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A Bi-Objective Home Health Care Routing and Scheduling Problem under Uncertainty.

Jiao Zhao¹, Tao Wang², Thibaud Monteiro¹

¹INSA Lyon, Université Lumière Lyon 2, Université Claude Bernard Lyon 1, Université Jean Monnet Saint-Etienne, DISP UR4570, 69621 Villeurbanne, France.

International Journal of Environmental Research and Public Health

|March 28, 2024

Summary

This study introduces an optimization model for home health care routing and scheduling, addressing uncertain service times to improve efficiency and patient satisfaction. The proposed adaptive large neighborhood search method effectively manages operational costs and caregiver workloads.

Keywords:

adaptive large neighborhood search bi-objective optimization home health care routing and scheduling problems uncertainty

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Area of Science:

Operations Research
Healthcare Management
Applied Mathematics

Background:

Home health care demand is rising, necessitating efficient operational cost management.
Uncertain patient service times in home care lead to delays and reduced service quality.
Balancing patient needs, caregiver workload, and operational costs is crucial.

Purpose of the Study:

To develop a bi-objective optimization model for home health care routing and scheduling under uncertainty.
To incorporate caregiver qualifications and workload into the optimization problem.
To propose an efficient algorithm for solving the complex routing and scheduling challenges.

Main Methods:

A mixed-integer linear programming formulation was developed for the optimization problem.
An Adaptive Large Neighborhood Search embedded in an Enhanced Multi-Directional Local Search (ALNS-EMDLS) framework was proposed.
A stochastic version of ALNS-EMDLS was introduced to handle uncertain service times.

Main Results:

The proposed ALNS-EMDLS method demonstrated efficiency in evaluating Pareto fronts for the bi-objective problem.
Sensitivity analysis confirmed the robustness of the developed model and algorithmic approach.
The method was successfully applied to a real-life home health care case.

Conclusions:

The developed optimization model and ALNS-EMDLS algorithm effectively address routing and scheduling under uncertainty in home health care.
The findings provide a robust framework for improving home health care operations and service quality.
Managerial recommendations are offered based on the real-life case application.