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Personne :
Coelho, Leandro C.

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Coelho

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Leandro C.

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Université Laval. Département d'opérations et systèmes de décision

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ncf10614755

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Résultats de recherche

Voici les éléments 1 - 10 sur 10
  • PublicationAccès 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%.
  • PublicationAccès libre
    Quadratic assignment problem variants : a survey and an effective parallel memetic iterated tabu search
    (2020-11-26) Silva, Allyson; Darvish, Maryam; Coelho, Leandro C.
    In the Quadratic Assignment Problem (QAP), facilities are assigned to sites in order to minimize interactions between pairs of facilities. Although easy to define, it is among the hardest problems in combinatorial optimization, due to its non-linear nature. After decades of research on the QAP, many variants of this problem arose to deal with different applications. Along with the QAP, we consider four variants – the Quadratic Bottleneck Assignment Problem, the Biquadratic Assignment Problem, the Quadratic Semi-Assignment Problem, and the Generalized QAP – and develop a single framework to solve them all. Our parallel memetic iterated tabu search (PMITS) extends the most successful heuristics to solve the QAP. It combines the diversification phase of generating new local optima found after solutions modified by a new crossover operator that is biased towards one of the parents, with the intensification phase of an effective tabu search which uses a simplified tabu list structure to reduce the number of parameters and a new long-term memory that saves solutions previously visited to speed up the search. Solutions are improved concurrently using parallelism, and a convergence criterion determines whether the search stops according to the best solutions in each parallel search. Computational experiments using the hardest benchmark instances from the literature attest the effectiveness of the PMITS, showing its competitiveness when compared to the state-of-the-art methods, sequential and parallel, to solve the QAP. We also show that PMITS significantly outperforms the best methods found for all four variants of the QAP, significantly updating their literature.
  • PublicationAccès libre
    Integrating storage location and order picking problems in warehouse planning
    (Pergamon, 2020-07-02) Silva, Allyson; Darvish, Maryam; Renaud, Jacques; Coelho, Leandro C.
    Storage location and order picking are two interdependent problems arising in warehouse planning traditionally solved independently. We introduce and model the integrated storage location and order picking problem and four special cases with imposed routing policies (return, S-shape, midpoint and largest gap). Experiments show that these models are difficult to solve, even for small warehouses and few orders. Therefore, we present a General Variable Neighborhood Search metaheuristic, which is observed to be very efficient for those small instances. For larger warehouses and more pickings, we show that our metaheuristic significantly improves solutions generated by common storage policies.
  • PublicationAccès libre
    Analyse spatio-temporelle des tournées de livraison d’une entreprise de livraison à domicile
    (Hermès, 2020-03-02) Belhassine, Khaled; Gagliardi, Jean-Philippe; Renaud, Jacques; Coelho, Leandro C.
    Dans cet article, nous présentons une analyse spatiotemporelle des tournées de livraison à domicile d’une entreprise d’électroménagers qui détient sa propre flotte de véhicules. Plusieurs millions d’observations de géolocalisation GPS issues de ces tournées de livraison sont collectées, traitées et assignées au réseau routier. À la suite de ces analyses spatiotemporelles, nous développons des calendriers quotidiens d’indices de congestion en fonction de l’heure. Des ratios de congestion sectoriels sont calculés afin de déterminer les meilleures heures de départ de livraison tout en évitant la congestion routière. La réduction de la durée des trajets a été quantifiée en comparant les meilleures heures de départs par rapport aux heures habituelles. À partir des données de notre partenaire, les analyses démontrent une réduction potentielle de 22 % de la durée des routes de livraison.
  • PublicationAccès libre
    Integrated production-distribution systems : Trends and perspectives
    (Scientific Electronic Library Online (SciELO Brazil), 2021-04-21) Darvish, Maryam; Renaud, Jacques; Coelho, Leandro C.; Kidd, Martin P.
    During the last two decades, integrated production-distribution problems have attracted a great deal of attention in the operations research literature. Within a short period, a large number of papers have been published and the field has expanded dramatically. The purpose of this paper is to provide a comprehensive review of the existing literature by classifying the existing models into several different categories based on multiple characteristics. The paper also discusses some trends and list promising avenues for future research.
  • PublicationAccès libre
    A cutting plane method and a parallel algorithm for packing rectangles in a circular container
    (Elsevier, 2022-02-15) Silva, Allyson; Darvish, Maryam; Renaud, Jacques; Coelho, Leandro C.
    We study a two-dimensional packing problem where rectangular items are placed into a circular container to maximize either the number or the total area of items packed. We adapt a mixed-integer linear programming model from the case with a rectangular container and design a cutting plane method to solve this problem by adding linear cuts to forbid items from being placed outside the circle. We show that this linear model allows us to prove optimality for instances larger than those solved using the state-of-the-art non-linear model for the same problem. We also propose a simple parallel algorithm that efficiently enumerates all non-dominated subsets of items and verifies whether pertinent subsets fit into the container using an adapted version of our linear model. Computational experiments using large benchmark instances attest that this enumerative algorithm generally provides better solutions than the best heuristics from the literature when maximizing the number of items packed. Instances with up to 30 items are now solved to optimality, against the eight-item instance previously solved.
  • PublicationAccès libre
    The time-dependent shortest path and vehicle routing problem
    (Taylor & Francis Group, 2021-09-11) Jaballah, Rabie; Veenstra, Marjolein; Renaud, Jacques; Coelho, Leandro C.
