TY - JOUR
T1 - Simheuristic and learnheuristic algorithms for the temporary-facility location and queuing problem during population treatment or testing events
AU - Bayliss, Christopher
AU - Panadero, Javier
N1 - Funding Information:
This work has been partially supported by the Erasmus+ programme (2019-I-ES01-KA103-062602) and by the Spanish Ministry of Science, Innovation, and Universities (RED2018-102642-T).
Publisher Copyright:
© 2023 The Operational Research Society.
PY - 2023/1/24
Y1 - 2023/1/24
N2 - Epidemic outbreaks, such as the one generated by the coronavirus disease, have raised the need for more efficient healthcare logistics. One of the challenges that many governments have to face in such scenarios is the deployment of temporary medical facilities across a region with the purpose of providing medical services to their citizens. This work tackles this temporary-facility location and queuing problem with the goals of minimising costs, the expected completion time, population travel time, and waiting time. The completion time for a facility depends on the numbers assigned to those facilities as well as stochastic arrival times. This work proposes a learnheuristic algorithm to solve the facility location and population assignment problem. Firstly a machine learning algorithm is trained using data from a queuing model (simulation module). The learnheuristic then constructs solutions using the machine learning algorithm to rapidly evaluate decisions in terms of facility completion and population waiting times. The efficiency and quality of the algorithm is demonstrated by comparison with exact and simulation-only (simheuristic) methodologies. A series of experiments are performed which explore the trade-offs between solution cost, completion time, population travel time, and waiting time.
AB - Epidemic outbreaks, such as the one generated by the coronavirus disease, have raised the need for more efficient healthcare logistics. One of the challenges that many governments have to face in such scenarios is the deployment of temporary medical facilities across a region with the purpose of providing medical services to their citizens. This work tackles this temporary-facility location and queuing problem with the goals of minimising costs, the expected completion time, population travel time, and waiting time. The completion time for a facility depends on the numbers assigned to those facilities as well as stochastic arrival times. This work proposes a learnheuristic algorithm to solve the facility location and population assignment problem. Firstly a machine learning algorithm is trained using data from a queuing model (simulation module). The learnheuristic then constructs solutions using the machine learning algorithm to rapidly evaluate decisions in terms of facility completion and population waiting times. The efficiency and quality of the algorithm is demonstrated by comparison with exact and simulation-only (simheuristic) methodologies. A series of experiments are performed which explore the trade-offs between solution cost, completion time, population travel time, and waiting time.
KW - Facilities planning and design
KW - Machine learning
KW - Queuing
KW - sim-learnheuristics
KW - Simulation
UR - http://www.scopus.com/inward/record.url?scp=85147220434&partnerID=8YFLogxK
U2 - 10.1080/17477778.2023.2166879
DO - 10.1080/17477778.2023.2166879
M3 - Article
AN - SCOPUS:85147220434
SN - 1747-7778
JO - Journal of Simulation
JF - Journal of Simulation
ER -