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A chance constrained programming model and an improved large neighborhood search algorithm for the electric vehicle routing problem with stochastic travel times

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Abstract

The Electric Vehicle Routing Problem (EVRP) is an extension to the well-known Vehicle Routing Problem (VRP) where the fleet consists of electric vehicles, which may need to visit recharging stations while servicing the customers due to their battery capacities. This paper solves the Electric Vehicle Routing Problem with Stochastic Travel Times (EVRPSTT) by proposing a Chance Constrained Programming (CCP) Model, as well as a new scheme based on an Improved Large Neighborhood Search (ILS) algorithm and a Monte Carlo Sampling (MCS) procedure. The proposed approach is firstly tested in the deterministic environment using the EVRP benchmark data set, where the numerical results show that this approach is able to provide EVRP optimal solutions, within a very short computational time, for 39 out of 48 used benchmark instances with 20, 50, 75 and 100 customers. Thereafter, to show the efficiency of the proposed approach for solving the CCP model of the EVRPSTT, others tests are performed on the same set of instances, while taking into consideration a large number of scenarios.

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  1. Logistics, Transport and Road Safety Research Team, Hassania School of Public Works, Casablanca, Morocco

    Elhassania Messaoud

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Messaoud, E. A chance constrained programming model and an improved large neighborhood search algorithm for the electric vehicle routing problem with stochastic travel times. Evol. Intel. 16, 153–168 (2023). https://doi.org/10.1007/s12065-021-00648-0

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  • DOI: https://doi.org/10.1007/s12065-021-00648-0

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