Multi-objective home health care routing: a variable neighborhood search method

Area of Science:

• Operations Research
• Healthcare Management
• Computer Science

Background:

• Home Health Care (HHC) services are crucial for patient convenience and societal well-being.
• Manual nurse planning in HHC leads to inefficiencies in time, cost, and overall productivity.
• There is a need for advanced planning models to optimize HHC operations.

Purpose of the Study:

• To develop a multi-objective mixed-integer programming model for optimizing Home Health Care (HHC) planning.
• To balance financial goals with service quality, environmental impact, and workload distribution.
• To address patient preferences for service levels and consider diverse staff qualifications and vehicle types.

Main Methods:

• A multi-objective mixed-integer model was formulated for HHC planning.
• The epsilon-constraint method was used with CPLEX for small-scale problem instances.
• A Multi-Objective Variable Neighborhood Search (MOVNS) algorithm with nine neighborhood moves was developed for large-scale instances.

Main Results:

• The MOVNS algorithm demonstrated effectiveness in solving practical-sized HHC planning problems.
• Sensitivity analysis highlighted the strengths and weaknesses of the proposed MOVNS algorithm.
• The model's applicability was validated using a real-world case study with actual data.

Conclusions:

• The proposed multi-objective model provides a robust framework for efficient and high-quality Home Health Care (HHC) planning.
• The MOVNS algorithm offers a scalable solution for optimizing complex HHC logistics.
• This research contributes to improving the operational efficiency and service delivery in the HHC sector.