Home // SIMUL 2024, The Sixteenth International Conference on Advances in System Modeling and Simulation // View article

Predictive AI To Feed Simulation

Authors:
Carlo Simon
Stefan Haag
Natan Georgievic Badurasvili

Keywords: Predictive Artificial Intelligence; Neural Networks; Machine Learning; Simulation; Petri Nets

Abstract:
In an industry project, the authors had successfully modelled and simulated the inbound and outbound traffic of a warehouse with the aid of high-level Petri nets. But instead of taking this simulation tool to improve the future planning, the practitioners confronted the authors with another problem: the reasons for the incorrect planning is less the planning process but the inability to foresee which of the scheduled trucks will be late and sabotage the time plan. As a solution to this problem, the authors considered methods of predictive artificial intelligence and especially machine learning. The idea is to take past schedules to train a neural network in order to forecast deviations of upcoming schedules. The paper answers the question on how to extend the previous simulation model by a suitable forecast component which is now ready to be tested with real-world data. The paper explains the scenario of the real-world warehouse, the simulation of its traffic and the information needed for this. Afterwards, the necessary extensions of the data set are explained and how to set up a machine learning component to predict future deviations of schedules. The adapted schedules can then be simulated to create alternative schedules. This is a next step of the authors' research to conduct their simulation on a set of future scenarios in order to chose the schedule that performs not worse than the initial schedule on the original data, but performs better under the alternative scenarios.

Pages: 58 to 63

Copyright: Copyright (c) IARIA, 2024

Publication date: September 29, 2024

Published in: conference

ISSN: 2308-4537

ISBN: 978-1-68558-197-8

Location: Venice, Italy

Dates: from September 29, 2024 to October 3, 2024