| Issue |
E3S Web Conf.
Volume 601, 2025
The 3rd International Conference on Energy and Green Computing (ICEGC’2024)
|
|
|---|---|---|
| Article Number | 00026 | |
| Number of page(s) | 10 | |
| DOI | https://doi.org/10.1051/e3sconf/202560100026 | |
| Published online | 16 January 2025 | |
Modelling and optimization of a machine production process: Buffer stock supply implementation and replenishment control
1 Laboratory of Electrical and industrial, Engineering, Information, Processing and Logistic. Faculty of sciene Ain Chok. Casablanca, Morocco
2 Laboratory of Electrical and industrial, Engineering, Information, Processing and Logistic. Faculty of science Ain Chok. Casablanca, Morocco
3 Laboratory of Electrical and industrial, Engineering, Information, Processing and Logistic. Faculty of sciene Ain Chok. Casablanca, Morocco
4 Laboratory of Electrical and industrial, Engineering, Information, Processing and Logistic. Faculty of science Ain Chok. Casablanca, Morocco
* Corresponding author: Saad.elbaraka@gmail.com
In the industry and logistic the production process is the sequence that aims to convert raw materials into finished or semi-finished products. It is identified as one of the most complex phases of the supply chain due to its close interdependence with other processes such as replenishment. In this perspective, the efficiency of the manufacturing process essentially relies on the decisions of the responsible operators for supplying machines with raw materials. These decisions do not ensure optimal operation efficiency. This document proposes a scientific study based on the modelling and control of the production process in a smart industry perspective. Initially, one will present a modelling of the manufacturing system conceptualized as an industrial machine, as well as the replenishment policy, that will be conceived as a control loop tuning the raw material supplies quantities into the production machine following a proposed architecture.
We will then proceed to simulate this scenario using the Simulink interface to identify the most influential parameters and improve them.
Key words: Smart manufacturing system / Production machine / Replenishment / smart control / Simulation / Stock Regulation
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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