TY - JOUR
T1 - Problem size reduction methods for large CVRPs
AU - Müller, Alina-Gabriela
AU - Müller, David
PY - 2024/12
Y1 - 2024/12
N2 - We solve the Capacitated Vehicle Routing Problem (CVRP) by introducing a novel approach to problem size reduction. We propose the generation of short sequences of nodes called “sections”, which effectively act as single nodes in a reduced CVRP that is faster and easier to solve. Three section generation methods are compared, and the trade-off between solution quality and computation time savings is evaluated. We show that reduced problem sizes of up to around 60 percent of the original problem size, result in only modest decreases in solution quality, but allow for significant reductions of computation time, regardless of the optimization algorithm used. Our findings highlight the potential benefits of problem aggregation and size reduction for large-scale CVRPs and suggest opportunities for further improving aggregation methods.
AB - We solve the Capacitated Vehicle Routing Problem (CVRP) by introducing a novel approach to problem size reduction. We propose the generation of short sequences of nodes called “sections”, which effectively act as single nodes in a reduced CVRP that is faster and easier to solve. Three section generation methods are compared, and the trade-off between solution quality and computation time savings is evaluated. We show that reduced problem sizes of up to around 60 percent of the original problem size, result in only modest decreases in solution quality, but allow for significant reductions of computation time, regardless of the optimization algorithm used. Our findings highlight the potential benefits of problem aggregation and size reduction for large-scale CVRPs and suggest opportunities for further improving aggregation methods.
KW - vehicle routing problem
KW - heuristics
KW - Decomposition methods
KW - Vehicle routing
KW - Heuristics
UR - http://www.scopus.com/inward/record.url?scp=85202347383&partnerID=8YFLogxK
U2 - 10.1016/j.cor.2024.106820
DO - 10.1016/j.cor.2024.106820
M3 - Article
SN - 0305-0548
VL - 172
JO - Computers & Operations Research
JF - Computers & Operations Research
M1 - 106820
ER -