Artificial Intelligence and Optimization Models for Agile and Resilient Production Planning and Control
[EN] The increasing uncertainty in the manufacturing industry, driven by disruptive events and market fluctuations, highlights the need for more agile and resilient Production Planning and Control (PPC) strategies. This research focuses on addressing the Capacitated Lot Sizing and Scheduling (CLSS)...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:dnet:riunet______::3dbfc1d64f2b79c768aa373c480f8838 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/235258 |
| Access Level: | acceso embargado |
| Palabra clave: | Production planning and control Capacitated lot sizing Dynamic scheduling Mathematical programming Artificial intelligence Smart manufacturing |
| Sumario: | [EN] The increasing uncertainty in the manufacturing industry, driven by disruptive events and market fluctuations, highlights the need for more agile and resilient Production Planning and Control (PPC) strategies. This research focuses on addressing the Capacitated Lot Sizing and Scheduling (CLSS) for medium-term planning and Dynamic Scheduling (DS) for short-term decision-making by integrating Mathematical Programming, Artificial Intelligence (AI), and Smart Manufacturing (SM) technologies. A systematic review of CLSS and DS models identifies gaps in modeling uncertainty and adapting to real-time disruptions. A conceptual framework is proposed to standardize CLSS characteristics, while a matheuristic approach combines metaheuristics and mathematical programming to optimize sustainability-driven lot sizing decisions. Additionally, an AI-based tool dynamically reschedules production in response to real-time factory data. The research further develops a scalable architecture for industrial implementation, leveraging IoT and AI for enhanced decision-making. Validation is conducted through real and synthetic case studies, assessing solution quality and computational efficiency. This work aims to bridge the gap between traditional optimization techniques and smart, data-driven SM environments. |
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