Activities per year
Project Details
Description
The problem of finding optimal locations for service facilities is of strategic importance. The optimal placement of a fire station could save a forest from destruction, that of a hospital could save numerous lives, and that of a supermarket could save thousands of minutes in customers’ travel times. Although an increasing number of models have been proposed, an adequate representation of demand is often crucially neglected. In most models customer demand is assumed to be discrete and aggregated to a relatively small number of points. However, in many applications the number of potential customers can be in the millions and representing every customer residence as a separate demand point is usually infeasible. Therefore it may be more accurate to represent customer demand as continuously distributed over some region.
Furthermore, the region of demand and the region over which a facility can be feasibly located are often assumed to be convex polygons. However, this supposition is not realistic for real-world applications. Moreover, in urban settings the predominantly used straight-line distance does not adequately represent the realised distance for the customer. Further problems arise when we introduce non-traversable areas (e.g. rivers or parks) since this fundamentally alters the way we measure distances between facilities and their demand.
Bringing together results from my PhD and recent work by Professor Suzuki, our goal is to plug the hole in such models by extending existing facility location algorithms to work with this continuous demand distributed over an arbitrary area using a more appropriate measure of distance for real-world applications. This will not only produce more applicable results but also increase the focus of academics onto these very relevant, but unjustifiably ignored, restrictions to such facility location problems, whilst bridging the gaps between academic communities since disciplines such as geometry, calculus, and algebra are also required.
Furthermore, the region of demand and the region over which a facility can be feasibly located are often assumed to be convex polygons. However, this supposition is not realistic for real-world applications. Moreover, in urban settings the predominantly used straight-line distance does not adequately represent the realised distance for the customer. Further problems arise when we introduce non-traversable areas (e.g. rivers or parks) since this fundamentally alters the way we measure distances between facilities and their demand.
Bringing together results from my PhD and recent work by Professor Suzuki, our goal is to plug the hole in such models by extending existing facility location algorithms to work with this continuous demand distributed over an arbitrary area using a more appropriate measure of distance for real-world applications. This will not only produce more applicable results but also increase the focus of academics onto these very relevant, but unjustifiably ignored, restrictions to such facility location problems, whilst bridging the gaps between academic communities since disciplines such as geometry, calculus, and algebra are also required.
Status | Finished |
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Effective start/end date | 25/04/22 → 24/04/23 |
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):
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Activities
- 1 Visiting an external academic institution
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Nanzan University
Byrne, T. (Visiting researcher)
25 Apr 2022 → 24 Apr 2023Activity: Visiting an external institution types › Visiting an external academic institution