Simheuristic and learnheuristic algorithms for the temporary-facility location and queuing problem during population treatment or testing events

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. © 2023 The Operational Research Society.

Agile Computational Intelligence for Supporting Hospital Logistics During the COVID-19 Crisis

This chapter describes a case study regarding the use of ‘agile’ computational intelligence for supporting logistics in Barcelona’s hospitals during the COVID-19 crisis in 2020. Due to the lack of sanitary protection equipment, hundreds of volunteers, the so-called “Coronavirus Makers” community, used their home 3D printers to produce sanitary components, such as face covers and masks, which protect doctors, nurses, patients, and other civil servants from the virus. However, an important challenge arose: how to organize the daily collection of these items from individual homes, so they could be transported to the assembling centers and, later, distributed to the different hospitals in the area. For over one month, we have designed daily routing plans to pick up the maximum number of items in a limited time—thus reducing the drivers’ exposure to the virus. Since the problem characteristics were different each day, a series of computational intelligence algorithms was employed. Most of them included flexible heuristic-based approaches and biased-randomized algorithms, which were capable of generating, in a few minutes, feasible and high-quality solutions to quite complex and realistic optimization problems. This chapter describes the process of adapting several of our ‘heavy’ route-optimization algorithms from the scientific literature into ‘agile’ ones, which were able to cope with the dynamic daily conditions of real-life routing problems. Moreover, it also discusses some of the computational aspects of the employed algorithms along with several computational experiments and presents a series of best practices that we were able to learn during this intensive experience. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.