Publications
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Exploiting Machine Learning Techniques to Predict the Stainless Steel Density Produced by Selective Laser Melting Additive Manufacturing
Porosity is one of the inherent defects that results from the Selective Laser Melting (SLM) additive manufacturing technique. The porosity related to fusion-solidification kinetics, results most often from non-optimally or poorly controlled manufacturing parameters. The density, a porosity indicator, affects the mechanical properties of the manufactured material (fatigue strength, cracks, deformations, etc.). Stainless steels are […]
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Application de systèmes multi-agents pour l’optimisation de la chaîne logistique industrielle
Dans le contexte dynamique des chaînes logistiques industrielles, l’optimisation des ressources est cruciale pour répondre aux exigences de plus en plus complexes du marché. La nécessité d’une gestion intégrée des ressources, englobant tant les machines de production que les moyens de transport, a émergé comme un impératif pour améliorer l’efficacité opérationnelle et maintenir la compétitivité. […]
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Energy-efficient flexible flow shop scheduling with renewable energy sources and energy storage systems
In France, the industrial sector is responsible for 18% of the total energy consumption in 2023, nevertheless, only 7% of it comes from renewable energy sources [1]. With the increasing concern over climate change and global warming, industries have started to integrate programs based on demand-side management which includes energy efficiency and demand response to […]
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Integrating Human-Centricity, Sustainability, and Resilience in Digital Twin Models for Industry 5.0: A Multi-Objective Optimization Approach
This paper presents the InduDesc framework, an innovative digital twin model within the CupCarbon software, designed for the advanced needs of Industry 5.0. It integrates human-centred ergonomics, sustainability and resilience into the Flexible Job Shop Scheduling Problem (FJSP), traditionally an NP-hard challenge. By minimising operating times and balancing machine utilisation with ergonomic and sustainability considerations, […]
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A polynomial algorithm for periodic scheduling
We are interested in problems related to periodic scheduling, driven by applications using networks in which terminals send identical flows periodically. The management of such flows must not only minimize latency, but also jitter, and we have shown that classical methods of sizing networks by statistical multiplexing are not suitable for this situation.
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Improving Semantic Mapping with Prior Object Dimensions Extracted from 3D Models
Semantic mapping is a critical challenge that must be addressed to ensure the safe navigation of mobile robots. Equipping robots with semantic information enhances their interactions with humans, as well as their navigation and task planning capabilities. Semantic maps go beyond occupancy information, providing supplementary details about mapped elements that empower robots to gain a […]
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Integration of Pricing and Production Scheduling Decisions: A Mathematical Model
In today’s competitive manufacturing landscape, achieving operational efficiency and optimizing revenue generation are key objectives for make-to-order manufacturers. This paper presents a novel approach for integrating production scheduling and pricing decisions in a make-to-order manufacturing environment. We propose a comprehensive mathematical model that addresses the complex interplay between production scheduling and pricing strategies. By jointly […]
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Evolutionary-Based Ant System Algorithm to Solve the Dynamic Electric Vehicle Routing Problem
This article addresses the Dynamic Electric Vehicle Routing Problem with TimeWindows (DEVRPTW) using a hybrid approach blending genetic and Ant Colony Optimization (ACO) algorithms. It employs an Ant System algorithm (AS) with an integrated memory system that undergoes mutations for solution diversification. Testing on Schneider instances under static and dynamic conditions, with run time of […]
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Elevating Manufacturing Excellence: A Data-Driven Approach to Optimize Overall Equipment Effectiveness (OEE) for a Single Machine
In the intensely competitive landscape of global manufacturing, achieving operational excellence is paramount for survival and success. Central to this endeavor is the optimization of Overall Equipment Effectiveness (OEE), a key metric that directly influences resource efficiency and product quality. Manufacturing companies face numerous challenges, including maintenance inefficiencies, unplanned downtimes, and product quality variations, which […]
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Raw dataset of compression tests on a vegetable oil-based polyurethane foam exposed to different ageing conditions
The current dataset brings raw compression test information of a vegetable-based polyurethane foam (PUF) exposed to different temperatures over different periods of time. Such experimental dataset can provide researchers with important information in the application of numerical and data-driven simulations. Also, it saves money and time once the experimental part is already available. At total, […]
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An Overview of Artificial Intelligence Driven Li-Ion Battery State Estimation
The omnipresence of batteries in nowadays appliances such as portable devices, electrical vehicles, hybrid microgrids, etc. gives them a special priority in energy storage. In addition, the high cost of battery manufacturing makes the maintenance and the monitoring of their states critical. The state estimation is a complicated process that may include many parameters to […]
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Development and Evolution of Hybrid Microgrids in the context of Contemporary Applications
This chapter provides a valuable insight to the development and evolution of hybrid microgrids, their applications, energy demand analysis, energy sources, mathematical modeling, objective parameters, and case studies. It begins with the definition of hybrid microgrids and highlights their importance in various sectors. Onward, it discusses the key microgrid applications. The analysis of energy demand […]
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