Martina Vinetti

Martina Vinetti, Sabino Francesco Roselli, Martin Fabian

This work addresses the Assembly Line Rebalancing Problem driven by cycle-time changes in manual assembly systems where multiple workers operate in parallel within the same station. A multi-objective optimization model is proposed that incorporates task reassignment, worker allocation, ergonomic evaluation, and explicit spatial feasibility through work-area constraints. The formulation minimizes deviations from the current configuration while promoting balanced workload and ergonomic conditions among workers. The main contribution is the extension of assembly line rebalancing to multi-worker settings with explicit spatial constraints. Computational experiments on synthetic instances demonstrate that the model consistently generates feasible reconfigurations, highlighting its potential as a decision-support tool for industrial rebalancing in flexible production environments.

Martina Vinetti, Sabino Roselli, Martin Fabian

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.