Publications
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Energy-Aware Optimization of Multi-Robot Systems with Task Allocation and Partial Recharge Scheduling
This paper presents a task allocation and scheduling model for multi-robot systems operating under energy constraints. The proposed model integrates key factors such as energy consumption, battery charging management, and task execution efficiency. To address this problem, we employ both an exact solver-based method and a bio-inspired algorithm, enabling a comparative analysis of their performance […]
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A New MILP Model for Supplier Selection: Improving Efficiency and Solution Quality under Risk
This paper contributes to the field of supplier selection under risk by proposing a Mixed Integer Linear Programming (MILP) model that integrates key decisionmaking factors such as cost and risk mitigation strategies. The proposed model offers a comprehensive approach for selecting the best supplier portfolio under uncertainty. Furthermore, the paper provides a detailed comparison of […]
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Pricing-Driven Optimization of Lot-Sizing and Scheduling in Hybrid Manufacturing-Remanufacturing Systems
The transition towards sustainable production models has intensified interest in hybrid manufacturing and remanufacturing systems, which play a crucial role in the circular economy. These systems integrate new production with the refurbishment of used products, presenting unique operational challenges. This paper examines the integration of pricing, scheduling, and lot-sizing in modern industrial operations, with a […]
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Digital Twin–Driven Multi-Objective Layout Optimisation of Flexible Robotic Cells with NSGA-II
Robotic cells in modern factories must be laid out for efficiency, flexibility, and fast adaptation to shifting product mixes [1]. Cycle time and safety hinge on where workstations, robot bases, stock bins, and tools sit, yet finding a balanced layout is challenging because static choices alter dynamic behaviour: moving a fixture a few centimetres can […]
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JIT Organization Method for Sustainable and Integrated Production and Delivery Scheduling
This paper integrates sustainable production and delivery scheduling using a Just In Time approach to minimize delays and CO2 emissions in a job shop environment with hybrid vehicle fleets for the delivery. Beyond existing integrated production and delivery models, this work explicitly incorporates electric vehicle constraints, addressing energy consumption and charging requirements alongside scheduling decisions. […]
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Enhancing Industrial Process Optimization through Digital Twins
This paper explores the role of Digital Twins (DTs) in optimizing industrial processes, particularly in strategic sectors such as automotive, aerospace, and water treatment. With the growing need for companies to adapt to increased competition and technological advancements, DTs offer an innovative approach to process management. The study outlines the integration of real-time data analytics, […]
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Contribution à la caractérisation de l’affordance d’un environnement de travail industriel : une approche basée sur l’apprentissage profond combinant données réelles et synthétiques
Ce travail s’inscrit dans le cadre du projet “École De La Batterie”, dont l’un des objectifs concerne l’optimisation de la conception des postes de travail manuel dans le but d’améliorer leur ergonomie. Nos travaux s’inscrivent dans cette démarche et visent à caractériser l’affordance des éléments de ces environnements avec lesquels les opérateurs interagissent (outils, composants, […]
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Optimizing building envelope design across various French climates: A multi-objective approach using NSGA II and RMP method
Architects face a major challenge in designing buildings that enhance human comfort while minimizing energy consumption. To address this, the present work presents a novel multi-objective optimization approach, aiming to determine the optimal building envelope design. The developed approach focuses on minimizing energy consumption for both heating and cooling demand. Therefore, the methodology follows three […]
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Metaheuristic and Reinforcement Learning Techniques for Solving the Vehicle Routing Problem: A Literature Review
The Vehicle Routing Problem remains a pivotal challenge in combinatorial optimization, where the objective is to determine optimal routes for a fleet of vehicles serving geographically distributed customers under specific constraints. Over decades, a diverse spectrum of solution methodologies—spanning exact algorithms, heuristics, metaheuristics, and, more recently, machine learning—has emerged. This review critically examines the intersection […]
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An innovative Machine Learning model for predicting compressive strength of biobased concretes
Biobased concretes, which incorporate renewable and environmentally friendly components such as plant-based aggregates, offer a promising alternative to conventional materials. However, their widespread adoption is hindered by several challenges such as variability in raw materials, complex interactions between components, the lack of standardized methodologies, and requirement of advanced technics for characterizing and optimizing their mechanical […]
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Lightweight Deep Learning for Photovoltaic Energy Prediction: Optimizing Decarbonization in Winter Houses
This paper proposes an innovative hybrid multivariate deep learning approach to predict photovoltaic (PV) energy production in winter houses, with a focus on lightweight models with low environmental impact. A methodology is developed to assess the carbon footprint of these models, considering training energy consumption, operational CO2 emissions, and energy savings from PV production optimization. […]
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Deterministic Scheduling of Periodic Messages for Low Latency in Cloud RAN
Cloud-RAN (C-RAN) is a cellular network architecture where pro- cessing units, previously attached to antennas, are centralized in data centers. The main challenge in meeting protocol time con- straints is minimizing the latency of periodic messages exchanged between antennas and processing units. We demonstrate that sta- tistical multiplexing introduces significant logical latency due to buffering […]
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