Portrait of Angus Furneaux

Angus Furneaux

Research Student


Angus is a Research Student at Kent Business School, the University of Kent.

Research interests

Green Logistics: Advanced Methods for Transport Logistics Management Systems Including Platooning and Alternative Fuel Powered Vehicles
Green logistics is concerned with environmental effects that are due to the supply chain and transport logistics activities such as energy consumption, waste disposal, refuse recycling, etc. (Sbihi and Eglese, 2007). Transportation and distribution activities are amongst the major contributors to carbon air emission, resulting in environmental degradation that has implications such as acid rain, global climate change, etc. (Bae., Sarkis and Yoo, 2011). The literature in this area is concentrated traditionally on efficient vehicle routing management in terms of cost savings and development of faster algorithms. The interest in environmental issues has turned the focus towards incorporating environmental issues into consideration while developing fast and cost efficient algorithms. Recent success stories on new artificial intelligence based approaches, such as hybrid meta-heuristic algorithms, have made it possible to tackle complex supply chain and transport logistics optimisation problems more efficiently. With those wider ecological objectives, the traditional approaches need to be revisited in terms of both model extensions and new robust solution methodologies. 

Modern information technology advances have enabled us to collect transportation and road traffic data about the times of day and days of week. There are few transport logistics studies available which attempt to address aspects such as carbon emission by using this form of data. However, there is a need to bridge the gap between the academic research and reality. For instance in those studies, vehicles used in transportation are typically assumed as homogeneous, whereas in reality many organisations also use heterogeneous fleet. The proposed project is to address such gaps and include other aspects like speed and congestion.
The aim of this project is to develop a model of this problem, to create an appropriate problem-solving tool by developing and applying a state-of-the-art artificial intelligence based methodology and to investigate the applicability of this model by analysing the results of extensive computational experiments. This will provide companies with advice on tangible and intangible benefits such as cost savings and carbon emission reduction.



1st Supervisor's Research Group

Management Science

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