an industrial assembly case, and its results and implications were discussed.

Abstract

Abstract Currently, the largest percentage of the employed workforce in the manufacturing industry is involved in the assembly process, making ergonomics a key factor when dealing with assembly-related problems. During these processes, repetitive tasks and heavy component handling are frequent for workers, who may result overloaded from an energetic point of view, thus affecting several aspects not only relating to the human factor but also to potentially reduced productivity. Different organizational strategies and technological solutions could be adopted to overcome these drawbacks. For these purposes, the present paper proposes a genetic algorithm for solving the typical problem of assembly line balancing, taking into account job rotation and human–robot collaboration for enhancing ergonomics of workers. The objectives of the problem are related to both economic aspects and human factor: (i) the cost for implementing the assembly line is minimized, evaluated on the basis of the number of workers and differentiated by skill levels and on equipment installed on workstations, including collaborative robots, and (ii) the energy load variance among workers is also minimized, so as to smooth their energy expenditure in performing the assigned assembly operations, calculated according to their movements, physiological characteristics, job rotations and degree of collaboration with robots. The paper finally presents and discusses the application of the developed tool to an industrial assembly case.