## 1 Introduction  
In most countries, the population of older adults is increasing. As people age, their health needs become more chronic and complex [1], which results in a rising demand for health care [2]. According to the Organisation for Economic Co-operation and Development (OECD), between 2010 and 2018, all developed countries registered an increase of 3% in their older population [3]. Furthermore, for most of those countries, a decrease in formal care leads to a substantial increase in demand for Home Health Care (HHC) services. In recent years, the Health Care (HC) industry has become one of the largest sectors of the economy in developed countries. In Europe, between 1% and 5% of the total public health budget is spent on HHC services [4]. In 2018, one-point-four million Canadians needed home care services [5] (4.1% of the total population). HHC services have emerged as a viable solution to meet the challenges of the aging population problem. HHC's aim is to improve patients' living conditions by providing them with medical, paramedical, and social services in their home. From an economic point of view, HHC decreases hospital congestion by avoiding or reducing hospitalizations, leading to both significant increases in quality of life for patients and relevant cost savings in the HC system [6, 7]. Building a proper HHC plan is a complex task regarding the different constraints. Currently, operational planning is mostly done manually and often by an experienced senior caregiver [5, 8, 9, 10]. According to the surveys of the HHC companies, each day, an HHC company carries out various logistics activities including the delivery of drugs or medical services from caregivers to patients and medical samples to the laboratory. Generally speaking, those activities can be considered as a Vehicle Routing Problem (VRP) or as a scheduling problem [11]. In both views, multiple constraints must be considered like workload constraints, interdependencies among services, etc. Interdependent services can be defined as having temporal dependencies between two services, or as synchronized services [12] (also called shared services), or as services with priority constraints. Thus, it is possible for a single patient to need a pair of services that must be separated from a given time interval. The HHC problem can be described regarding a short-, medium-, or long-term horizon, ranging from one day to a couple of months. Daily problems have received the most attention in the literature [13; 14]. Usually, medium-term problems consider a planning horizon of a week. Another observation is that all constraints mentioned above are rarely met in the same problem. A set of constraints is chosen to reflect the scope of how the problem is treated. According to the review of [PERSON] and [PERSON] [13], short-term problems usually consider time windows and qualification requirements, whereas medium-term problems deal with working time regulations and continuity of care considerations. However, inter-dependent and synchronized services are weakly discussed in the HHC literature. Finally, to establish an exhaustive HHC problem, many other constraints and aspects should be taken into account such as geographical constraints or legal working considerations. The problem holds many constraints and parameters depending on the way it is handled. Due to medical confidentiality, researchers lack public benchmarks to validate and compare their models. Indeed, in the current literature, many studies cannot publish their datasets due to medical confidentiality. Some studies offer homemade datasets, but they are often developed to fit into the scope of the HHCRSP. These homemade datasets can rarely be used by other researchers. This article proposes a generator able to establish HHC models fitting most of the constraints and features linked to the HHC literature.