Building low CO2 solutions to the Vehicle Routing Problem with Time Windows using an Evolutionary Algorithm

Urquhart, N., Hart, E., Scott, C. (2010). Building low CO2 solutions to the Vehicle Routing Problem with Time Windows using an Evolutionary Algorithm. In: (Ed.) Procedings of CEC 2010, , () ( ed.). (pp. ). Barcelona: . IEEE.


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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.
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Authors

Emma Hart
Director of CEC
e.hart@napier.ac.uk
+44 131 455 2783
Neil Urquhart
Lecturer
n.urquhart@napier.ac.uk
+44 131 455 2655
Catherine Scott
Research Student
c.scott@napier.ac.uk
+44 131 455

Areas of Expertise

Bio-inspired Computing
The Bio-Inspired Algorithms group within the Centre for Algorithms, Visualisation and Evolving Systems is a large and thriving group with interests in nature-inspired computing that include Evolutionary Computing, Hyper-Heuristics, Artificial Immune Systems and Swarm Intelligence.

Associated Projects