Abstract
The Production Scheduling is an important phase in a manufacturing system, where the aim is to improve the productivity of one or more factories. Finding an optimal solution to scheduling problems means to approach complex combinatorial optimization problems, and not all of them are solvable in a mathematical way, in fact a lot of them are part of the class of NP-hard combinatorial problems. In this paper a joint mixed approach based on a joint use of Evolutionary Algorithms and their quantum version is proposed. The context is ideally located inside two factories, partners and use cases of the white’R FP7 FOF MNP Project, with high manual activity for the production of optoelectronics products, switching with the use of the new robotic (re)configurable island, the white’R, to highly automated production. This is the first paper approaching the problem of the dynamic production scheduling for these types of production systems proposing a cooperative solving method. Results show this mixed method provide better answers and is faster in convergence than others.
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Fiasché, M., Liberati, D.E., Gualandi, S., Taisch, M. (2018). Quantum-Inspired Evolutionary Multiobjective Optimization for a Dynamic Production Scheduling Approach. In: Esposito, A., Faudez-Zanuy, M., Morabito, F., Pasero, E. (eds) Multidisciplinary Approaches to Neural Computing. Smart Innovation, Systems and Technologies, vol 69. Springer, Cham. https://doi.org/10.1007/978-3-319-56904-8_19
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