Personne :
Renaud, Jacques

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Renaud
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Jacques
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Université Laval. Département d'opérations et systèmes de décision
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ncf10313377
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2019 - 201912020 - 20211
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  • Publication
    Accès libre
    Measuring fuel consumption in vehicle routing : new estimation models using supervised learning
    (Taylor & Francis Group, 2021-07-06) Heni, Hamza; Diop, Serigne Arona; Renaud, Jacques; Coelho, Leandro C.
    In this paper, we propose and assess the accuracy of new fuel consumption estimation models for vehicle routing. Based on real-world data consisting of instantaneous fuel consumption, time-varying speeds observations, and high-frequency traffic, we propose effective methods to estimate fuel consumption. By carrying out nonlinear regression analysis using supervised learning methods, namely Neural Networks, Support Vector Machines, Conditional Inference Trees, and Gradient Boosting Machines, we develop new models that provide better prediction accuracy than classical models. We correctly estimate consumption for time-dependent point-to-point routing under realistic conditions. Our methods provide a more precise alternative to classical regression methods used in the literature, as they are developed for a specific situation. Extensive computational experiments under realistic conditions show the effectiveness of the proposed machine learning consumption models, clearly outperforming macroscopic and microscopic consumption models such as the Comprehensive Modal Emissions Model (CMEM) and the Methodology for Estimating air pollutant Emissions from Transport (MEET). Based on sensitivity analyses we show that MEET underestimates real-world consumption by 24.94% and CMEM leads to an overestimation of consumption by 7.57% with optimised parameters. Our best machine learning model (Gradient Boosting Machines) exhibited superior estimation accuracy with a gap of only 1.70%.
  • Publication
    Accès libre
    An iterated local search for the biomedical sample transportation problem with multiple and interdependent pickups
    (Pergamon, 2019-12-14) Anaya-Arenas, Ana Maria; Prodhon, Caroline; Ruiz, Angel; Renaud, Jacques
    This paper addresses a new version of the biomedical sample transportation problem, as a vehicle routing problem with precedence constraints arising in the context of health care logistics, and proposes an iterated local search algorithm to solve it. This new version is more realistic and complex since it considers the collection centers’ opening hours and the moment at which they are visited as decision variables, granting additional flexibility to elaborate more efficient routes. Indeed, this problem is harder to model and to solve than its previous version because the constraint on the short samples’ lifetime leads to interdependency between successive pickups at each collection center. A metaheuristic is thus proposed to solve real-life instances. Numerical experiments confirm (1) the value of simultaneously planning routes, opening hours, and visit hours (which is new in the literature) and (2) the efficiency of the proposed algorithm to solve this problem.