Paper Info
Title
Building Low Co2 Solutions To The Vehicle Routing Problem With Time Windows Using An Evolutionary Algorithm
Abstract
An evolutionary Multi-Objective Algorithm (MOA) is used to investigate the trade-off between CO2 savings, distance and number of vehicles used in a typical vehicle routing problem with Time Windows (VRPTW). A problem set is derived containing three problems based on accurate geographical data which encapsulates the topology of streets as well as layouts and characteristics of junctions. This is combined with realistic speed-flow data associated with road-classes and a power-based instantaneous fuel consumption model to calculate CO2 emissions, taking account of drive-cycles. Results obtained using a well-known MOA with twin objectives show that it is possible to save up to 10% CO2, depending on the problem instance and ranking criterion used.
Year
DOI
Venue
2010
10.1109/CEC.2010.5586088
2010 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
Keywords
Field
DocType
evolutionary computation,transportation,vehicle routing problem,evolutionary algorithm,optimization,routing,drive cycle,fuel consumption
Mathematical optimization,Vehicle routing problem,Evolutionary algorithm,Ranking,Computer science,Problem set,Road traffic,Evolutionary computation,Fuel efficiency,Driving cycle
Conference
Citations 
PageRank 
References 
7
0.67
8
Authors
3
Name
Order
Citations
PageRank
neil b urquhart18314.70
Emma Hart2286.90
catherine scott3132.00