HRC-RMS in industry 5.0: labour allocation, configuration optimisation, and production planning under uncertain absenteeism

In the context of Industry 5.0, human–robot collaboration (HRC) has emerged as a significant research area. For manufacturing companies, the prevalence of personnel absences due to disasters, viruses, and other factors is a global concern. Moreover, market and energy consumption uncertainties are increasingly prevalent. However, existing HRC research often neglects these uncertainties in the reconfigurable manufacturing systems (RMSs) that are not fully automated. To address these gaps, this study proposes an HRC-RMS framework, explicitly considering uncertain demand, energy consumption, and personnel absenteeism. The problem encompasses labour allocation, configuration optimisation, and production planning. The objective is to minimise the total cost, including reconfiguration, exploitation, energy consumption, inventory, and stockout costs. A two-stage stochastic programming model is introduced, where the first stage includes labour allocation, configuration optimisation, and planned production quantity decisions in all periods, while the second stage captures scenario-dependent recourse decisions through inventory and stockouts. To solve the model, we develop a column generation algorithm (CG) and a CG-GAT algorithm that integrates CG with a multi-head Graph Attention Network. We conduct numerical experiments to compare the performance of state-of-the-art algorithm and the proposed algorithms. Numerical results show that CG-GAT outperforms the others. This work further provides managerial insights.