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

Urquhart, Neil B, Hart, Emma and Scott, Catherine (2010) Building low CO2 solutions to the vehicle routing problem with time windows using an evolutionary algorithm. In: International Conference on Evolutionary Computation, 24th-26th October 2010, Valencia, Spain.

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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.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Full publication details including a DOI will be added when the proceedings are published.
Uncontrolled Keywords: Multi-Objective Algorithm; vehicle routing; Time Windows; CO2 emissions;
University Divisions/Research Centres: Faculty of Engineering, Computing and Creative Industries > School of Computing
Dewey Decimal Subjects: 000 Computer science, information & general works > 000 Computer science, knowledge & systems > 003 Systems > 003.3 Computer modelling & simulation
Library of Congress Subjects: Q Science > QA Mathematics > QA76 Computer software
Item ID: 3792
Depositing User: Computing Research
Date Deposited: 14 Jun 2010 15:52
Last Modified: 27 Jul 2011 15:34

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