Intelligent, Optimised Scheduling and Routing

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Introduction
Many methods exist for producing automated solutions to problems. Often a solver will produce a single solution, with little or no detail as to how that solution was arrived at or what alternatives exist.

Evolutionary Algorithms have the potential to produce a group of solutions, all of which may be optimised towards specific objectives. If we can produce a group of solutions then we have the ability to give the end user a choice and allow them to explore options. For instance if we are trying to schedule social care visits, we could produce a range solutions that encompass differing environmental footprints, travel costs or staff costs. In such a scenario the user can make the final choice after viewing possible options. The final choice may well be due to organisational or political considerations (e.g. financial cost versus environmental impact) in such cases we present the user with a small number of differing solutions and let them make the final choice.

There are massive potential benefits of this research to society.  If we can convince agencies and individuals to optimise their travel activities and consider alternative solutions then we can potentially reduce our environmental impact, cut the cost of public services as well as supporting new and innovative services such as shortening food supply chains by linking consumers and local suppliers.

Collaboration with External Partners
The team are currently in the early stages of collaborating with a Scottish based start-up who are delivering food from small local suppliers directly to consumers. The focus is on finding ways for them to visualise their problems and find innovative solutions that balance the needs of customers and suppliers.

A longer-term project has been working with a supplier of software to the social care sector.  The team have been applying evolutionary techniques to some of the problems faced by their customers in trying to schedule care visits.

Academic Partners 
Within Napier,  the team has worked extensively with the Transport Research Institute and drawn upon their expertise in transport planning, costing and environmental issues. On a wider scale they are also working with Technische Hochschule Mittelhessen in Germany.

Researcher Background
Neil Urquhart is a lecturer at Edinburgh Napier University, with an interest in solving and optimising scheduling and routing problems.  During the late 1990s as an undergraduate Neil produced a dissertation on using Evolutionary Algorithms for factory scheduling, using production data from a work placement with a local printing company. After graduation, Neil undertook a PhD. which examined the use of software agents and evolutionary algorithms applied to the problem of designing networks for postal deliveries. Neil’s current research interests include the application of evolutionary algorithms to mobile workforces (such as social care workers), logistics and commuters.

Contact Details
Dr Neil Urquhart
Email: n.urquhart@napier.ac.uk
Website: http://www.napier.ac.uk/people/neil-urquhart