In order to improve the picking efficiency,reduce the picking time,this paper take artificial picking operation of a certain distribution center which has double-area warehouse as the studying object.Discuss the picki...In order to improve the picking efficiency,reduce the picking time,this paper take artificial picking operation of a certain distribution center which has double-area warehouse as the studying object.Discuss the picking task allocation and routing problems.Establish the TSP model of order-picking system.Create a heuristic algorithm bases on the Genetic Algorithm(GA)which help to solve the task allocating problem and to get the associated order-picking routes.And achieve the simulation experiment with the Visual 6.0C++platform to prove the rationality of the model and the effectiveness of the arithmetic.展开更多
To solve the problem of low efficiency in pipe routing design, an improved genetic algorithm based approach is proposed. To present this approach, the paper mainly describes a generation method of nodes considering th...To solve the problem of low efficiency in pipe routing design, an improved genetic algorithm based approach is proposed. To present this approach, the paper mainly describes a generation method of nodes considering the safety distance of pipes and the directional constraints at terminals, the definition of a double coding technique, the collision detection method, the concept of energy and the definition of fitness functions. The similarity detection is introduced to prevent close breeding in the crossover operator, the selection pressure is controlled according to the evolution situation and a heuristic mutation method is used to boost the evolution. Simulation case shows that this approach is more practical and can satisfy different design requirements by changing algorithm parameters.展开更多
The purpose of this paper is to solve the problem of Ad Hoc network routing protocol using a Genetic Algorithm based approach. In particular, the greater reliability and efficiency, in term of duration of communicatio...The purpose of this paper is to solve the problem of Ad Hoc network routing protocol using a Genetic Algorithm based approach. In particular, the greater reliability and efficiency, in term of duration of communication paths, due to the introduction of Genetic Classifier is demonstrated.展开更多
文摘In order to improve the picking efficiency,reduce the picking time,this paper take artificial picking operation of a certain distribution center which has double-area warehouse as the studying object.Discuss the picking task allocation and routing problems.Establish the TSP model of order-picking system.Create a heuristic algorithm bases on the Genetic Algorithm(GA)which help to solve the task allocating problem and to get the associated order-picking routes.And achieve the simulation experiment with the Visual 6.0C++platform to prove the rationality of the model and the effectiveness of the arithmetic.
基金Supported by National "863" Project of China (2006AA09A104)
文摘To solve the problem of low efficiency in pipe routing design, an improved genetic algorithm based approach is proposed. To present this approach, the paper mainly describes a generation method of nodes considering the safety distance of pipes and the directional constraints at terminals, the definition of a double coding technique, the collision detection method, the concept of energy and the definition of fitness functions. The similarity detection is introduced to prevent close breeding in the crossover operator, the selection pressure is controlled according to the evolution situation and a heuristic mutation method is used to boost the evolution. Simulation case shows that this approach is more practical and can satisfy different design requirements by changing algorithm parameters.
文摘The purpose of this paper is to solve the problem of Ad Hoc network routing protocol using a Genetic Algorithm based approach. In particular, the greater reliability and efficiency, in term of duration of communication paths, due to the introduction of Genetic Classifier is demonstrated.
