Multi-Worker Assembly Line Rebalancing with Relevance-Guided Configuration Preservation
For production managers needing to rebalance assembly lines efficiently, this work offers a method to preserve important task assignments while minimizing computational cost.
The paper tackles assembly line rebalancing under changing conditions, proposing a pruned Mean Similarity Factor that focuses on structurally relevant tasks to preserve configuration. The approach, integrated into a MILP formulation, achieves optimal rebalancing solutions with reduced computational effort while maintaining high task colocation and balanced workload/ergonomic distributions.
In assembly line balancing, tasks are assigned to stations in order to satisfy a required cycle time. When production conditions change, the line must be rebalanced by modifying the current task allocation, typically aiming to move as few tasks as possible between stations. Similarity measures are commonly used to control such changes, but they generally evaluate configuration preservation by treating all tasks equally, which may not reflect their different practical importance. In this work, a \emph{pruned Mean Similarity Factor} is proposed for assembly line rebalancing, evaluating similarity only over a subset of structurally relevant tasks identified through a relevance score. The proposed measure is integrated into a compact mixed-integer linear programming (MILP) formulation that considers practical aspects of manual assembly, specifically workload balance, ergonomic exposure, multi-worker stations, and positional constraints. Computational experiments on extended benchmark instances derived from the literature show that the proposed approach can obtain optimal rebalancing solutions within reasonable computational times, while maintaining high task colocation and balanced workload and ergonomic distributions. In particular, focusing the similarity evaluation on relevant tasks helps reduce the computational effort.