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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 20
  • 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
    A parallel variable neighborhood search for Alpha-neighbor facility location problems
    (Elsevier Science, 2024-02-23) Chagas, Guilherme O.; Lorena, Luiz A.N.; Santos, Rafael D. C. dos; Renaud, Jacques; Coelho, Leandro C.
    In this paper, we employ the less is more approach to develop a Parallel Variable Neighborhood Search (VNS) algorithm for the Alpha-neighbor p-center problem (AlphaNpCP) and the Alpha-neighbor p-median problem (AlphaNpMP). The AlphaNpCP and the AlphaNpMP are generalizations of the p-center (pCP) and p-median (pMP) problems, respectively. In the Alpha-neighbor problems, one seeks to open p facilities and assign each of the n customers to their closest Alpha ones. The objective is to minimize the maximum distance of a customer to its Alpha th facility, in the case of the AlphaNpCP, and the sum of the distances from each customer to their Alpha nearest facilities, in the case of the AlphaNpMP. Our VNS adapts simple but efficient algorithms and data structures from the pCP and pMP literature to the AlphaNpCP and AlphaNpMP context. We also introduce an updated objective function for the AlphaNpCP, which adds more information to the solution cost and helps the VNS to escape from local optima. Several experimental tests show that our VNS outperforms more complex state-of-the-art algorithms. Regarding the AlphaNpCP, on 120 instances derived from the OR-library set, our algorithm improved best-known solutions for 22, with an average improvement of 34.26%; the overall gap on the 120 instances is 6.18% in favor of our algorithm. Moreover, on 231 instances derived from the TSPLIB set, we improved the solutions for 115, with an average improvement of 5.30%, and an overall improvement gap of 2.47% for all 231 instances. Considering the AlphaNpMP results, our heuristic obtained better results than a heuristic from literature in all 80 instances tested, finding optimal solutions in all these instances.
  • 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
    Service level, cost and environmental optimization of collaborative transportation
    (Elsevier, 2017-12-09) Legault-Michaud, Ariane; Bouchard, Florence; Chabot, Thomas; Renaud, Jacques; Coelho, Leandro C.
    Less than truckload is an important type of road-based transportation. Based on real data and on a collaboration with industry, we show that a collaborative approach between companies offers important benefits. We propose to develop partnerships between shipping companies and to synchronize their shipments. Four operational collaborative schemes with different objectives are developed. The first one focuses on minimizing shipping costs for shippers. The second and third ones minimize the carrier’s costs and the environmental cost, respectively. The fourth one is a combination of all three. The results of our computational experiments demonstrate that collaboration lead to significant cost reductions.
  • 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
    Mathematical Model, Heuristics and Exact Method for Order Picking in Narrow Aisles
    (Basingstoke Basingstoke Stockton Palgrave, 2018-07-01) Chabot, Thomas; Côté, Jean-François; Renaud, Jacques; Coelho, Leandro C.
    Order picking is one of the most challenging operations in distribution centre management and one of the most important sources of costs. One way to reduce the lead time and associated costs is to minimise the total amount of work for collecting all orders. This paper is motivated by a collaboration with an industrial partner who delivers furniture and electronic equipment. We have modelled their narrow aisles order picking problem as a vehicle routing problem through a series of distance transformations between all pairs of locations. Security issues arising when working on narrow aisles impose an extra layer of difficulty when determining the routes. We show that these security measures and the operator equipment allow us to decompose the problem per aisle. In other words, if one has to pick orders from three aisles in the warehouse, it is possible to decompose the problem and create three different instances of the picking problem. Our approach yields an exact representation of all possible picking sequences. We also show that neglecting 2D aspects and solving the problem over a 1D warehouse yields significant difference in the solutions, which are then suboptimal for the real 2D case. We have solved a large set of instances reproducing realistic configurations using a combination of heuristics and an exact algorithm, minimising the total distance travelled for picking all items. Through extensive computational experiments, we identify which of our methods are better suited for each aisle configuration. We also compare our solutions with those obtained by the company order picking procedures, showing that improvements can be achieved by using our approach
  • PublicationAccès libre
    Road-based goods transportation : a survey of real-world logistics applications from 2000 to 2015
    (Taylor & Francis, 2016-04-04) Renaud, Jacques; Coelho, Leandro C.; Laporte, Gilbert
    The vehicle routing problem has been widely studied from a technical point of view for more than 50 years. Many of its variants are rooted in practical settings. This paper provides a survey of the main real-life applications of road-based goods transportation over the past 15 years. It reviews papers in the areas of oil, gas and fuel transportation, retail, waste collection and management, mail and package delivery and food distribution. Some perspectives on future research and applications are discussed.
  • PublicationAccès libre
    Order picking problems under weight, fragility, and category constraints
    (Taylor & Francis, 2016-10-27) Chabot, Thomas; Lahyani, Rahma; Renaud, Jacques; Coelho, Leandro C.
    Warehouse order picking activities are among the ones that impact the most the bottom lines of warehouses. They are known to often account for more than half of the total warehousing costs. New practices and innovations generate new challenges for managers and open new research avenues. Many practical constraints arising in real-life have often been neglected in the scientific literature. We introduce, model and solve a rich order picking problem under weight, fragility and category constraints, motivated by our observation of a real-life application arising in the grocery retail industry. This difficult warehousing problem combines complex picking and routing decisions under the objective of minimising the distance travelled. We first provide a full description of the warehouse design which enables us to algebraically compute the distances between all pairs of products. We then propose two distinct mathematical models to formulate the problem. We develop five heuristic methods, including extensions of the classical largest gap, mid-point, S-shape and combined heuristics. The fifth one is an implementation of the powerful adaptive large neighbourhood search algorithm specifically designed for the problem at hand. We then implement a branch-and-cut algorithm and cutting planes to solve the two formulations. The performance of the proposed solution methods is assessed on a newly generated and realistic test bed containing up to 100 pickups and 7 aisles. We compare the bounds provided by the two formulations. Our in-depth analysis shows which formulation tends to perform better. Extensive computational experiments confirm the efficiency of the ALNS metaheuristic and derive some important insights for managing order picking in this kind of warehouses.