| Abstract and Applied Analysis | |
| Research on Internal Layout Optimization of Logistics Node under the Conditions of Complex Terrain Based on Computer Vision and Geographical Simulation System | |
| Research Article | |
| Xuejun Feng2  Wei Wang1  | |
| [1] Water Transportation Planning & Logistics Engineering Institution, College of Harbor, Coastal and Offshore Engineering, Hohai University, Jiangsu, Nanjing 210098, China, hhu.edu.cn;Key Laboratory of Highway Engineering of Ministry of Education, Changsha University of Science & Technology, Changsha 410004, China;Water Transportation Planning & Logistics Engineering Institution, College of Harbor, Coastal and Offshore Engineering, Hohai University, Jiangsu, Nanjing 210098, China, hhu.edu.cn | |
| Others : 1268699 DOI : 10.1155/2012/964291 |
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| received in 2012-09-24, accepted in 2012-10-17, 发布年份 2012 | |
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【 摘 要 】
This paper solves the problem of logistics node space relationship beyond expression based on computer vision technology, proposes internal layout optimization mathematical model of logistics node on the basis of overall consideration of function zone geometry shape, the optimal area utilization rate, and the minimum material handling cost, and then designs a highly mixed genetic simulated annealing algorithm based on multiagent to get layout solution. Through contrasting, the result has shown that the model and algorithms put forward in this paper can realize large-scale internal layout optimization of logistics node under the conditions of complex terrain and multiple constraints.
【 授权许可】
CC BY
Copyright © 2012 Wei Wang and Xuejun Feng. 2012
【 预 览 】
| Files | Size | Format | View |
|---|---|---|---|
| 964291.pdf | 1153KB | ||
| Figure 5 | 179KB | Image | |
| Figure 4 | 49KB | Image | |
| Figure 3 | 171KB | Image | |
| Figure 2 | 166KB | Image | |
| Figure 1 | 190KB | Image |
【 图 表 】
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【 参考文献 】
- [1]Y. H. Jiang, X. S. Wang. (2002). Using genetic algorithms to generate alternative schemes for urban planning. Engineering Journal of Wuhan University.35(3):63-66. DOI: 10.1080/00207540903117840.
- [2]C. Linning. (2003). Logistics System Planning- Modeling and Case Study. DOI: 10.1080/00207540903117840.
- [3]J. R. Jaramillo, A. R. McKendall. (2010). The generalised machine layout problem. International Journal of Production Research.48(16):4845-4859. DOI: 10.1080/00207540903117840.
- [4]Y. Jianhua, P. Lijing. (2012). Optimizing design of layout arrangement for workshop logistics system. Journal of Convergence Information Technology.7(13):499-507. DOI: 10.1080/00207540903117840.
- [5]C. J. Brookes. (2001). A genetic algorithm for designing optimal patch configurations in GIS. International Journal of Geographical Information Science.15(6):539-559. DOI: 10.1080/00207540903117840.
- [6]C. J. Brookes. (1997). A parameterized region-growing programme for site allocation on raster suitability maps. International Journal of Geographical Information Science.11(4):375-396. DOI: 10.1080/00207540903117840.
- [7]W. Xinsheng, J. Youhua. (2004). Simulating annealing for generating the optimal urban land-use plans. Geographical Research.23(6):727-736. DOI: 10.1080/00207540903117840.
- [8]C. M. Feng, J. J. Lin. (1999). Using a genetic algorithm to generate alternative sketch maps for urban planning. Computers, Environment and Urban Systems.23(2):91-108. DOI: 10.1080/00207540903117840.
- [9]J. C. J. H. Aerts, G. B. M. Heuvelink. (2002). Using simulated annealing for resource allocation. International Journal of Geographical Information Science.16(6):571-587. DOI: 10.1080/00207540903117840.
- [10]R. G. Cromley, D. M. Hanink. (1999). Coupling land use allocation models with raster GIS. Journal of Geographical Systems.1(2):137-153. DOI: 10.1080/00207540903117840.
- [11]K. B. Matthews, A. R. Sibbald, S. Craw. (1999). Implementation of a spatial decision support system for rural land use planning: integrating geographic information system and environmental models with search and optimisation algorithms. Computers and Electronics in Agriculture.23(1):9-26. DOI: 10.1080/00207540903117840.
- [12]E. Bribiesca. (2000). A chain code for representing 3D curves. Pattern Recognition.33(5):755-765. DOI: 10.1080/00207540903117840.
- [13]J. Chunyan. (2006). Algorithm for obtaining the freeman codes of components' contour in an image. Journal of Shaoyang University (Science and Technology).3(4):25-28. DOI: 10.1080/00207540903117840.
- [14]Y. Sudi, C. Fang. (2005). An optimized linear-time component-labeling algorithm of image. Journal of Shihezi University (Natural Science).23(6):775-777. DOI: 10.1080/00207540903117840.
- [15]S. Zhenhong, F. Xiaodong, Y. Meiyu, L. Hui. et al.(2008). Line detection based on Euclidean distance. Journal of Computer Applications.28(1):177-180. DOI: 10.1080/00207540903117840.
- [16]H. Freeman. (1970). Boundary encoding and processing. Picture Processing and Psy-Chohistories:41-266. DOI: 10.1080/00207540903117840.
- [17]C. Xinming, J. Ruibin. (2007). The study on the minimum enclosure rectangle of irregular parts. Bulletin of Science and Technology.23(1):102-105. DOI: 10.1080/00207540903117840.
- [18]T. Xiaodong, L. Zhong. (2007). Mine target recognition based on shape similarity. Technical Acoustics.26(3):493-497. DOI: 10.1080/00207540903117840.
- [19]Z. Hongbin. (2004). Research on Content Based Image Retrieval Technology and Its Applications in Defense Information Technology Area. DOI: 10.1080/00207540903117840.
- [20]M. Flickner. Efficient and effective querying by image content. IBM Research Division. DOI: 10.1080/00207540903117840.
- [21]J. Wang, W. Yang. (2005). Dynamic attractive factors applied in packing problems. Journal of Computer-Aided Design & Computer Graphics.17(8):1725-1730. DOI: 10.1080/00207540903117840.
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