Otimização de serviços de assistência social ao domicílio

The existing context in Home Social Services (HSS) organizations reveals a notable lack of decision support tools tailored to their specific needs, in contrast to the availability of route optimization tools in other sectors. While route optimization tools are excellent at improving processes, reducing costs, and increasing efficiency for organizations, they fall short in addressing the complex challenges unique to HSS planning. Unlike the vehicle routing problems faced by transportation companies, home care planning requires specialized approaches and tools that are currently lacking in commercial options. In HSS, the primary focus is on delivering quality care to clients in their homes, with special attention to factors such as continuity of care, caregiver skills, patient vulnerability, and adaptability to evolving service needs. Human factors are crucial in this field, as workers handle emotionally demanding situations while aiming to offer compassionate and efficient care. Emotional demands, work-life balance, and physical strain are key issues that affect the well-being, effectiveness of caregivers, increasing unplanned (long term and short term) absences. The lack of optimization tools to manage the contingencies of such absences leads organizations to rely on overtime, resulting in increased costs and staff strain. Balancing overtime costs with hiring new staff becomes a critical consideration, especially in the face of workforce management challenges posed by unplanned absences and increasing service demands. Moreover, HSS agencies often struggle with long waiting lists that prevent individuals from receiving needed services in a timely manner. Planning complexities arise because traditional approaches such as FIFO (First In, First Out) may not be appropriate for client selection in home care. Admitting new clients requires careful consideration to maintain continuity of care and preserve existing care patterns. The lack of tools to address these complexities further complicates the decision-making process within HSS. This work advances the state of the art by: 1. explicitly representing the multiple sources of uncertainty (demand and supply-side uncertainties), as well as travel time and service delivery uncertainties, to allow organizations to more efficiently deal with staffing shortages and unpredictability; 2. integrating decisions about the assignment of professionals to patients, taking into account continuity of care, appropriate time windows for care delivery, and the choice of schedules for professionals, and the impact of these assignments on caregivers' well-being and on work/life balance; 3. taking into account the appropriate match between the skills of the professionals and the skills required by the services being provided; 4. integrating decisions that take into account the well-being of the caregivers; 5. considering decisions about whether or not to accept new patients to be cared for and which patients to accept; and 6. Integrating all of these features into a prototype DSS to support HSS planning.