TY - JOUR
T1 - Green hybrid fleets using electric vehicles
T2 - solving the heterogeneous vehicle routing problem with multiple driving ranges and loading capacities
AU - Hatami, Sara
AU - Eskandarpour, Majid
AU - Chica, Manuel
AU - Juan, Angel A.
AU - Ouelhadj, Djamila
PY - 2020/6/1
Y1 - 2020/6/1
N2 - The introduction of Electric Vehicles (EVs) in modern fleets facilitates green road transportation. However, the driving ranges of EVs are limited by the duration of their batteries, which arise new operational challenges. Hybrid fleets of gas and EVs might be heterogeneous both in loading capacities as well as in driving-rangecapabilities, which makes the design of efficient routing plans a difficult task. In this paper, we propose a new Multi-Round Iterated Greedy (MRIG) metaheuristic to solve the Heterogeneous Vehicle Routing Problem with Multiple Driving ranges and loading capacities (HeVRPMD). MRIG uses a successive approximations method to offer the decision maker a set of alternativefleet configurations, with different distance-based costs and green levels. The numerical experiments show that MRIG is able to outperform previous works dealing with the homogeneous version of the problem, which assumes the same loading capacity for all vehicles in the fleet. The numerical experiments also confirm that the proposed MRIG approach extends previous works by solving a more realistic HeVRPMD and provides the decision-maker with fleets with higher green levels.
AB - The introduction of Electric Vehicles (EVs) in modern fleets facilitates green road transportation. However, the driving ranges of EVs are limited by the duration of their batteries, which arise new operational challenges. Hybrid fleets of gas and EVs might be heterogeneous both in loading capacities as well as in driving-rangecapabilities, which makes the design of efficient routing plans a difficult task. In this paper, we propose a new Multi-Round Iterated Greedy (MRIG) metaheuristic to solve the Heterogeneous Vehicle Routing Problem with Multiple Driving ranges and loading capacities (HeVRPMD). MRIG uses a successive approximations method to offer the decision maker a set of alternativefleet configurations, with different distance-based costs and green levels. The numerical experiments show that MRIG is able to outperform previous works dealing with the homogeneous version of the problem, which assumes the same loading capacity for all vehicles in the fleet. The numerical experiments also confirm that the proposed MRIG approach extends previous works by solving a more realistic HeVRPMD and provides the decision-maker with fleets with higher green levels.
KW - Electric Vehicles
KW - Heterogeneous Fleet
KW - Iterated Greedy heuristic
KW - Multiple Driving Ranges
KW - Successive Approximations Method
KW - Vehicle Routing Problem
UR - http://www.scopus.com/inward/record.url?scp=85094106450&partnerID=8YFLogxK
U2 - 10.2436/20.8080.02.98
DO - 10.2436/20.8080.02.98
M3 - Article
AN - SCOPUS:85094106450
SN - 1696-2281
VL - 44
SP - 141
EP - 170
JO - SORT
JF - SORT
IS - 1
ER -