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Districting Decisions in Home Health Care Services: Modeling and Case Study

  • Sebastian CortésEmail author
  • Elena Valentina Gutiérrez
  • Juan D. Palacio
  • Juan G. Villegas
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 916)

Abstract

Home health care (HHC) services are a growing segment in the global health care industry in which patients receive coordinated medical care at their homes. When designing the service, HHC providers face a set of logistics decisions that include the districting configuration of the coverage area. In HHC, the districting problem seeks to group small geographic basic units-BUs (i.e., city quarters) into districts with balanced workloads. In this work, we present a modeling approach for the problem that includes a mixed integer linear programming (MILP) formulation and a greedy randomized adaptive search procedure (GRASP). The MILP formulation solves instances up to 44 BUs, while the GRASP allows to solve instances up to 484 BUs in less than 2.52 min. Computational experiments performed with a set of real instances from a Colombian HHC provider, show that the GRASP can reduce workload imbalances in a 57%.

Keywords

Home health care Districting Mixed integer linear programming Greedy randomized adaptive search procedure 

Notes

Acknowledgments

The authors are grateful to Universidad of Antioquia, specifically to the Vicerrectoría de Investigación, for their partial funding in the research project PRV16-1-03. We also thank the IPS Universitaria and their HHC program (PAD) for providing the information used in this work.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Sebastian Cortés
    • 1
    Email author
  • Elena Valentina Gutiérrez
    • 1
  • Juan D. Palacio
    • 2
  • Juan G. Villegas
    • 1
  1. 1.Departamento de Ingeniería Industrial, Facultad de IngenieríaUniversidad de AntioquiaMedellínColombia
  2. 2.Departamento de Ciencias Matemáticas, Escuela de CienciasUniversidad EAFITMedellínColombia

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