Rodrigo Lankaites PinheiroDario Landa-SilvaWasakorn LaesanklangAdemir Aparecido ConstantinoUniversidade Estadual de MaringaUniversity of NottinghamMahidol UniversityWebroster Ltd.2019-08-232019-08-232018-01-01ICORES 2018 - Proceedings of the 7th International Conference on Operations Research and Enterprise Systems. Vol.2018-January, (2018), 132-1432-s2.0-85047962591https://repository.li.mahidol.ac.th/handle/20.500.14594/45687Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved. Modern multiobjective algorithms can be computationally inefficient in producing good approximation sets for highly constrained many-objective problems. Such problems are common in real-world applications where decision-makers need to assess multiple conflicting objectives. Also, different instances of real-world problems often share similar fitness landscapes because key parts of the data are the same across these instances. We we propose a novel methodology that consists of solving one instance of a given problem scenario using computationally expensive multiobjective algorithms to obtain a good approximation set and then using Goal Programming with efficient single-objective algorithms to solve other instances of the same problem scenario. We propose three goal-based objective functions and show that on a real-world home healthcare planning problem the methodology can produce improved results in a shorter computation time.Mahidol UniversityComputer ScienceDecision SciencesEngineeringConference PaperSCOPUS