This paper presents a real-world optimization problem in home health care that is solved on a daily basis. It can be described as follows: care staff members with different qualification levels have to visit certain clients at least once per day. Assignment constraints and hard time windows at the clients have to be observed. The staff members have a maximum working time and their workday can be separated into two shifts. A mandatory break that can also be partitioned needs to be scheduled if the consecutive working time exceeds a certain threshold. The objective is to minimize the total travel-and waiting times of the care staff. Additionally, factors influencing the satisfaction of the clients or the care staff are considered. Most of the care staff members from the Austrian Red Cross (ARC) in Vienna use a combination of public transport modes (bus, tram, train, and metro) and walking. We present a novel model formulation for this problem, followed by an efficient exact solution approach to compute the time-dependent travel times out of the timetables from public transport service providers on a minute-basis. These travel time matrices are then used as input for three Tabu Search based solution methods for the scheduling problem. Extensive numerical studies with real-world data from the ARC show that the current planning can be improved significantly when these methods are applied.

PurposeThe number of care‐dependent people will rise in future. Therefore, it is important to support home health care (HHC) providers with suitable methods and information, especially in times of disasters. The purpose of this paper is to reveal potential threats that influence HHC and propose an option to incorporate these threats into the planning and scheduling of HHC services.Design/methodology/approachThis paper reveals the different conditions and potential threats for HHC in rural and urban areas. Additionally, the authors made a disaster vulnerability analysis, based on literature research and the experience of the Austrian Red Cross (ARC), one of the leading HHC providers in Austria. An optimization approach is applied for rural HHC that also improves the satisfaction levels of clients and nurses. A numerical study with real life data shows the impacts of different flood scenarios.FindingsIt can be concluded that HHC service providers will be faced with two challenges in the future: an increased organizational effort and the need for an anticipatory risk management. Hence, the development and use of powerful decision support systems are necessary.Research limitations/implicationsFor an application in urban regions new methods have to be developed due to the use of different modes of transport by the nurses. Additionally, an extension of the planning horizon and triage rules will be part of future research.Practical implicationsThe presented information on developments and potential threats for HHC are very useful for service providers. The introduced software prototype has proven to be a good choice to optimize and secure HHC; it is going to be tested in the daily business of the ARC.Social implicationsEven in the case of disasters, HHC services must be sustained to avoid health implications. This paper makes a contribution to securing HHC, also with respect to future demographic trends.Originality/valueTo the best of the authors’ knowledge there are no comprehensive studies that focus on disaster management in the field of HHC. Additionally, the combination with optimization techniques provides useful insights for decision makers in that area.

This paper considers the problem of supporting immediate response operations after a disaster with information about the available road network to reach certain locations. We propose an online algorithm that aims to minimize the route length required by an unmanned aerial vehicle (UAV) to explore the road accessibility of potential victim locations. It is assumed that no information about disruptions in the road network is available at the start of the exploration. The online algorithm applies two movement and three orientation strategies. Additionally, a cutting strategy is used to restrict the search space after new information about the state of single roads is obtained. We consider a road and an aerial network for the movements of the UAV, since it is not necessary to follow the route of a road any longer, if it can be marked as disrupted. In extensive numerical studies with artificial and real-world test instances, it is evaluated for different disruption levels, which combinations of movement and orientation strategies perform best. Additionally, we propose different refuelling strategies for the UAV and present how they differ in the number of refuelling operations and the required additional route length. The results show that an efficient online algorithm can save valuable exploration time.

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Home health care (HHC) services are of vital importance for the health care system of many countries. Further increases in their demand must be expected and with it grows the need to sustain these services in times of disasters. Existing risk assessment tools and guides support HHC service providers to secure their services. However, they do not provide insights on interdependencies of complex systems like HHC. Causal-Loop-Diagrams (CLDs) are generated to visualize the impacts of epidemics, blackouts, heatwaves, and floods on the HHC system. CLDs help to understand the system design as well as cascading effects. Additionally, they simplify the process of identifying points of action in order to mitigate the impacts of disasters. In a case study, the course of the COVID-19 pandemic and its effects on HHC in Austria in spring 2020 are shown. A decision support system (DSS) to support the daily scheduling of HHC nurses is presented and applied to numerically analyze the impacts of the COVID-19 pandemic, using real-world data from a HHC service provider in Vienna. The DSS is based on a Tabu Search metaheuristic that specifically aims to deal with the peculiarities of urban regions. Various transport modes are considered, including time-dependent public transport.