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Building low CO2 solutions to the vehicle routing problem with time windows using an evolutionary algorithm.

Urquhart, Neil B; Hart, Emma; Scott, Cathy


Cathy Scott


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.

Presentation Conference Type Conference Paper (Published)
Conference Name International Conference on Evolutionary Computation
Start Date Jul 18, 2010
End Date Jul 23, 2010
Acceptance Date Sep 27, 2010
Publication Date Sep 27, 2010
Deposit Date Jun 14, 2010
Publicly Available Date Sep 27, 2010
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Book Title IEEE Congress on Evolutionary Computation
ISBN 9781424469093
Keywords Multi-Objective Algorithm; vehicle routing; Time Windows; CO2 emissions;
Public URL
Contract Date Jun 14, 2010


Building Low CO2 Solutions To The Vehicle Routing Problem With Time Windows Using An Evolutionary Algorithm. (923 Kb)

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