Journal
PROCEEDINGS OF THE 2023 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, GECCO 2023
Volume -, Issue -, Pages 1409-1417Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3583131.3590434
Keywords
Emergency Medical Dispatch; Genetic Programming; Hyper-Heuristic; Simulation; Dynamic Optimisation
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The task of emergency medical response in modern municipalities is valuable. To ensure minimal response times, resource allocation and maximizing ambulance coverage can be done by human experts or automation.
Of great value to modern municipalities is the task of emergency medical response in the community. Resource allocation is vital to ensure minimal response times, which we may perform via human experts or automate by maximising ambulance coverage. To combat black-box modelling, we propose a modularised Genetic Programming Hyper Heuristic framework to learn the five key decisions of Emergency Medical Dispatch (EMD) within a reactive decision-making process. We minimise the representational distance between our work and reality by working with our local ambulance service to design a set of heuristics approximating their current decision-making processes and a set of synthetic datasets influenced by existing patterns in practice. Through our modularised framework, we learn each decision independently to identify those most valuable to EMD and learn all five decisions simultaneously, improving performance by 69% on the largest novel dataset. We analyse the decision-making logic behind several learned rules to further improve our understanding of EMD. For example, we find that emergency urgency is not necessarily considered when dispatching idle ambulances in favour of maximising fleet availability.
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