## 1 Introduction  
Thanks to scientific developments and concurrent improvements in healthcare, hygiene, nutrition, and social conditions, the life expectancy of humans has consistently increased in the developed world over the last century, coupled with a steadily declining birth rate over the course of recent decades, has resulted in an increasingly aging population. In 2020, 20% of the French population was over 60 years old in France, and according to the _Institut National de la Statistique et des Etudes Economiques_, this will rise to 30% by 2070 [1]. In addition, there is a desire to reduce the demand for medical structures (e.g., hospitals), exerting pressure on people to return home as quickly as possible. As a result, the demand for home health care services has increased sharply in recent years, as well as the number of studies on this topic.  
In the scientific literature, the management of home care routes is known as the Home Health Care Routing and Scheduling Problem (HHCRSP) and has been studied for fifty years [2]. The goal is to find the best possible routes according to the characteristics of the problem, e.g., strategies to minimize travel times, maximize the stakeholders' satisfaction, take time windows and difficulty of care visit provisions into consideration, etc. Although the variability of HHCRSP systems is increasingly under scrutiny, most studies are carried out in a deterministic environment. For the past five years, an ever-growing number of publications have considered uncertainties, especially for travel times and visit times. Robust approaches build efficient solutions as long as the uncertainties are bound within a defined interval [3], whereas resilient approaches propose policies to recover after a disruption. In the vehicle routing literature, stochastic routing problems are often tackled with robust approaches, such as chance-constrained programming (CCP), or resilient approaches, such as stochastic programming with recourse (SPR). [PERSON] et al. [4] argue that the objective functions in SPRs are more relevant than CCPs in solving a stochastic VRP. Moreover, a typical concern with robust approaches is that they can be too conservative and thus too far from optimality for the nominal problem [5]. That is why robust solutions are effective against small variations but become useless against strong disruptions; it is then necessary to turn to resilient approaches. The study of resilient approaches to counter high-impact disruptions remains one of the gaps to be filled in the spectrum of HHCRSP publications. In addition, it is difficult to find a clear, realistic, and readily available use case in the scientific literature.  
The aim of this paper is to analyze and compare the performance of resilient approaches applied to a use case close to reality, open to the scientific community, and inspired by home health care centers in the department of Tarn (France) in an environment subject to both variations and high-impact disruptions. Two different approaches are analyzed and compared to a baseline approach using discrete event simulations, including an empirical approach modeling what is done today in the home health care centers interviewed and a new approach based on the centralization and sharing of information between caregivers to improve local decision-making. The two main contributions of the article are (1) creating a realistic case study based on home health care center interviews and making it available to the scientific community and (2) proposing a resilient approach based on the centralization and sharing of information for improving local decision-making when routes are disrupted.  
This paper is organized as follows: Section 2 is dedicated to the review of literature on publications related to the topic. Then, Section 3 describes the use case and the design of experiments (resilient approaches and disruptions). Section 4 presents the results and analysis. Finally, Section 5 concludes and proposes avenues for further research.