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Inventory management for slow-moving items is challenging due to their high intermittence and lumpiness. Recent developments in machine learning and computat...

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This paper introduces the Heterogeneous-Fleet Electric Vehicle Routing Problem with Nonlinear Charging Functions (HEVRP-NL). This problem involves routing a ...

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A challenge in aircraft design optimization is the presence of non-computable, so-called hidden, constraints that do not return a value in certain regions of...

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In this work, we improve the efficiency of Unit Commitment (UC) optimization solvers using a Graph Convolutional Neural Network (GCNN). In power systems, UC ...

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Cet article propose une nouvelle étape à ajouter à chaque itération de la Méthode de Recherche Directe Direct Search Method (DSM) en anglais) pour renfor...

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Le problème des décisions optimales de réapprovisionnement des stocks vise à équilibrer les coûts des stocks excédentaires et les risques de pénurie. Dans la...

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In this work, we propose a non-intrusive and training free method to detect behind-the-meter (BTM) electric vehicle (EV) charging events from the data measur...

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La bibliothèque de sous-programmes Harwell (HSL) est une suite renommée de méthodes numériques efficaces et robustes conçus pour résoudre des problèmes mathé...

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We show that the two-stage minimum description length (MDL) criterion widely used to estimate linear change-point (CP) models corresponds to the marginal lik...

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We study the relation between the promotion of a cryptocurrency on Twitter and its return dynamics around pump-and-dump events. By analyzing abnormal retur...

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Monte Carlo (MC) is widely used for the simulation of discrete time Markov chains. We consider the case of a \(d\)-dimensional continuous state space and w...

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Randomized Quasi-Monte Carlo (RQMC) methods provide unbiased estimators whose variance often converges at a faster rate than standard Monte Carlo as a functi...

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This work introduces a novel multi-fidelity blackbox optimization algorithm designed to alleviate the resource-intensive task of evaluating infeasible points...

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This study addresses large-scale personnel scheduling problems in the service industry by combining mathematical programming with data mining techniques to...

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We consider the set of graphs that can be constructed from a one-vertex graph by repeatedly adding a clique or a stable set linked to all or none of the vert...

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Incentive-based demand response aggregators are widely recognized as a powerful strategy to increase the flexibility of residential community microgrid (RCM)...

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In 1960, Ailsa Land and Alison Doig published the first linear programming-based branch-and-bound algorithm for the solution of mixed integer linear progra...

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In this paper, a mixed integer nonlinear model for the short-term hydropower optimization problem considering operational constraints such as demand and st...

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Classical mean field games (MFG) have been concerned with large games amongst symmetrically influential agents with asymptotically negligible weight. In th...

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L'apprentissage par renforcement (RL) pour les processus décisionnels de Markov partiellement observables (POMDP) est un problème difficile car les décisions...

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