A Hybrid MOO/MCDM Optimization Approach to Improve Decision-Making in Multiobjective Optimization
Abstract
Multiobjective optimization (MOO) and multicriteria decision-making (MCDM) are critical disciplines in operations research, aiming to assist decision-makers in making the best decisions in complex problems. Nevertheless, the hybridization of the process has yet to be explored. In this case, the hybridization of the decision process is analyzed to evaluate the solution set obtained with this approach and compare it against the solutions obtained with Pareto Set. This novel approach shows that according to the decision-maker preferences, solutions could be in this solution set despite not being included in the Pareto Set. This approach gives alternatives to decision-makers without moving apart much from the best solution. A flow shop is used as a numerical example to compare the Pareto Set and hybrid approach outcomes.
Más información
Título según WOS: | ID WOS:001159614700008 Not found in local WOS DB |
Título de la Revista: | PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS, CIARP 2023, PT I |
Volumen: | 14056 |
Editorial: | SPRINGER INTERNATIONAL PUBLISHING AG |
Fecha de publicación: | 2023 |
Página de inicio: | 100 |
Página final: | 111 |
DOI: |
10.1007/978-3-031-48044-7_8 |
Notas: | ISI |