Managing uncertainty in production sequencing: a digital twin framework for mixed-model assembly lines

In response to dynamic and uncertain contexts, companies adopt mixed-model lines over single lines to ensure flexibility and meet diverse, low-volume production needs. Consequently, sequencing plays a central role in multi-objective daily production optimization. To identify optimal solutions, the multi-objective sequencing problem needs metaheuristics, especially evolutionary approaches, while digital twins manage uncertainty in dynamic production contexts. This work proposes a digital twin framework combining evolutionary algorithms and simulation to optimize sequencing for mixed-model lines under stochastic conditions. Validation through simulation confirms its feasibility and effectiveness. A case study in a leather goods company validates the framework’s applicability in the fashion industry, showcasing managerial benefits for decision-makers. Additionally, its iterative, data-driven implementation ensures easy updates, crucial for High Variety/Low Volume environments. Academically, the study innovates by combining evolutionary algorithms and simulation, demonstrating their synergy in real-world optimization and providing a replicable approach for companies operating in highly variable production scenarios.