TY - JOUR
T1 - The location routing problem using electric vehicles with constrained distance
AU - Almouhanna, Abdullah
AU - Quintero-Araujo, Carlos L.
AU - Panadero, Javier
AU - Khosravi, Banafsheh
AU - Ouelhadj, Djamila
AU - Juan, Angel A.
N1 - Publisher Copyright:
© 2019 Elsevier Ltd
PY - 2020/3
Y1 - 2020/3
N2 - The introduction of Electric Vehicles (EVs) in modern fleets facilitates a shift towards greener road transportation practices. However, the driving ranges of EVs are limited by the duration of their batteries, which raises some operational challenges. This paper discusses the Location Routing Problem with a Constrained Distance (LRPCD), which is a natural extension of the Location Routing Problem when EVs are utilized. A fast multi-start heuristic and a metaheuristic are proposed to solve the LRPCD. The former combines biased-randomization techniques with the well-known Tillman's heuristic for the Multi-Depot Vehicle Routing Problem. The latter incorporates the biased-randomized approach into the Variable Neighborhood Search (VNS) framework. A series of computational experiments show that the multi-start heuristic is able to generate good-quality solutions in just a few seconds, while the biased-rendomized VNS metaheuristic provides higher-quality solutions by employing more computational time.
AB - The introduction of Electric Vehicles (EVs) in modern fleets facilitates a shift towards greener road transportation practices. However, the driving ranges of EVs are limited by the duration of their batteries, which raises some operational challenges. This paper discusses the Location Routing Problem with a Constrained Distance (LRPCD), which is a natural extension of the Location Routing Problem when EVs are utilized. A fast multi-start heuristic and a metaheuristic are proposed to solve the LRPCD. The former combines biased-randomization techniques with the well-known Tillman's heuristic for the Multi-Depot Vehicle Routing Problem. The latter incorporates the biased-randomized approach into the Variable Neighborhood Search (VNS) framework. A series of computational experiments show that the multi-start heuristic is able to generate good-quality solutions in just a few seconds, while the biased-rendomized VNS metaheuristic provides higher-quality solutions by employing more computational time.
KW - Biased randomization
KW - Green logistics
KW - Location routing problem
KW - Variable neighborhood search
UR - http://www.scopus.com/inward/record.url?scp=85075969501&partnerID=8YFLogxK
U2 - 10.1016/j.cor.2019.104864
DO - 10.1016/j.cor.2019.104864
M3 - Article
AN - SCOPUS:85075969501
SN - 0305-0548
VL - 115
JO - Computers and Operations Research
JF - Computers and Operations Research
M1 - 104864
ER -