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

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.