Exploration of Human–Robot Collaborative Two-Sided Assembly Line Optimization Based on Workload Balancing

Nian Lu; Xuan Zhu

doi:10.62051/ijgem.v10n3.15

Authors

Nian Lu
Xuan Zhu

DOI:

https://doi.org/10.62051/ijgem.v10n3.15

Keywords:

Human–robot collaboration, Workload, Assembly line balancing, Multi-objective optimization, Migrating birds optimization algorithm

Abstract

In the context of Industry 4.0, collaborative robots, as a key enabler of intelligent manufacturing, are increasingly being introduced into assembly environments, forming a novel human–robot collaborative paradigm with workers. This study focuses on the optimal allocation of human–robot collaborative assembly tasks in two-sided assembly line scenarios. A systematic investigation is conducted on assembly line balancing modeling methods incorporating both physical and cognitive workload evaluation mechanisms. On this basis, a multi-objective optimization model is developed with production cycle time, cost, working time, and workload balance as the primary objectives. Furthermore, the conventional Migrating Birds Optimization (MBO) algorithm is extended to a multi-objective framework and enhanced with adaptive improvement strategies to improve solution effectiveness and distribution. The results provide theoretical support and methodological insights for developing human-centric and efficiency-oriented collaborative production systems.

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