Trade-offs in Robotic Assembly Line Balancing: Investigating Cycle Time, Energy Consumption, and Efficiency through Mathematical Optimization

Improving robotic assembly line balancing (RALBP) is key to increasing productivity, reducing cycle time (CT), and lowering energy consumption (EN). This study highlights the importance of optimizing robot deployment and workstation configuration to balance both objectives. A dual-objective model using a weighted sum approach evaluates different CT-to-EN priority ratios. Experiments across three problem sizes analyze the impact of station count and robot selection on CT, EN, and line efficiency. Results show that prioritizing CT increases energy use, while energy-focused configurations extend CT but enhance sustainability. ANOVA showed that robots count has a strong impact on efficiency in terms of WEST ratio, while adding more stations does not necessarily improve efficiency. The interaction effect (Robots × Stations) is insignificant as the two factors work independently rather than together. This research also introduces a benchmark dataset to support future studies in sustainable manufacturing.