Balancing Sofa Assembly Workstations: Integration of an Optimization Approach for the Furniture Industry

Abstract

The assembly line balancing problem (ALBP) is an optimization problem which involves assigning tasks to stations to maximize line efficiency. In the context of Industry 4.0, assembly lines must be flexible and adaptable to stochastic and real-world conditions. However, in the literature, line balancing is mostly studied in deterministic environments. As a result, this paper presents a robust optimization approach to bridge the gap between theory and practice by addressing a real-world ALBP in the furniture industry. A mixed integer programming (MIP) model is proposed, considering uncertainty in task execution times, which are expressed as intervals of possible values, and the sequence of module assembly in addition to the standard ALBP constraints. Then, it is linearized by duality to be solved by Gurobi. Two objectives, (1) cycle time minimization and (2) tool efficiency, are optimized lexicographically using the proposed MILP models. Computational experiments and Monte Carlo simulations demonstrate that robust solutions outperform deterministic ones, ensuring stable throughput under variability. The study highlights the trade-off between efficiency and robustness, offering practical insights for furniture manufacturers.