Publication Type

Conference Proceeding Article

Version

Postprint

Publication Date

6-2016

Abstract

Emergency Medical Systems (EMSs) are an important component of public health-care services. Improving infrastructure for EMS and specifically the construction of base stations at the ”right” locations to reduce response times is the main focus of this paper. This is a computationally challenging task because of the: (a) exponentially large action space arising from having to consider combinations of potential base locations, which themselves can be significant; and (b) direct impact on the performance of the ambulance allocation problem, where we decide allocation of ambulances to bases. We present an incremental greedy approach to discover the placement of bases that maximises the service level of EMS. Using the properties of submodular optimisation we show that our greedy algorithm provides quality guaranteed solutions for one of the objectives employed in real EMSs. Furthermore, we validate our derived policy by employing a real-life event driven simulator that incorporates the real dynamics of EMS. Finally, we show the utility of our approaches on a real-world dataset from a large asian city and demonstrate significant improvement over the best known approaches from literature.

Keywords

Ambulances, Scheduling, Allocation problems, Direct impact, Emergency Medical system, Greedy algorithms, Greedy approaches, Quality guaranteed, Service levels

Discipline

Artificial Intelligence and Robotics | Computer Sciences | Medicine and Health Sciences | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 26th International Conference on Automated Planning and Scheduling (ICAPS 2016): London, June 12-17

First Page

385

Last Page

393

Publisher

AAAI Press

City or Country

Palo Alto, CA

Copyright Owner and License

LARC

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

https://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/13031

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