As a new type of logistics network nodes, the logistics park is a significant part of the logistics system. Planning and constructing logistics parks scientifically is conducive to not only the construction of modern logistics environment but also the realization of the parks’ function and the whole benefit of the logistics system. Realization of the optimal layout of functional areas with appropriate methods is the basis of logistics park planning and construction. Accordingly, the internal layout of each functional area can be further designed. In this paper, the genetic algorithm is adopted to solve the functional areas layout optimization problem of the railway logistics parks. After getting the comprehensive relationship chart of the different functional areas, the paper solved the layout problem with mathematical methods instead of the traditional manual adjustment method. Combined with relevant constraint conditions, the paper constructed the model taking the maximal arithmetic product of comprehensive relationship and adjacency degree as the objective function. Then the article coded with Matlab based on genetic algorithm. The model was testified to its feasibility and rationality by a practical illustrative example. In this paper, the functional area layout problem of the logistics park was regarded as a mathematical optimization problem, so that the uncertainties of layout affected by subjective factors was reduced to a certain extent combining qualitative analysis with quantitative analysis. The application of genetic algorithm in the layout optimization model greatly improved the quantifiable accuracy which provided a new thought for the functional areas layout of railway logistics parks.
[1]
Marián Šulgan,et al.
Logistics park development in Slovak Republic
,
2006
.
[2]
Uday Kumar Chakraborty,et al.
A Branching Process Model for Genetic Algorithms
,
1995,
Inf. Process. Lett..
[3]
Joanna Lis,et al.
Genetic algorithm with the dynamic probability of mutation in the classification problem
,
1995,
Pattern Recognit. Lett..
[4]
Lk Chu,et al.
Facilities Planning and Enterprise logistics
,
2004
.
[5]
Panagiotis P. Repoussis,et al.
The open vehicle routing problem with time windows
,
2007,
J. Oper. Res. Soc..
[6]
Dana Vrajitoru,et al.
Crossover Improvement for the Genetic Algorithm in Information Retrieval
,
1998,
Information Processing & Management.
[7]
Kyu-Yeul Lee,et al.
An improved genetic algorithm for multi-floor facility layout problems having inner structure walls and passages
,
2005,
Comput. Oper. Res..