Development of Time Couplings Method Using Evolutionary Algorithms

The article presents the results of a computation experiment in which a genetic algorithm (GA) and a hybrid evolutionary algorithm (HEA) were used. The respective results are compared for an objective function describing employment level regularity. It has been demonstrated that evolutionary algorithms can be used for optimizing demand for resources (workers) in time coupling methods.

[1]  Sou-Sen Leu,et al.  RESOURCE LEVELING IN CONSTRUCTION BY GENETIC ALGORITHM-BASED OPTIMIZATION AND ITS DECISION SUPPORT SYSTEM APPLICATION , 2000 .

[2]  T. Yamada,et al.  Solving the C/sub sum/ permutation flowshop scheduling problem by genetic local search , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[3]  Takeshi Yamada,et al.  Genetic Algorithms, Path Relinking, and the Flowshop Sequencing Problem , 1998, Evolutionary Computation.

[4]  Wojciech Bozejko,et al.  A hybrid evolutionary algorithm for some discrete optimization problems , 2005, 5th International Conference on Intelligent Systems Design and Applications (ISDA'05).