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A Vehicle Routing Problem with Time Windows and Workload Balancing for COVID-19 Testers: A Case Study.

S Shahnejat-Bushehri¹, A Kermani¹, O Arslan¹

¹Department of Logistics and Operations Management, HEC Montréal, 3000 Chemin de la Côte-Sainte-Catherine, Montréal, Canada.

Ifac-Papersonline

|April 15, 2024

Summary

This summary is machine-generated.

Laboratories can optimize in-home testing delivery by balancing tester workloads. This study presents a vehicle routing model to improve lab assignment and routing policies, reducing costs and meeting high demand.

Keywords:

Adaptive Large Neighborhood Search COVID-19 Home Health Care Metaheuristic VRP with Time Windows VRP with Workload Balancing Vehicle Routing Problem

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Area of Science:

Operations Research
Healthcare Logistics
Applied Mathematics

Background:

The COVID-19 pandemic significantly increased demand for in-home laboratory testing services.
Laboratories require efficient strategies to manage testers and operational costs amidst surging demand.

Purpose of the Study:

To develop an optimization model for laboratory assignment and routing policies for in-home testing.
To incorporate tester workload balancing into the vehicle routing problem with time windows.

Main Methods:

A mixed-integer programming model was formulated and solved using CPLEX for smaller problem instances.
An adaptive large neighborhood search algorithm was implemented for larger, real-world instances.
The model was validated against a medical lab's operational data over six consecutive days.

Main Results:

The optimization model demonstrated improvements in assignment and routing efficiency.
Workload balancing was effectively integrated, leading to more equitable tester assignments.
The proposed methods provided a viable solution for managing high-volume in-home testing logistics.

Conclusions:

The study provides a practical optimization framework for laboratories managing in-home testing services.
Implementing workload balancing enhances operational efficiency and potentially reduces costs.
The findings offer valuable insights for improving healthcare logistics and service delivery.