[1] Zhang Yong, Li Jian, Li Xuhong, Mao Haijun, et al. Integrated logistics facilities network designfor 3PLS under uncertainty [J]. Journal of Southeast University (English Edition), 2006, 22 (4): 570-576. [doi:10.3969/j.issn.1003-7985.2006.04.026]

Copy

Integrated logistics facilities network designfor 3PLS under uncertainty()

Share：

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

Volumn:

22

Issue:

2006 4

Page:

570-576

Research Field:

Traffic and Transportation Engineering

Publishing date:

2006-12-30

Info

Title:

Integrated logistics facilities network designfor 3PLS under uncertainty

Author(s):

Zhang Yong¹;  Li Jian²;  Li Xuhong¹;  Mao Haijun¹

¹ School of Transportation, Southeast University, Nanjing 210096, China
² College of Engineering, Nanjing Agricultural University, Nanjing 210031, China

Keywords:

third party logistics supplier;  integrated logistics facilities network design;  fuzzy chance constrained model;  hybrid genetic algorithm;  uncertainty

PACS:

U492.3

DOI:

10.3969/j.issn.1003-7985.2006.04.026

Abstract:

According to the operational characteristics of the logistics networks for the third party logistics supplier(3PLS), the forward and reverse logistics networks together for 3PLS under the uncertain environment are designed.First, a fuzzy model is proposed by taking multiple customers, multiple commodities, capacitated facility location and integrated logistics facility layout into account.In the model, the fuzzy customer demands and transportation rates are illustrated by triangular fuzzy numbers.Secondly, the fuzzy model is converted into a crisp model by applying fuzzy chance constrained theory and possibility theory, and one hybrid genetic algorithm is designed for the crisp model.Finally, two different examples are designed to illustrate that the model and solution discussed are valid.

References:

[1] Nagurney Anna, Toyasaki Fuminori.Reverse supply chain management and electronic waste recycling:a multitiered network equilibrium framework for e-cycling[J].Transportation Research, Part E, 2005, 41(1):1-28.
[2] Krumwiedea Dennis W, Sheu Chwen.A model for reverse logistics entry by third-party providers[J].Omega, 2002, 30(5):325-333.
[3] Fleischmann Moritz, Bloemhof-Ruwaard Jacqueline M, Dekker Rommert, et al.Quantitative models for reverse logistics:a review[J].European Journal of Operation Research, 1997, 103(1):1-17.
[4] Rogers D S, Tibben-Lembke R S.Going backwards:reverse logistics trends and practices [R].Reno:The University of Nevada, Center for Logistics Management, Reverse Logistics Council, 1998.
[5] Jayaraman Vaidyanathan, Patterson Raymond A, Erik Rolland.The design of reverse distribution networks:Models and solution procedures[J].European Journal of Operational Research, 2003, 150(1):128-149.
[6] Fleischmann Mortiz, Krikke Hans Ronald, Dekker Rommert, et al.A characterization of logistics networks for product recovery [J].Omega, 2000, 28(6):653-666.
[7] Yalabik Baris, Petruzzi Nicholas C, Chhajed Dilip.An integrated product returns model with logistics and marketing coordination[J].European Journal of Operational Research, 2005, 161(1):162-182.
[8] Listes Ovidiu, Dekker Rommert.A stochastic approach to a case study for product recovery network design.[J].European Journal of Operational Research, 2005, 160(1):268-287.
[9] Ko Hyun Jeung.Optimization modeling for the design and operation of dynamic facility networks for 3PLS [D].Louisville:Department of Industrial Engineering of University of Louisville, 2003.
[10] Liu Baoding, Iwamura Kakuzo.Chance constrained programming with fuzzy parameters [J].Fuzzy sets and Systems, 1998, 94(2):227-237.
[11] Liu Baoding, Iwamura Kakuzo.A note chance constrained programming with fuzzy coefficients [J].Fuzzy Sets and Systems, 1998, 100(1-3):229-233.
[12] Zhao Xiaoyu, Wang Dingwei.Fuzzy chance constrained programming model for bi-level distribution network design in the supply chain[J].Control Theory and Application, 2002, 19(2):249-252.(in Chinese)
[13] Holland J H.Adaptation in natural and artificial systems [M].MI: University of Michigan Press, 1975.
[14] Chan Felix T S, Chung S H, Wadhwa Subhash.A hybrid genetic algorithm for production and distribution [J].Omega, 2005, 33(4):345-355.
[15] Hwang Heung-Suk.An improved model for vehicle routing problem with time constraint based on genetic algorithm[J].Computers & Industrial Engineering, 2002, 42(2-4):361-369
[16] Min Hokey, Ko Hyun Jeung, Ko Chang Seong. A genetic algorithm approach to developing the multi-echelon reverse logistics network for product returns[J].Omega, 2006, 34(1):56-69.
[17] Zhou Gengui, Min Hokey, Mitsuo Gen.The balanced allocation of customers to multiple distribution centers in the supply chain network:a genetic algorithm approach[J].Computers & Industrial Engineering, 2002, 43(1, 2):251-261.
[18] Zhang Yong.Optimization methodology for the layout of the logistics crunodes for 3PLS [D].Nanjing:School of Transportation of Southeast University, 2006.(in Chinese)

Memo

Memo:

Biography: Zhang Yong(1976—), male, doctor, lecturer, zan7678@163.com.

Common functions