Optimizing home health care: A decision support system for districting and fleet management in urban health services

Abstract Mitigating the environmental impacts of the transportation sector involves a multifaceted approach that includes improving vehicle energy efficiency, promoting alternative fuels, and optimizing routes and transport operations. In this scenario of multiple impacts, road transportation stands out as a major contributor, responsible for the emission of numerous atmospheric pollutants and high energy consumption, being related to virtually all human activities. Home health care (HHC) has emerged as a strategic solution to reduce hospitalization costs while maintaining or enhancing healthcare service quality. However, HHC presents complex logistical challenges, including districting and fleet management, that require careful planning and optimization. This study addresses a significant gap in the literature by developing a decision support system (DSS) that integrates districting and fleet management for HHC. Unlike previous studies, which often focused on these problems individually, our DSS offers a holistic solution to optimize HHC operations. Both problems are formulated using multi-objective optimization and solved via the augmented weighted Tchebycheff method. The DSS was applied to a Brazilian city, resulting in significant improvements in districting and fleet management. The districting model generated more efficient plans than the current solution, reducing travel distance by 17% and workload deviation by 77% while maintaining district contiguity. The fleet management model optimized vehicle composition, incorporating hybrid and electric vehicles to reduce CO 2 emissions and operational costs. This research contributes to the field of HHC logistics by providing a practical tool to enhance service efficiency, reduce costs, and minimize environmental impact.