A Fuzzy Chance Constraint Programming Approach for Location-Allocation Problem under Uncertainty in a Closed-Loop Supply Chain

As cost pressure and worldwide resource limitation continue to mount in this era of economic slowdowns, more and more firms and communities have begun to explore the possibility of managing both of the forward and reverse flows within a closed-loop supply chain in a more cost-efficient and timely ma...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:2009 International Joint Conference on Computational Sciences and Optimization : 24-26 April 2009 Ročník 2; s. 836 - 840
Hlavní autoři: Yanxue Gong, Dao Huang, Enbo Wang, Yigong Peng
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.04.2009
Témata:
adaptive genetic algorithm
closed-loop supply chain
Conference management
Constraint optimization
Cost function
fuzzy chance constraint programming
Genetic algorithms
location-allocation problem
Resource management
Reverse logistics
Supply chain management
Supply chains
Technology management
Uncertainty
ISBN:9780769536057, 0769536050
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:As cost pressure and worldwide resource limitation continue to mount in this era of economic slowdowns, more and more firms and communities have begun to explore the possibility of managing both of the forward and reverse flows within a closed-loop supply chain in a more cost-efficient and timely manner. But limited number of research set foot in this area, especially for the location-allocation problem, a basilica part of the supply chain network design, which plays an important role in reducing the whole cost and provide better service. Meanwhile, uncertainties, which can not be avoided in the process of supply chain, should be taken into account so as to decrease the influence of bullwhip effect. In this paper, a fuzzy chance constraint programming approach is put forward and an adaptive genetic algorithm is applied to search for the optimization. Finally, concluding remarks and some recommendations for further research are also presented.
ISBN:9780769536057
0769536050
DOI:10.1109/CSO.2009.151