Multiperiod stochastic optimization with machine learning for dynamic home healthcare routing

Home healthcare (HHC) is gaining popularity as a means of alleviating pressure on healthcare systems and improving patient well-being. However, effective HHC operations require optimized resource utilization, a challenge complicated by inherent uncertainty and dynamism. Although deterministic variants of the home healthcare routing and scheduling problem have been extensively studied, this is not the case of stochastic and dynamic versions. Aiming to address this gap, our work analyzes the problem of a home healthcare provider (HHCP) making daily decisions regarding the acceptance of new patient requests that arrive dynamically over the planning horizon. The HHCP also has to assign the first visit of each accepted patient to a day. It is assumed that every patient needs at least one visit and a specific periodicity of care, but the total number of visits that a patient will effectively require is stochastic. However, the HHCP must commit to serving accepted patients for their total number of visits. Moreover, the HHCP creates routes for the available caregivers in order to cover the visits planned for the current day. The problem is modeled as a Markov decision process. We will outline a solution approach that integrates machine learning techniques with traditional combinatorial optimization algorithms. This hybrid approach aims to effectively tackle the stochasticity inherent in the patients arrival process and care duration.