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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.
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This articles provides a short summary of the research topics and latest research results of the European Working Group "Operations Research Applied to Health Services" (ORAHS) organized as an e-conference in Juli 2020 at the University of Vienna, Austria (https://orahs2020.univie.ac.at/). Furthermore, challenges for OR in health care including application areas, decision support systems, general trends, and modelling techniques are briefly illustrated from an European and international perspective by providing selected essential literature reviews.
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In-situ thermoforming and overmolding of continuous fiber-reinforced thermoplastic composites by hybrid injection molding enables the mass production of thermoplastic lightweight structures with a complex geometry. In this study, the anisotropic mechanical behavior of such hybrid injection molded short and continuous fiber-reinforced thermoplastics and the numerical simulation of the resulting mechanical properties under flexural loading were investigated. For this, the influence of the volume flow rate between 25 and 100 cm3/s during injection molding of a PP/GF30 short fiber-reinforced overmolding material was studied and showed a strong effect on the fiber orientation but not on the fiber length, as investigated by computer tomography and fiber length analysis. Thus, the resulting anisotropies of the stiffness and strength as well as the strain hardening investigated by tensile testing were considered when the mechanical behavior of a hybrid test structure of short and continuous fiber-reinforced thermoplastic composites was predicted by numerical simulations. For this, a PP/GF60 and PP/GF30 hybrid injection molded test structure was investigated by a numerical workflow with implemented injection molding simulation data. In result, the prediction of the mechanical behavior of the hybrid test structure under flexural loading by numerical simulation was significantly improved, leading to a reduction of the deviation of the numerically predicted and experimentally measured flexural strength from 21% to 9% in comparison to the isotropic material model without the implementation of the injection molding data.
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BACKGROUND: Efficient transport of non-emergency patients is crucial for ambulance service providers to cope with increased demand resulting from aging Western societies. This paper deals with the optimization of the patient transport operations of the Red Cross of Lower Austria, which is the main provider in this state. Different quality levels of the provided service - expressed by time windows, feasible maximum ride times and exclusive transports - are tested and analyzed on real-life instances to show daily impacts on the provider's resources. Comparisons of the developed solution approach to the recorded manual schedule prove its advantages. In contrast to previous work in this field, non-static service times that depend on the combination of patients, their transport mode, the vehicle type as well as the pickup or delivery locations are used. These service times are based on statistical analyses that have been performed on an anonymized dataset with more than 600,000 requests. METHODS: To solve the given problem, a matheuristic solution approach was developed that deals with the exact optimization of combinations of requests as a first stage. Subsequently, the identified combinations are used as an input into a Tabu Search strategy, where the vehicle routing is optimized. Three representative days of the year 2012 were chosen for the four regions of Lower Austria to test five different service levels and the quality of the solution method. RESULTS: For the standard scenario, the operation time of the manual schedule is reduced in the range from 14.1 % to 19.8 % for all tested instances. Even in the best service scenario, the matheuristic computes better results than the manual schedule. The service level has a high impact on the operation time of providers. The relative savings that are achieved by the algorithm are significantly lowered by introducing higher quality standards. The main reason is that less feasible combinations of patients can be generated. This leads to diminished opportunities for patients to be transported at the same time. The results indicate that the implementation of the developed matheuristic in daily planning decisions could decrease operation times significantly. CONCLUSIONS: Managers have to define minimum standards for the punctuality, exclusive transports and excess ride times. This is crucial in order to find a suitable compromise between the service level and an optimized resource management.