A theoretical and computational study of green vehicle routing problems

A theoretical and computational study of green vehicle routing problems

This work investigates green vehicle routing problems (G-VRPs). GVRPs are NP-hard problems embodying the motivation, concepts, and advances of green logistics in the vehicle routing problem (VRP) domain. To address the shorter autonomy of electric vehicles, the G-VRP considers Alternative Fuel Stati...

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Bibliographic Details
Published in:Journal of combinatorial optimization Vol. 45; no. 5
Main Authors: Andrade, Matheus Diógenes, Usberti, Fábio Luiz
Format: Journal Article
Language:English
Published: New York Springer US 01-07-2023
Springer Nature B.V
Subjects:
Alternative fuels
Combinatorial analysis
Combinatorics
Convex and Discrete Geometry
Electric vehicles
Energy consumption
Exact solutions
Fuel consumption
Lower bounds
Mathematical Modeling and Industrial Mathematics
Mathematics
Mathematics and Statistics
Operations Research/Decision Theory
Optimization
Theory of Computation
Vehicle routing
Vehicles
Mixed integer linear programming
Combinatorial optimization
Green logistics
Vehicle routing problem
Mathematical programming
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Summary:This work investigates green vehicle routing problems (G-VRPs). GVRPs are NP-hard problems embodying the motivation, concepts, and advances of green logistics in the vehicle routing problem (VRP) domain. To address the shorter autonomy of electric vehicles, the G-VRP considers Alternative Fuel Stations (AFSs) that can be used to refuel vehicles in travel. Originally, the G-VRP prohibits consecutive AFS visits, i.e., a solution cannot have an edge between two AFSs. Here, besides the original G-VRP, we also consider the variant in which consecutive AFS visits are allowed. This research proposes combinatorial properties, concerning the number of visits to the AFSs, bounds on fuel consumption, and bounds on the number of routes and their cost. Furthermore, this research proposes valid inequalities, MILP formulations, preprocessing conditions, and lower bounds which strengthen the mathematical formulations for both G-VRP versions, thus improving their exact solution. The proposed methodologies were evaluated with extensive computational experiments. The results are analyzed and discussed, and conclusions on the benefits of the contributions are presented.
ISSN:1382-6905
1573-2886
DOI:10.1007/s10878-023-01043-4