Abstract
In light of the Industry 5.0 trend towards human-centric and resilient industries, human-robot collaboration (HRC) assembly lines can be used to enhance productivity and workers’ well-being, provided that the optimal allocation of tasks and available resources can be determined. This study investigates the assembly line balancing problem (ALBP), considering HRC. This problem, abbreviated ALBP-HRC, arises in advanced manufacturing systems, where humans and collaborative robots share the same workplace and can simultaneously perform tasks in parallel or in collaboration. Driven by the need to solve the more complex assembly line-balancing problems found in the automotive industry, this study aims to address the ALBP-HRC with the cycle time and the number of operators (humans and robots) as the primary and secondary objective, respectively. In addition to the traditional ALBP constraints, the human and robot characteristics, in terms of task times, allowing multiple humans and robots at stations, and their joint/collaborative tasks are formulated into a new mixed-integer linear programming (MILP) model. A neighborhood-search simulated annealing (SA) is proposed with customized solution representation and neighborhood search operators designed to fit into the problem characteristics. Furthermore, the proposed SA features an adaptive neighborhood selection mechanism that enables the SA to utilize its exploration history to dynamically choose appropriate neighborhood operators as the search evolves. The proposed MILP and SA are implemented on real cases taken from the automotive industry where stations are designed for HRC. The computational results over different problems show that the adaptive SA produces promising solutions compared to the MILP and other swarm intelligence algorithms, namely genetic algorithm, particle swarm optimization, and artificial bee colony. The comparisons of human/robot versus HRC settings in the case study indicate significant improvement in the productivity of the assembly line when multiple humans and robots with collaborative tasks are permissible at stations.
Highlights
Industry 4.0 represents a solid ambition for innovation and further technological development (De Nul et al, 2021) and has received great attention from many industries, such as the automotive and electronics industries
With the advent of recent advancements under the Industry 4.0 umbrella, increasing numbers of manufacturers have attempted to align with the emerging technologies developed in this era
Inspired by a real-world case study from the automotive industry, this study addresses the assembly line balancing problem (ALBP) arising in human-robot collabo ration (HRC) environments
Summary
Industry 4.0 represents a solid ambition for innovation and further technological development (De Nul et al, 2021) and has received great attention from many industries, such as the automotive and electronics industries. The authors developed a genetic algorithm (GA) for optimizing the number of workers, ergonomic loads of humans, and the number of equipment, including cobots They assumed that one human and one robot could be assigned to each station and tested the performance of the GA on a real case study. Weckenborg et al, (2020) solved an ALBP-HRC in which the CT was optimized given an NS while considering human and robot character istics (in terms of task times) and collaborative tasks between a maximum of one human and one robot per station. Raatz et al, (2020) addressed task scheduling in an HRC environment, aiming to improve CT as the main measure They proposed a framework based on a GA that considered human and robot features and other factors (including human ergonomic and safety factors) to obtain optimal schedules for an industrial case concerning a gearbox assembly line.
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