Emergencies such as the breakdown of an MRI-scanner or a domestic fire demand a timely response. This means that the resources required for addressing such incidents (spare parts and fire trucks, respectively) need to be stored in relative proximity of potential incidents and dispatched on short notice. This requires a network of resources in several storage locations. Owners of such Emergency Resource Networks (ERNs) face three issues: (i) Where should resources be stored, and how many resources need to be available at each location? (ii) How should resources be dispatched in response to an emergency? (iii) Can the performance of the system be improved by proactive relocation of resources? Answering (i)-(iii) is essential to achieve the required timely response against low costs. We construct the first general model for ERNs, unifying and extending results from disjoint application areas. In addition, we make the following contributions: 1. The rate at which emergencies occur varies over time, as a result of daily and seasonal effects. We investigate how to deal with this variability. 2. Questions (i)-(iii) have only been addressed sequentially: determine the storage locations, then the resource levels, finally the dispatching rules. This decoupling leads to relatively poor solutions. In this project, we provide a better integration of the decision-making process. 3. We implement a proof of concept planning tool, and test its efficiency at Philips Healthcare Service Parts Supply Chain, and Amsterdam/Amstelland Fire Brigade, using historical data and in close collaboration with these external partners.

The main purpose of ASSISTANCE project is twofold: On the one hand to help and protect different kind of first responders’ (FR) organizations that work together taking into account the type of disaster/crisis they are mitigating in each moment and on the other hand, to enhance their capabilities for facing complex situations providing them advanced training based on Virtual Reality (VR), Mixed Reality (MR) and Augmented Reality (AR), tailored to their real needs depending on the type of incident. ASSISTANCE project will use novel technologies such as; UAV, Robots, drones’ swarms and advanced training based on VR, MR and AR for increasing the FR’s situation awareness (SA) taking into account their need in terms of data (e.g. real time video, persons and objects location, evacuation routes status, ad-hoc network coverage and so on). Different types of adapted SA modules will be developed inside a common SA framework capable of offering the sensor outcome needed by each FR organization (e.g. real time video and resources location for firemen, evacuation routes status for emergency health services and so on). Regarding training, an advanced training network based on VR, MR, AR and other novel technologies and methodologies (e.g. tailored curricula, immersive interfaces, adapted training methodology definition, etc.) will be established in order to share different VR platforms and scenarios for enhancing the current training capabilities and skills of different FRs organization. All the ASSISTANCE results will be tested under controlled conditions in three different demostration pilots. Solutions will be developed in compliance with EU societal values, fundamental rights and applicable legislation, including in the area of privacy and personal data protection. Societal aspects (e.g. perception of security, possible effects of technological solutions on societal resilience, gender diversity) have to be taken into account in a comprehensive and thorough manner.