[en] The simulation-based and computationally expensive problem tackled in this paper addresses COVID-19 vaccines allocation in Malaysia. The multi-objective formulation considers simultaneously the total number of deaths, peak hospital occupancy and relaxation of mobility restrictions. Evolutionary algorithms have proven their capability to handle multi-to-many objectives but require a high number of computationally expensive simulations. The available techniques to raise the challenge rely on the joint use of surrogate-assisted optimization and parallel computing to deal with computational expensiveness. On the one hand, the simulation software is imitated by a cheap-to-evaluate surrogate model. On the other hand, multiple candidates are simultaneously assessed via multiple processing cores. In this study, we compare the performance of recently proposed surrogate-free and surrogate-based parallel multi-objective algorithms through the application to the COVID-19 vaccine distribution problem.
Disciplines :
Computer science
Author, co-author :
Briffoteaux, Guillaume ; Université de Mons - UMONS > Faculté Polytechnique > Service de Mathématique et Recherche opérationnelle ; University of Lille, Inria UMR 9189 - CRIStAL, Lille, France
Ragonnet, Romain; School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
Tomenko, Pierre; Mathematics and Operational Research Department, University of Mons, Mons, Belgium
Mezmaz, Mohand; Mathematics and Operational Research Department, University of Mons, Mons, Belgium
Melab, Nouredine; University of Lille, Inria UMR 9189 - CRIStAL, Lille, France
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