This project aims to contribute to the development of safe and trusted, artificially intelligent transportation in healthcare. The London Ambulance Service (LAS) operates more than 1100 ambulances to respond to medical emergencies in the London area. On an annual basis, LAS serves more than 1.2 million incidents. Effective ambulance positioning and allocation to incidents are critical decisions, while also accounting for the imprecise, annually increasing, and time-varying demand. Optimizing the incident response times with a clear human-trusted rationale behind the decision-making process poses a real challenge.
To this end, [Letsios et al. 2019, Liaskas 2020, Liu 2020] have developed discrete optimization methods for resource allocation problems arising in transportation companies, including LAS. These methods are used for descriptive analytics purposes, by identifying improvements in historically implemented solutions to these problems and providing useful, data-driven insights for future decisions. Typically, the allocation of ambulances to incidents is decided using the incidence criticality, location, and ambulance availability. Optimization software offers major benefits in transportation companies, but cannot be fully trusted by healthcare practitioners due to the lack of explainability. Argumentation-based models have been shown to be able to support decision-making in a transparent way. [Cyras et al. 2019, Cocarascu et al. 2020] proposed argumentation-based approaches that enable interaction with and generation of user explanations for decisions produced by optimization solvers and predictive analytics tools.
This project aims to enhance descriptive analytics with discrete optimization by developing robust integer optimization models and algorithms for ambulance management, taking into account the uncertainty and temporal nature of medical emergencies. It also aims to develop novel argumentation-based approaches for generating user explanations tailored to ambulance management.