    We introduce the time-dependent shortest path and vehicle routing problem. In this problem, a set of homogeneous vehicles is used to visit a set of customer locations dispersed over a very large network where the travel times are time-dependent and therefore the shortest path between two locations may change over time. The aim of the problem is to simultaneously determine the sequence in which the customer locations are visited and the arcs traveled on the paths between each pair of consecutively visited customers, such that the total travel time is minimized. We are the first to formally define and solve this fully integrated problem, providing tight bounds to it. We then propose a dynamic time-dependent shortest path algorithm embedded within a simulated annealing metaheuristic to efficiently solve the problem. We also propose a variant of the algorithm where some time-dependent shortest paths are precomputed. We test our formulations and algorithms on a set of real-life instances generated from a dataset of the road network in Québec City, Canada. Our results indicate that the resulting models are too large to be solved even for small instances. However, the obtained bounds show that the developed simulated annealing heuristic performs very well. We also demonstrate that neglecting time-dependent information on traffic leads to imprecise estimation of the traveling time. Moreover, the results show the importance of solving the shortest paths and routing problems simultaneously, as using a set of precomputed shortest paths leads to slightly worse solutions. This work adds new research avenues to city logistics and congestion studies.
  • PublicationRestriction temporaire
    Modeling and solving the waste valorization production and distribution scheduling problem
    (Elsevier, 2022-06-24) Chagas, Guilherme O.; Darvish, Maryam; Renaud, Jacques; Coelho, Leandro C.
    Bio-based waste valorization is one of the current trends in municipal waste management. It decreases the amount of waste to be disposed of, reduces the sourcing of limited chemical compounds used in fertilizer production, and promotes a circular economy perspective vital in big cities. However, modeling and optimizing a biorefinery plant’s operations is challenging and requires innovative approaches and solutions. In this paper, we model and solve the integrated production and distribution scheduling problem faced by an industrial partner. We propose three models for the waste valorization production and distribution scheduling problem: a time-discretized integer linear program, and two mixed-integer linear program with continuous timing variables. Moreover, several powerful and problem-specific valid inequalities and variable reduction procedures are proposed. We study some variants of the problem and propose a simple heuristic algorithm that mimics the logic of a decision maker. Through a series of computational experiments, we determine how critical operational parameters affect the performance of the system and demonstrate how significant improvements can be achieved in our industrial partner’s biorefinery plant.
  • PublicationRestriction temporaire
    The dial-a-ride problem with private fleet and common carrier
    (Pergamon, 2022-07-06) Schenekemberg, Cleder M.; Coelho, Leandro C.; Chaves, Antonio A.; Guimarães, Thiago A.; Avelino, Gustavo G.
    Dial-a-ride problems aim to design the least-costly door-to-door vehicle routes for transporting individual users, subject to several service constraints like time windows, service and route durations, and ride-time. In some cases, providers cannot meet the demand and may outsource some requests. In this paper, we introduce, model, and solve the dial-a-ride problem with private fleet and common carrier (DARP-PFCC) that makes it possible to transfer the demand unmet by the provider to mobility-on-demand services and taxis. All outsourced vehicles are assumed to be available at any instant of the day and have unlimited capacity, enabling to satisfy all user requests, particularly during peak times. We implement a branch-andcut (B&C) algorithm based on an exact method from the literature to solve the DARP-PFCC, and we develop a near parameter-free parallel metaheuristic to handle large instances. Our metaheuristic combines the Biased Random-key Genetic Algorithm (BRKGA) and the Q-learning (QL) method into the same framework (BRKGA-QL), in which an agent helps to use feedback information to dynamically choose the parameters of BRKGA during the search to select the most appropriate configuration to solve a specific problem instance. Both algorithms are flexible enough to solve the classical DARP, and extensive computational experiments demonstrate the efficiency of our methods. For the DARP instances, the B&C proved optimality for 41 of the 42 instances tested in a reasonable computational time, and the BRKGA-QL found the best-known solution for these instances within a matter of seconds. These results indicate that our metaheuristic performs equally well than state-of-the-art DARP algorithms. In the DARP-PFCC experiments on a set of 504 small-size instances, B&C proved optimality for 497 instances, while BRKGA-QL found 452 optimal solutions, totalling 90.94% of the instances solved to optimality. Finally, we present the results for a real case study for the DARP-PFCC, where BRKGA-QL solved very large problem instances containing up to 713 transportation requests. We also derive some managerial analyses to assess the effects of vehicle capacity reduction, for example due to the COVID-19 pandemic, on shared transportation. The results point to the benefits of combining the private fleet and common carriers in dial-a-ride problems, both for the provider and for the users.
  • PublicationRestriction temporaire
    Estimating optimal ABC zone sizes in manual warehouses
    (Elsevier, 2022-08-01) Silva, Allyson; Roodbergen, Kees Jan; Darvish, Maryam; Coelho, Leandro C.
    The ABC storage is the most popular class-based policy for the storage location assignment in warehouses. It divides a storage area into three zones and assigns the most demanded products to the best-located zone. Despite the policy's popularity, arbitrary zone sizes are commonly used, which can lead to major efficiency losses. We investigate how several factors, such as the warehouse layout, the demand characteristics, and the storage and routing policies, impact the solutions for the zone sizing problem. We propose a new methodology to solve it using machine learning models to predict the optimal zone sizes considering the mentioned factors. We simulate many common manual warehouse settings, such as the multi-block layout, demand distributions, and several operating policies, to observe which zone sizes lead to the best performance in each one. The data generated is used to train four regression models – ordinary least squares, regression tree, random forest, and multilayer perceptron – to predict the optimal zone sizes from the best ones observed. Computational experiments show that zone sizes provided by all models significantly improve the order picking efficiency when compared to the arbitrary zone sizes commonly used, notably for the one-zone (random policy), the two-zone (20/80 rule), and the three-zone (20/30/50) systems. The proposed methodology is easily adaptable for different warehousing systems and problems when enough data is available to train the models. The resulting linear functions and decision trees are made available and can be used by practitioners for determining zone sizes for their particular warehouse.