| Issue |
E3S Web Conf.
Volume 430, 2023
15th International Conference on Materials Processing and Characterization (ICMPC 2023)
|
|
|---|---|---|
| Article Number | 01300 | |
| Number of page(s) | 12 | |
| DOI | https://doi.org/10.1051/e3sconf/202343001300 | |
| Published online | 06 October 2023 | |
Optimal Production in Vacuum Food Packaging Thermoforming with Linear Programming
1 Universidad Politécnica Salesiana, Production and Industrial Operations, Cuenca 010105, Ecuador
2 Universidad Politécnica Salesiana, IDEIAGEOCA Research Group, Quito 170146, Ecuador
* Corresponding author: dvallejoh@ups.edu.ec
This article presents an optimization model to determine the ideal production distribution in the thermoforming lines of a sausage factory. A linear programming model subject to the simplex method is used to find the production coefficient, determining the ideal workload for three machines involved in the packaging process. The methodology considers common characteristics of the sausage industry, where production programming, thermoforming machines, and their capacities are the ones that directly influence the execution of the experiment. Using the model allows for analyzing the variations of the actual production plan and its effectiveness against the optimized production results. The experiments compared and evaluated the fulfillment of the optimization objective. The modeling was executed with the free software language programming R. A mathematical model was generated that allows evaluating and programming the optimal production in the thermoforming lines to maximize packaging and optimize resources, considering the restrictions of the process. The purpose is to avoid overproduction and misuse of available resources. The results show that the model makes it possible to optimize daily production in the thermoforming lines by 11% for October and 8% for March, equivalent to 5,500 and 3,500 units per day, respectively.
© The Authors, published by EDP Sciences, 2023
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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