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Building low CO2 solutions to the vehicle routing problem with Time Windows using an evolutionary algorithm

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3 Author(s)
Urquhart, N. ; Centre for Emergent Comput., Edinburgh Napier Univ., Edinburgh, UK ; Hart, E. ; Scott, C.

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.

Published in:
Evolutionary Computation (CEC), 2010 IEEE Congress on

Date of Conference: 18-23 July 2010

Page(s):
1 - 6
Print ISBN:
978-1-4244-6909-3
INSPEC Accession Number:
11567960
Conference Location :
Barcelona
Digital Object Identifier :
10.1109/CEC.2010.5586088
Product Type:
Conference Publications

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