[1] Xu Hang, Xu Rong, Ye Qingtai,. Modeling and optimization of unbalanced multi-stage logistic system [J]. Journal of Southeast University (English Edition), 2005, 21 (2): 220-224. [doi:10.3969/j.issn.1003-7985.2005.02.021]

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Modeling and optimization of unbalanced multi-stage logistic system()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

21

Issue:

2005 2

Page:

220-224

Research Field:

Automation

Publishing date:

2005-06-30

Info

Title:

Modeling and optimization of unbalanced multi-stage logistic system

Author(s):

Xu Hang;  Xu Rong;  Ye Qingtai

School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200030, China

Keywords:

logistics;  optimization;  genetic algorithm;  Prüfer number;  spanning tree;  parameterized interface distribution;  unbalanced multi-stage logistic system

PACS:

TP18

DOI:

10.3969/j.issn.1003-7985.2005.02.021

Abstract:

To decompose an unbalanced multi-stage logistic system to multiple independent single-stage logistic systems, a new notion of parameterized interface distribution is presented.For encoding the logistic pattern on each stage, the Prüfer number is used.With the improved decoding procedure, any Prüfer number produced stochastically can be decoded to a feasible logistic pattern, which can match with the capacities of the nodes of the logistic system.With these two innovations, a new modeling method based on parameterized interface distribution and the Prüfer number coding is put forward.The corresponding genetic algorithm, named as PIP-GA, can find better solutions and require less computational time than st-GA.Although requiring a little more consumption of memory, PIP-GA is still an efficient and robust method in the modeling and optimization of unbalanced multi-stage logistic systems.

References:

[1] Syarif A, Yun Y S, Gen M.Study on multi-stage logistic chain network:a spanning tree-based genetic algorithm approach [J].Computers and Industrial Engineering, 2002, 43(1, 2):299-314.
[2] Syarif A, Gen M.Hybrid genetic algorithm for production/distribution system in supply chain [J].International Journal of Smart Engineering System Design, 2003, 5(4):289-298.
[3] Gen M, Cheng R.Genetic algorithms and engineering optimization [M].New York:Wiley, 2000.
[4] Gen M, Cheng R, Oren S.Network design techniques using adapted genetic algorithms [J].Advances in Engineering Software, 2001, 32(9):731-744.
[5] Zhou G, Min H, Gen M.The balanced allocation of customers to multiple distribution centers in the supply chain network:a genetic algorithm approach [J].Computers and Industrial Engineering, 2002, 43(1, 2):251-261.
[6] Zhou G, Zhu Y, Weng X, et al.The generalized approaches of genetic algorithms on constrained minimum spanning tree problems [A].In:Proc of World Congr Intelligent Control Autom[C].Piscataway:Institute of Electrical and Electronics Engineers Inc, 2004.2141-2145.
[7] Syarif A, Gen M.Solving exclusionary side constrained transportation problem by using a hybrid spanning tree-based genetic algorithm [J].Journal of Intelligent Manufacturing, 2003, 14(3, 4):389-399.
[8] Zhou G, Gen M.A genetic algorithm approach on tree-like telecommunication network design problem [J].Journal of the Operational Research Society, 2003, 54(3):248-254.

Memo

Memo:

Biographies: Xu Hang(1975—), male, graduate;Ye Qingtai(corresponding author), male, professor, yqingtai@online.sh.cn.

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