Publications
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Two-stage stochastic program for disassembly lot-sizing under random ordering lead time
This study addresses disassembly lot-sizing that determines the ordering and disassembly schedules of end-of-life (EOL) products and subassemblies to satisfy items demands. A stochastic version with uncertain ordering lead time (OLT) is considered for the first time. Here, OLT represents the time elapsed between placing an order and receiving it (only an EOL product can […]
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Sim-optimization hybrid approach for scheduling randomly deteriorating treatment tasks in horticulture
In this paper, we study the problem of scheduling robotized tasks in the context of Agriculture 4.0. The objective is to optimize the treatment tasks of plants against an evolving disease (mildew) within a greenhouse. The treatment is performed using a type-C ultraviolet radiation (UV-C) by a UV-Robot. We propose a semi-dynamic simulation-optimization approach based […]
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Modeling and simulation of human behavior impact on production throughput
The development of new technologies generates intelligent, complex, and collaborative production systems. Several research works want to improve the production performances while improving the comfort of the human operator. However, it is not obvious to define optimal strategies of operations planning and control that consider the unexpected and variable character of human operators. It is […]
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Ikigai assessment in a western work context
The measurement of well-being is widely studied in the literature but may still benefit from a multicultural viewpoint. We present a new approach by measuring the ikigai of individuals. Ikigai is part of the Japanese philosophy of life purpose and well-being. Japanese research offers the Ikigai-9 scale (Imai, 2012) based on a three-dimensional model measuring: […]
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Deep transferable learning on heartbeat classification for imbalance dataset
Electrocardiogram (ECG) data recorded by medical devices are hard to analyze manually. Therefore, it is important to analyze and categorize each heartbeat using machine learning. Recently, advancements in machine learning have made classification of complex data easy and fast. However, these machine learning algorithms require sufficient amount of training data and have limited performance in […]
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Conception universelle, lead user et intelligence émotionnelle
L’objet de cette conférence était de présenter les résultats de notre étude portant sur l’identification des caractéristiques des lead users, à soir l’empathie et la compétence dans le domaine.
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Impliquer les managers dans le développement des compétences de leurs équipes avec l’intégration de dynamiques apprenantes: construire des environnements capacitants
Comprendre comment l’idée d’environnement capacitant revisite les modalités de management
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Buckling of Timoshenko beam under two-parameter elastic foundations
This paper exposes buckling solutions of a plane, quasi-static Timoshenko beam with small transformation subjected to a longitudinal force and surrounded by an elastic wall modeled by two-parameter elastic foundations. A non-dimensional analysis of associated Haringx and Engesser model is performed and buckling stress and shape are exposed analytically. Relations for rigidity of the wall […]
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Community-based method for extracting backbones
Networks are an adequate representation for modeling and analyzing a great variety of complex systems. However, understanding networks with millions of nodes and billions of connections can be pretty challenging due to memory and time constraints. Therefore, selecting the relevant nodes and edges of these large-scale networks while preserving their core information is a major […]
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Improved Hourly Prediction of BIPV Photovoltaic Power Building Using Artificial Learning Machine: A Case Study
In the energy transition, controlling energy consumption is a challenge for everyone, especially for BIPV (Building Integrated Photovoltaics) buildings. Artificial Intelligence is an efficient tool to analyze fine prediction with a better accuracy. Intelligent sensors are implemented on the different equipments of a BIPV building to collect information and to take decision about the energy […]
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A new approach for removing point cloud outliers using the standard score
Cleaning data is one of the most important tasks in data science and machine learning. It solves many problems in datasets, such as time complexity, added noise, and so on. In a huge datasets, outliers are extreme values that deviate from an overall pattern on a sample. Usually, they indicate variability in measurements or experimental […]
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