2013Applied Mechanics and MaterialsOpen access

Path Optimization of Container Multimodal Transportation Based on Improved Genetic Algorithm

Jing Li, Yue Fang Yang, Huan Liu

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Abstract

This paper mainly studies on the optimization and algorithm of multimodal transport path. The algorithm considered the transportation time, freight, the different transport ways, and the possibility of occurrence of facelift premise between each node. All of this determined the best path and the mixture of intermodal transport, which minimize the total freight fee. Take the complexity of multimodal optimization into account, this paper optimized the genetic algorithm to transport scheme. The certain population of crossover and mutation rules in application will continue to evolve by coding each path, finally achieve the solution of concrete steps.

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What this paper is about

This paper mainly studies on the optimization and algorithm of multimodal transport path. The algorithm considered the transportation time, freight, the different transport ways, and the possibility of occurrence of facelift premise between each node. All of this determined the best path and the mixture of intermodal transport, which minimize the total freight fee. Take the complexity of multimodal optimization into account, this paper optimized the genetic algorithm to transport scheme. The certain population of crossover and mutation rules in application will continue to evolve by coding each path, finally achieve the solution of concrete steps.

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Available abstract

This paper mainly studies on the optimization and algorithm of multimodal transport path. The algorithm considered the transportation time, freight, the different transport ways, and the possibility of occurrence of facelift premise between each node. All of this determined the best path and the mixture of intermodal transport, which minimize the total freight fee. Take the complexity of multimodal optimization into account, this paper optimized the genetic algorithm to transport scheme. The certain population of crossover and mutation rules in application will continue to evolve by coding each path, finally achieve the solution of concrete steps.

Key concepts: Crossover, Multimodal transport, Path (computing), Genetic algorithm, Coding (social sciences), Mathematical optimization, Computer science, Container (type theory)

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