ESHT - DSIM - Comunicações em eventos científicos
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Browsing ESHT - DSIM - Comunicações em eventos científicos by Sustainable Development Goals (SDG) "09:Indústria, Inovação e Infraestruturas"
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- A hybrid strategy for oven optimization in aerospace manufacturing: lean principles and mathematical modellingPublication . Pereira, M. Teresa; Pereira, Marisa G.; Ferreira, Fernanda A.; Silva, Francisco G.; Guimarães, AndréAdopting the Lean philosophy is essential for reducing costs and increasing productivity in industrial environments, where equipment efficiency plays a pivotal role. This study focuses on optimizing the use of an oven, a critical equipment, in the aircraft parts manufacturing process, in a company producing composite components for aerospace applications. A mathematical model based on the classic two-dimensional knapsack problem was developed to address inefficiencies in space utilization and workflow inconsistencies. The model was implemented to determine the optimal parts’ allocation, maximizing oven occupancy while ensuring efficient workflows. The study introduces a hybrid strategy that combines a structured allocation system with vertical shelving units to streamline part storage and improve the kiln-loading process. Extensive experiments validated the model’s performance, demonstrating its ability to support Lean principles by enhancing productivity and reducing costs in the aeronautical industry. This work underscores the importance of mathematical modelling for optimizing manufacturing processes and meeting the increasing demands of modern production environments.
- Optimization of metal sheet cutting processes using integer linear programming: reducing waste and enhancing production efficiencyPublication . Pereira, Marisa G.; Pereira, M. Teresa; Fernandes, Miguel A.; Silva, Francisco G.; Guimarães, André; Ferreira, Fernanda A.This paper presents an optimization approach for metal sheet cutting processes using Integer Linear Programming (ILP). It addresses the critical challenges of material waste and inefficiencies inherent in traditional manual cutting methods. The primary objective was to develop and implement an ILP-based model to automate and optimize cutting plans, thereby minimizing material waste and enhancing production efficiency. The proposed model extends existing optimization frameworks by incorporating constraints and decision variables tailored to the Two-Dimensional Strip Packing Problem (2D-SPP). Implemented in Python with libraries such as PuLP for mathematical modeling and Matplotlib for result visualization, the model was validated using real-world datasets. Results demonstrated substantial improvements, achieving material utilization rates of up to 87.94%. These findings underscore the effectiveness of ILP in addressing complex industrial challenges, offering a systematic approach to waste reduction and workflow optimization. The paper concludes by evaluating the model’s practical implications and its potential scalability for broader industrial applications.