A Two-Stage Location Problem with Order Solved Using a Lagrangian Algorithm and Stochastic Programming for a...

Area of Science:

• Operations Research
• Healthcare Management
• Computational Optimization

Background:

• Strategic healthcare service center location is critical, especially during the COVID-19 pandemic.
• Patient preferences for specific vaccine brands/laboratories complicate optimal facility siting.
• The simple plant/center location problem with order (SPLPO) is less explored than the basic SPLP.

Purpose of the Study:

• To propose a novel two-stage stochastic formulation for the SPLPO (2S-SPLPO) applicable to pandemic vaccination scenarios.
• To address the complexity introduced by patient preference orders in facility location.
• To evaluate the performance of the new formulation using computational experiments.

Main Methods:

• Developed a two-stage stochastic programming model (2S-SPLPO) treating patient preference order as a random vector.
• Employed an algorithm based on Lagrangian relaxation, known for efficiency in large SPLPO instances.
• Proposed algorithms for data warehousing and sensor-based instance generation for 2S-SPLPO.

Main Results:

• The 2S-SPLPO formulation effectively models the complexity of patient preferences in healthcare facility location.
• Lagrangian relaxation-based algorithm demonstrated efficiency in handling simulated 2S-SPLPO instances.
• The study explored potential applications in optimizing COVID-19 vaccination strategies.

Conclusions:

• The proposed 2S-SPLPO offers a robust framework for optimizing healthcare service center placement considering patient preferences.
• The computational approach provides a scalable solution for complex location problems.
• This research contributes to improving public health logistics and response strategies during health crises.