Sowinski C, Feng Y, Schlichte S, Kedilioglu O, Franke J, Reitelshöfer S (2026)
Publication Type: Journal article
Publication year: 2026
Book Volume: 139
Pages Range: 66-75
DOI: 10.1016/j.procir.2025.10.003
Process simulation tools, such as discrete-event simulation (DES), offer domain experts low-barrier environments for designing and evaluating industrial processes, avoiding the expense and complexity of physical prototypes. While classical optimization approaches are routinely embedded in DES workflows, quantum-enhanced optimization remains largely unexplored. With the emergence of advanced quantum computing hardware, integrating it into industrial optimization has become realistic and the potential to unlock faster, more cost-effective solutions for complex scheduling, routing, and resource allocation compared to classical methods. To broaden its adoption among domain experts without a background in quantum theory, we must offer intuitive, application-oriented software tools. Yet the ecosystem of such frameworks is vast, and guidance is lacking as to which platform or library best suits a given domain expert´s needs. In this work, we bridge the gap between DES and quantum optimization by first conducting a literature review of existing quantum application-oriented optimization frameworks. From these findings, we derive recommendations, answering which tools and platforms engineers or researchers can choose to formulate and test their DES optimization problems across multiple quantum backends. Building on our survey, we introduce a theoretical framework that integrates quantum-optimization algorithms directly into the DES optimization cycle. We then apply this framework to the benchmark sequence-dependent job-scheduling problem with setup times (SDJST), which, to the best of our knowledge, constitutes one of the first quantum-enabled simulation-optimization studies for this problem. We further validate that classical optimization approaches outperform for smaller problem sizes, whereas the quantum approach becomes increasingly promising as the problem size grows.
APA:
Sowinski, C., Feng, Y., Schlichte, S., Kedilioglu, O., Franke, J., & Reitelshöfer, S. (2026). Quantum-enhanced discrete-event simulation and optimization of sequence-dependent job scheduling with setup times. Procedia CIRP, 139, 66-75. https://doi.org/10.1016/j.procir.2025.10.003
MLA:
Sowinski, Christopher, et al. "Quantum-enhanced discrete-event simulation and optimization of sequence-dependent job scheduling with setup times." Procedia CIRP 139 (2026): 66-75.
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