A Decision Support Tool for the Location Routing Problem During the COVID-19 Outbreak in Colombia

EasyChair Preprint 4062

Abstract

During the outbreak of coronavirus disease 2019 (COVID-19) in Bogotá, Colombia, some strategies for dealing with the increasing number of infected people and the level of occupation of intensive care units include the use of Personal Protective Equipment (PPE). PPE is a crucial component for patient care and a priority for protecting healthcare workers. For attending this necessity, the location of distribution centers within the city and the corresponding routes to supply the intensive care units (ICU) with PPE have an important role. Formally, this problem is defined as the Location Routing Problem (LRP). The LRP is an NP-Hard problem that combines the Facility Location Problem (FLP) and the Vehicle Routing Problem with Multiple Depots (MDVRP). This work presents a decision support tool based in a hybrid method consisting of an Iterated Local Search (ILS) algorithm combined with Monte Carlo simulation to deal with the LRP with uncertain demands. Realistic data from Bogotá (Colombia) was retrieved using Google Maps to characterize the geographical distribution of both potential facilities and ICUs, while demands were generated using the uniform probability distribution. Our preliminary results suggest the competitiveness of the algorithm in both the deterministic and the stochastic versions of the LRP.

Keyphrases: COVID-19, Capacitated location routing problem, Healthcare Logistics, ILS, Monte Carlo simulation, decision support tool, location routing

@booklet{EasyChair:4062,
  author    = {Andres Felipe Martinez Reyes and Carlos Quintero Araujo and Elyn Lizeth Solano Charris},
  title     = {A Decision Support Tool for the Location Routing Problem During the COVID-19 Outbreak in Colombia},
  howpublished = {EasyChair Preprint 4062},
  year      = {EasyChair, 2020}}