Abstract
Updating the set of Multiple Dispatch Rules (MDRs) for scheduling of machines in a Flexible Manufacturing System (FMS) is computationally intensive. It becomes a major bottleneck when these rules have to be updated in real-time in response to changes in the manufacturing environment. Machine Learning (ML) based solutions for this problem are considered to be state-of-the-art. However, their accuracy and correctness depend on the availability of high-quality training data. To address the shortcomings of the ML-based approaches, we propose a novel Quadratic Unconstrained Binary Optimization (QUBO) formulation for the MDR scheduling problem. A novel aspect of our formulation is that it can be efficiently solved on a quantum annealer. We solve the proposed formulation on a production quantum annealer from D-Wave and compare the results with single dispatch rule based baseline model.
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Rao, P.U., Sodhi, B. (2022). Scheduling with Multiple Dispatch Rules: A Quantum Computing Approach. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13353. Springer, Cham. https://doi.org/10.1007/978-3-031-08760-8_20
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