A hybrid evolutionary approach for multi-objective optimization in emergency blood transportation: A case study in Ho Chi Minh City

Abstract

This study presents a multi-objective approach to support decision-making in blood transportation. Since emergency blood demands are life-threatening for patients, it is crucial to have a flexible and responsive solution for blood product delivery to avoid blood stockouts in hospitals. Furthermore, reducing carbon emissions is essential in Vietnam, where internal combustion engine vehicles are mainly utilized. Thus, the use of swappable battery motorcycles as an alternative promotes environmental sustainability while addressing the charging time issue of plug-in electric vehicles. In the context of emergency blood transportation, the two objectives of our EVRP-BSS-TW problem are: (1) minimizing costs, including travel distance and the number of vehicles, and (2) minimizing the total hospital blood demand waiting time across public hospitals. To deal with these challenges, we propose two hybrid algorithms: Hybrid NSGA-II and Hybrid SPEA-2. Computational experiments showed that Hybrid NSGA-II outperforms the other algorithm, achieving optimal solutions for medium-sized instances. However, for larger instances, the proposed hybrid MOEAs fail to meet the execution time requirements.