This paper describes a multi-objective evolutionary algorithm for a typical serial production problem, in which two or more consecutive departments must schedule their internal work, each taking into account the requirements of the other departments. There are various single-objective heuristics to deal with this problem, while the multi-objective formulation calls for innovative approaches. To this aim, we devise a novel evolutionary algorithm, and compare it with two other state-of-art genetic optimizers used in similar contexts. The results obtained on both small-size problems with known Pareto-sets, and larger problems derived from industrial production of furniture confirm the effectiveness of the proposed approach.
Multi-objective evolutionary algorithms for a class of sequencing problems in manufacturing environments / Meloni, C.; Naso, D.; Turchiano, B.. - (2003), pp. 8-13. (Intervento presentato al convegno IEEE International Conference on Systems, Man & Cybernetics, 2003 tenutosi a Washington, DC) [10.1109/ICSMC.2003.1243784].
Multi-objective evolutionary algorithms for a class of sequencing problems in manufacturing environments
Meloni C.;
2003
Abstract
This paper describes a multi-objective evolutionary algorithm for a typical serial production problem, in which two or more consecutive departments must schedule their internal work, each taking into account the requirements of the other departments. There are various single-objective heuristics to deal with this problem, while the multi-objective formulation calls for innovative approaches. To this aim, we devise a novel evolutionary algorithm, and compare it with two other state-of-art genetic optimizers used in similar contexts. The results obtained on both small-size problems with known Pareto-sets, and larger problems derived from industrial production of furniture confirm the effectiveness of the proposed approach.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
