Hybrid decision technique for supplier selection
The aim of the hybrid decision technique for supplier allocation is to help corporate decision makers who are faced with making numerous and conflicting evaluations to decide on the choice of supplier for business collaboration, follow by recommending splitting order quantities obtained under the...
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2010
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sg-ntu-dr.10356-399232023-03-03T20:32:21Z Hybrid decision technique for supplier selection Tan, Keng Guan. Goh Eck Soong, Angela School of Computer Engineering DRNTU::Engineering::Computer science and engineering::Computer applications The aim of the hybrid decision technique for supplier allocation is to help corporate decision makers who are faced with making numerous and conflicting evaluations to decide on the choice of supplier for business collaboration, follow by recommending splitting order quantities obtained under the limitations from supplier such as capacity, procurement criteria and user‐specified constraints. There is an appeal for effective supplier‐selection as it helps corporation to build business competitive advantage and pull off strong attainment in interrelated corporate objective. Currently, the multiple criteria decision making techniques of cognitive advisor system that are implemented during previous final year projects could neither consider supplier capacities and constraints nor handle the order allocation. Hence, the project propose to develop a multiple objective programming model based on a multiple criteria decision making context to provide a systematic and rational approach for performing supplier‐selection and order splitting. To implement a dependable and viable technique, the author reviewed the use of Matlab optimization solvers, nonetheless ultimately developing a customized branch and bound algorithm for solving the devised model. Bachelor of Engineering (Computer Engineering) 2010-06-08T02:46:12Z 2010-06-08T02:46:12Z 2010 2010 Final Year Project (FYP) http://hdl.handle.net/10356/39923 en Nanyang Technological University 70 p. application/pdf |
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DRNTU::Engineering::Computer science and engineering::Computer applications Tan, Keng Guan. Hybrid decision technique for supplier selection |
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The aim of the hybrid decision technique for supplier allocation is to help corporate decision makers who are faced with making numerous and conflicting
evaluations to decide on the choice of supplier for business collaboration, follow by
recommending splitting order quantities obtained under the limitations from supplier
such as capacity, procurement criteria and user‐specified constraints. There is an
appeal for effective supplier‐selection as it helps corporation to build business
competitive advantage and pull off strong attainment in interrelated corporate
objective. Currently, the multiple criteria decision making techniques of cognitive
advisor system that are implemented during previous final year projects could neither
consider supplier capacities and constraints nor handle the order allocation. Hence, the
project propose to develop a multiple objective programming model based on a
multiple criteria decision making context to provide a systematic and rational approach
for performing supplier‐selection and order splitting. To implement a dependable and
viable technique, the author reviewed the use of Matlab optimization solvers,
nonetheless ultimately developing a customized branch and bound algorithm for
solving the devised model. |
author2 |
Goh Eck Soong, Angela |
author_facet |
Goh Eck Soong, Angela Tan, Keng Guan. |
format |
Final Year Project |
author |
Tan, Keng Guan. |
author_sort |
Tan, Keng Guan. |
title |
Hybrid decision technique for supplier selection |
title_short |
Hybrid decision technique for supplier selection |
title_full |
Hybrid decision technique for supplier selection |
title_fullStr |
Hybrid decision technique for supplier selection |
title_full_unstemmed |
Hybrid decision technique for supplier selection |
title_sort |
hybrid decision technique for supplier selection |
publishDate |
2010 |
url |
http://hdl.handle.net/10356/39923 |
_version_ |
1759854136167235584 |