@inproceedings{MunozSimulation2025,
abstract = {This article addresses the problem of production scheduling in a single station with unrelated parallel machines, based on a real industrial case. The complexity of the production process is increased by the interrelationship with two additional resources, which requires a high-fidelity modelling approach. Traditional methods often fail in such complex environments, leading in some cases to the integration of classical optimisation techniques with discrete event simulation. In this study, this type of simulation is used to model the real system, incorporating a heuristic that automates the current decision-making process for the factory's production planning. This provides a detailed plan that takes into account all resources and their constraints. Using real production data, the usability of the simulator in this context is validated, demonstrating its effectiveness as a production planning support tool. These results open up significant avenues of research, including reversing the roles of the simulation and optimisation modules, and extending the simulation logic to other stations in the production system.},
address = {Cham},
author = {Mu{\~{n}}oz-D{\'i}az, Mar{\'i}a-Luisa
and La-Banca, Christian
and Escudero-Santana, Alejandro
and Flores-Huam{\'a}n, Kenny-Jes{\'u}s},
booktitle = {Data Science, Challenges and Applications in Industrial Operations},
editor = {Barbadilla-Mart{\'i}n, Elena
and Robles-Velasco, Alicia
and Rodr{\'i}guez-Palero, Mar{\'i}a
and Cort{\'e}s, Pablo},
isbn = {978-3-032-10126-6},
pages = {300--304},
publisher = {Springer Nature Switzerland},
title = {Discrete Event Simulation to Support Production Planning in Real Systems},
year = {2025}
}
Abstract
This article addresses the problem of production scheduling in a single station with unrelated parallel machines, based on a real industrial case. The complexity of the production process is increased by the interrelationship with two additional resources, which requires a high-fidelity modelling approach. Traditional methods often fail in such complex environments, leading in some cases to the integration of classical optimisation techniques with discrete event simulation. In this study, this type of simulation is used to model the real system, incorporating a heuristic that automates the current decision-making process for the factory's production planning. This provides a detailed plan that takes into account all resources and their constraints. Using real production data, the usability of the simulator in this context is validated, demonstrating its effectiveness as a production planning support tool. These results open up significant avenues of research, including reversing the roles of the simulation and optimisation modules, and extending the simulation logic to other stations in the production system.