Automatic production planning using hybrid semi-supervised learning and optimization algorithms
Year: 2025
Type: article
Abstract: The Industrial Hub of Manaus faces a challenging reality, marked by recurring logistical issues and supply shortages especially during the dry season. In this context, ensuring the continuity of production operations requires more than just planning; it demands intelligence and adaptability. This article presents the development of a recommendation system that leverages artificial intelligence and machine learning to swiftly suggest alternative production models when materials are unavailable. The solution combines various techniques, including similarity-based clustering (k-means and PCA), collaborative filtering, and linear programming. It was tested using real data from a local manufacturing plant in a simulated environment. The results were highly positive: there was a reduction in material shortages, an increase in continuous production time, and improved adherence to production plans. More than just numbers, this study represents a concrete advancement in dealing with uncertainties and harnessing technology for faster, safer, and well-informed decision-making. The proposed solution not only addresses current challenges but also points toward a more flexible, intelligent, and responsive approach to modern industrial planning.
Authors: Jardson Rainerio da Silveira Rodrigues, Luís Ricardo Arantes Filho, Luiz Osmar Cunha Augusto Ruiz, Jaqueline Oliveira da Silva, Valcinei Xavier da Silva, Honorato Rocha Costa Júnior, Nelson Alexandre Gouvea, Carla Oran Fonseca de Souza
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4413675850