The requirements for enterprises which have to maintain their position in the international competition have fundamentally changed during the last years. The potential of an enterprise in the market is determined by its logistic performance. To meet the dynamic demands of the markets, companies have to be extremely flexible and productive. These market demands have changed the way of production planning as well as the way of order management of which sequencing is a part. Tools based on Evolutionary Algorithms (EAs) are efficient means to solve the sequencing tasks. This paper describes a parallel evolutionary approach to solve sequencing problems, and compares the efficiency of different EAs. To demonstrate the practical relevance, a real-...
Evolutionary and genetic algorithms are problem-solving methods designed according to a nature inspi...
Traditional genetic algorithms often meet the occurrence of slow convergence and enclosure competiti...
In the real world, it is common to face optimization problems that have two or more objectives that ...
The requirements for enterprises which have to maintain their position in the international competit...
The requirements for enterprises which have to maintain their position in the international competit...
Sequencing problems are difficult combinatorial problems because of the extremely large search space...
The main aim of this thesis is the comparison of parallel and sequential algorithm implementation fo...
Evolution algorithms for combinatorial optimization have been proposed in the 70's. They did not hav...
This study addresses process sequencing subject to precedence constraints which arises as a sub-prob...
Ant Colony Optimisation is a relatively new class of meta-heuristic search techniques for optimisati...
HOLANDA, T. C. O Relacionamento do problema de sequenciamento clássico com o problema do caixeiro vi...
This paper describes a multi-objective evolutionary algorithm for a typical serial production proble...
This thesis addresses process sequencing subject to precedence constraints which arises as a subprob...
Evolutionary algorithms (EAs) are stochastic optimization techniques based on the principles of natu...
Evolutionary Algorithms (EAs) are population-based, stochastic search algorithms that mimic natural ...
Evolutionary and genetic algorithms are problem-solving methods designed according to a nature inspi...
Traditional genetic algorithms often meet the occurrence of slow convergence and enclosure competiti...
In the real world, it is common to face optimization problems that have two or more objectives that ...
The requirements for enterprises which have to maintain their position in the international competit...
The requirements for enterprises which have to maintain their position in the international competit...
Sequencing problems are difficult combinatorial problems because of the extremely large search space...
The main aim of this thesis is the comparison of parallel and sequential algorithm implementation fo...
Evolution algorithms for combinatorial optimization have been proposed in the 70's. They did not hav...
This study addresses process sequencing subject to precedence constraints which arises as a sub-prob...
Ant Colony Optimisation is a relatively new class of meta-heuristic search techniques for optimisati...
HOLANDA, T. C. O Relacionamento do problema de sequenciamento clássico com o problema do caixeiro vi...
This paper describes a multi-objective evolutionary algorithm for a typical serial production proble...
This thesis addresses process sequencing subject to precedence constraints which arises as a subprob...
Evolutionary algorithms (EAs) are stochastic optimization techniques based on the principles of natu...
Evolutionary Algorithms (EAs) are population-based, stochastic search algorithms that mimic natural ...
Evolutionary and genetic algorithms are problem-solving methods designed according to a nature inspi...
Traditional genetic algorithms often meet the occurrence of slow convergence and enclosure competiti...
In the real world, it is common to face optimization problems that have two or more objectives that ...