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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.

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The planning of home health care services is still done manually in many industrial countries. However, efficient decision support is necessary to improve the working plans and relieve the nurses from this time consuming task. The problem can be summarized as follows: clients need to be visited one or several times during the week by appropriately skilled nurses; their treatments have predefined time windows. Additionally, working time requirements for the nurses such as breaks, maximum working time per day, and daily as well as weekly rest times have to be considered. We propose a Branch-Price-and-Cut solution approach to solve this problem exactly, using the solutions of a variable neighborhood search solution approach as upper bounds. The algorithm is capable of solving to optimality real-life based test instances with up to nine nurses, 45 clients, and 203 visits during the week.

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.

In this paper, we consider a vehicle routing problem in which a fleet of homogeneous vehicles, initially located at a depot, has to satisfy customers' demands in a two‐echelon network: first, the vehicles have to visit intermediate nodes (e.g., a retail center or a consolidation center), where they deliver raw materials or bulk products and collect a number of processed items requested by the customers in their route; then, the vehicles proceed to complete their assigned routes, thus delivering the processed items to the final customers before returning to the depot. During this stage, vehicles might visit other intermediate nodes for reloading new items. In some real‐life scenarios, this problem needs to be solved in just a few seconds or even milliseconds, which leads to the concept of “agile optimization.” This might be the case in some rescue operations using drones in humanitarian logistics, where every second can be decisive to save lives. In order to deal with this real‐time two‐echelon vehicle routing problem with pickup and delivery, an original constructive heuristic is proposed. This heuristic is able to provide a feasible and reasonably good solution in just a few milliseconds. The constructive heuristic is extended into a biased‐randomized algorithm using a skewed probability distribution to modify its greedy behavior. This way, parallel runs of the algorithm are able to generate even better results without violating the real‐time constraint. Results show that the proposed methodology generates competitive results in milliseconds, being able to outperform other heuristics from the literature.