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

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

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.

Citation

Urquhart, N. B., Hart, E., & Scott, C. (2010). Building low CO2 solutions to the vehicle routing problem with time windows using an evolutionary algorithm. In IEEE Congress on Evolutionary Computation. https://doi.org/10.1109/CEC.2010.5586088

• Conference Name: International Conference on Evolutionary Computation
• Conference Location: Barcelona, Spain
• 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
• DOI: https://doi.org/10.1109/CEC.2010.5586088
• Keywords: Multi-Objective Algorithm; vehicle routing; Time Windows; CO2 emissions
• Public URL: http://researchrepository.napier.ac.uk/id/eprint/3792