Navegando por Autor "Prins, Christian"

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A hybrid heuristic for a broad class of vehicle routing problems with heterogeneous fleet.
(2019) Penna, Puca Huachi Vaz; Subramanian, Anand; Ochi, Luiz Satoru; Vidal, Thibaut Victor Gaston; Prins, Christian
We consider a family of rich vehicle routing problems (RVRP) which have the particularity to combine a heterogeneous fleet with other attributes, such as backhauls, multiple depots, split deliveries, site dependency, open routes, duration limits, and time windows. To efficiently solve these problems, we propose a hybrid metaheuristic which combines an iterated local search with variable neighborhood descent, for solution improvement, and a set partitioning formulation, to exploit the memory of the past search. Moreover, we investigate a class of combined neighborhoods which jointly modify the sequences of visits and perform either heuristic or optimal reassignments of vehicles to routes. To the best of our knowledge, this is the first unified approach for a large class of heterogeneous fleet RVRPs, capable of solving more than 12 problem variants. The efficiency of the algorithm is evaluated on 643 well-known benchmark instances, and 71.70% of the best known solutions are either retrieved or improved. Moreover, the proposed metaheuristic, which can be considered as a matheuristic, produces high quality solutions with low standard deviation in comparison with previous methods. Finally, we observe that the use of combined neighborhoods does not lead to significant quality gains. Contrary to intuition, the computational effort seems better spent on more intensive route optimization rather than on more intelligent and frequent fleet re-assignments.
A hybrid iterative local search algorithm for the electric fleet size and mix vehicle routing problem with time windows and recharging stations.
(2016) Penna, Puca Huachi Vaz; Afsar, Hasan Murat; Prins, Christian; Prodhon, Caroline
As the cities around the world become larger, quality of life of the citizens is more and more threatened due to the traffic congestion, energy consumption, noise disturbance and carbon emissions because of of increasing transport. Electrical vehicles present an opportunity to reduce greenhouse gas emissions. But limited driving range and long recharging times are among the challenges that the research community has to face. This paper proposes an iterative local search algorithm coupled with a set partitioning model to solve The Electric Fleet Size and Mix Vehicle Routing Problem with Time Windows and Recharging Stations. Several types of electrical vehicles with varying driving range, capacity and fixed cost should service a set of customers within their time limits and during their tours each vehicle can be recharged in stations. We show the efficiency and the quality of our method by solving benchmark instances of the Heterogeneous Fleet Electric Vehicle Problems with Time Windows.
Vehicle routing problems for last mile distribution after major disaster.
(2018) Penna, Puca Huachi Vaz; Santos, Andrea Cynthia; Prins, Christian
This study is dedicated to a complex Vehicle Routing Problem (VRP) applied to the response phase after a natural disaster. Raised by the last mile distribution of relief goods after earthquakes, it is modelled as a rich VRP involving a heterogeneous fleet of vehicles, multiple trips, multiple depots, and vehicle-site dependencies. The proposed method is a generic hybrid heuristic that uses a Set Partitioning formulation to add memory to a Multi-Start Iterated Local Search framework. To better fit the requirements of last mile distribution, the algorithm has been developed in partnership with members of the International Charter on Space and Major Disasters and has been evaluated on real scenarios from Port-au-Prince earthquake. The heuristic quickly computes efficient routes while determining the number of required vehicles and the subset of depots to be used. Moreover, the computational results show that the proposed method is also competitive compared to the state of the art heuristics on closely related problems found in industrial distribution.