DocumentCode
1653628
Title
Notice of Retraction
A Support Vector Machines based framework of Inventory Decision Supporting System
Author
Li Qingsong ; Qin Lizhi
Author_Institution
Sch. of Transp. & Automotive Eng., Xihua Univ., Chengdu, China
Volume
1
fYear
2010
Firstpage
576
Lastpage
578
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Inventory Decision Supporting System (IDSS) is one of the most hot points in Logistics. Since the inventory decision is conditioned by so many changing factors, available models and methods seem not so effective as in image. A new framework of Inventory Decision Supporting System is provided in this paper. The framework is based on pattern classification. The classification adopts Support Vector Machines, which is more suitable for context of fewer samples and more factors for consideration. Our IDSS dynamically evaluates the efficiency of models and its applicable targets to adjusts decision to adapt changing conditions.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Inventory Decision Supporting System (IDSS) is one of the most hot points in Logistics. Since the inventory decision is conditioned by so many changing factors, available models and methods seem not so effective as in image. A new framework of Inventory Decision Supporting System is provided in this paper. The framework is based on pattern classification. The classification adopts Support Vector Machines, which is more suitable for context of fewer samples and more factors for consideration. Our IDSS dynamically evaluates the efficiency of models and its applicable targets to adjusts decision to adapt changing conditions.
Keywords
decision support systems; inventory management; logistics; pattern classification; support vector machines; IDSS; inventory decision supporting system; logistics; pattern classification; support vector machines; Computers; Educational institutions; Man machine systems; Support Vector Machines; decision supporting system; efficiency evaluation; inventory; pattern classification;
fLanguage
English
Publisher
ieee
Conference_Titel
Advanced Management Science (ICAMS), 2010 IEEE International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-6931-4
Type
conf
DOI
10.1109/ICAMS.2010.5553091
Filename
5553091
Link To Document