In this study, security and safety in rural parts of Sweden are investigated. New ways of organizing for efficient response can be found in the extended collaboration between societal sectors and in the utilization of local social capital. New categories of first responders and their requirements are identified and technical and non-technical solutions as support are proposed. The solutions include e.g. mobile applications and a technical infrastructure making it possible for volunteers to obtain information about events requiring emergency response. Emergency management in rural areas shows several similarities to large-scale crises, e.g. in terms of insufficient infrastructure available and the need to use local resources in the immediate aftermath of the event. Therefore, the results of the study can be transferable to large-scale crises.

Providing Emergency Medical Services (EMS) is a key function of society. To achieve high quality EMS, planning is of vital importance. An important strategic and tactical problem is the location of ambulance stations and the allocation of ambulances to these stations. This paper presents a new mixed integer model for this problem especially suitable for regions with heterogeneous demand and multiple performance measures. The model decides on the location/allocation of stations/ambulances, calculates the service and arrival rates for each station and the probabilities that a call is served by a particular station. The model is tested on a combined urban and rural area in Norway with multiple performance measures. Compared with the current solution for the area, the best solution from the model has a higher expected performance on each of the performance measures used.

This paper introduces the concept of airport logistics, along with a proof of concept through a case study where we model and optimize de-icing services at Stockholm Arlanda airport. The optimized schedule is tested using a simulation model of the turn-around process. The results demonstrate that a schedule taking into account the overall airport performance results in less delay than the schedule targeting solely the performance objective of the de-icing company.

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Most delays in the air transport occur at the airport. A particular reason is the complexity of managing the large number of supporting flows in airport logistics. We consider the optimisation problem of scheduling de-icing vehicles that is one of the key supporting logistic flows in the turn-around process of aircraft. The objective is to minimize the delay of flights due to de-icing, and the travel distance of the de-icing vehicles. We study the complexity of the problem, and develop a solution algorithm using greedy randomized adaptive search. A case study of real-life data from Stockholm Arlanda Airport shows that optimised schedule leads to significantly better performance in comparison to intuitive and simple scheduling strategies. The benefit of optimisation in reducing the waiting time for de-icing is further demonstrated via dynamic simulations.