scholarly journals A branch-and-price algorithm for two-echelon electric vehicle routing problem

Author(s):  
Zhiguo Wu ◽  
Juliang Zhang

AbstractMotivated by express and e-commerce companies’ distribution practices, we study a two-echelon electric vehicle routing problem. In this problem, fuel-powered vehicles are used to transport goods from a depot to intermediate facilities (satellites) in the first echelon, whereas electric vehicles, which have limited driving ranges and need to be recharged at recharging stations, are used to transfer goods from the satellites to customers in the second echelon. We model the problem as an arc flow model and decompose the model into a master problem and pricing subproblem. We propose a branch-and-price algorithm to solve it. We use column generation to solve the restricted master problem to provide lower bounds. By enumerating all the subsets of the satellites, we generate feasible columns by solving the elementary shortest path problem with resource constraints in the first echelon. Then, we design a bidirectional labeling algorithm to generate feasible routes in the second echelon. Comparing the performance of our proposed algorithm with that of CPLEX in solving a set of small-sized instances, we demonstrate the former’s effectiveness. We further assess our algorithm in solving two sets of larger scale instances. We also examine the impacts of some model parameters on the solution.

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Alberto Ceselli ◽  
Ángel Felipe ◽  
M. Teresa Ortuño ◽  
Giovanni Righini ◽  
Gregorio Tirado

AbstractWe provide an exact optimization algorithm for the electric vehicle routing problem with multiple recharge technologies. Our branch-and-cut-and-price algorithm relies upon a path-based formulation, where each column in the master problem represents a sequence of customer visits between two recharge stations instead of a whole route. This allows for massive decomposition, and parallel implementation of the pricing phase, exploiting the large number of independent pricing sub-problems. The algorithm could solve instances with up to thirty customers, nine recharge stations, five vehicles and three technologies to proven optimality. Near-optimal heuristic solutions were obtained with a general-purpose MIP solver from the columns generated at the root node.


2011 ◽  
Vol 37 ◽  
pp. 249-254 ◽  
Author(s):  
Fernando Afonso Santos ◽  
Geraldo Robson Mateus ◽  
Alexandre Salles da Cunha

Networks ◽  
2018 ◽  
Vol 73 (4) ◽  
pp. 401-417 ◽  
Author(s):  
Hamza Ben Ticha ◽  
Nabil Absi ◽  
Dominique Feillet ◽  
Alain Quilliot ◽  
Tom Van Woensel

2010 ◽  
Vol 206 (2) ◽  
pp. 341-349 ◽  
Author(s):  
Gabriel Gutiérrez-Jarpa ◽  
Guy Desaulniers ◽  
Gilbert Laporte ◽  
Vladimir Marianov

2014 ◽  
Vol 48 (3) ◽  
pp. 425-441 ◽  
Author(s):  
Ibrahim Muter ◽  
Jean-François Cordeau ◽  
Gilbert Laporte

Author(s):  
Alexandre M. Florio ◽  
Richard F. Hartl ◽  
Stefan Minner ◽  
Juan-José Salazar-González

In many routing applications, it is necessary to place limits on the duration of the individual routes. When demands are stochastic and restocking during route execution is allowed, the durations of the resulting routes are also stochastic. In this paper, we consider the vehicle routing problem with stochastic demands and probabilistic duration constraints (VRPSD-PDC). We assume optimal restocking, which means that, during the route execution, replenishment trips to the depot are performed in an optimal way. The resulting optimization problem calls for a set of routes with minimal total expected cost for visiting all customers, such that the duration of each route, with a given probability, does not exceed a prescribed limit. We solve the VRPSD-PDC with a novel branch-and-price algorithm. An orienteering-based completion bound is proposed to control the growth of labels in the pricing algorithm. Feasibility of a priori routes is verified by applying Chebyshev’s bounds, by Monte Carlo simulation and statistical inference, or by analytically deriving the distribution of the route duration. Consistency checks are incorporated into the branch-and-price framework to detect statistical errors. Computational experiments are performed with demands following binomial, Poisson, or negative binomial probability distributions and with duration constraints enforced at the levels of 90%, 95%, and 98%. Optimal solutions to the VRPSD-PDC may contain routes that serve an expected demand that is larger than the capacity of the vehicle. These solutions actively employ optimal restocking to reduce traveling costs and the number of required vehicles. Sensitivity analyses indicate that high demand variability negatively impacts the solution, both in terms of total expected cost and the number of routes employed.


2017 ◽  
Vol 100 ◽  
pp. 115-137 ◽  
Author(s):  
Gizem Ozbaygin ◽  
Oya Ekin Karasan ◽  
Martin Savelsbergh ◽  
Hande Yaman

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