![](bg5.png)
the proposed FANP with GP model. A committee
of experts in the IC industry is formed to define the
problem of supplier selection. A questionnaire is
constructed and is targeted on the experts in the IC
design company. Based on the collected opinions of
the experts and the proposed model, the performance
results of the suppliers can be generated. The five
criteria and their respective sub-criteria are listed in
Table 2.
Table 2: Criteria and sub-criteria of FANP.
Criteria Sub-criteria
C1
Purchasing
management
C
11
Low pollution
C
12
Material label
C
13
Recycling
C
2
Process
management
C
21
Modularization
C
22
Process control
C
23
Technology level
C
24
Process improvement capability
C
3
Quality control
C
31
Environmental regulation fulfilment
C
32
Product quality control
C
33
Capability of handling abnormal
products
C
34
Delivery quality and date
C
35
Quality certification
C
4
Business
management
C
41
Internal education and training
C
42
Green R&D design capability
C
43
Pollution control
C
44
Regulation of harmful material control
C
5
Cost control
C
51
Production cost
C
52
Business
cost
C
53
Purchase cost
5 CONCLUSIONS
Green and low carbon supplier evaluation selection
and selection is a very complicated process
involving interrelationship among two or more firms
in a supply chain, and the process is multi-objective
in nature. This research thus develops a model for
fulfilling the task. Based on the selected criteria and
sub-criteria, fuzzy analytic network process (FANP)
is used to evaluate various aspects of suppliers, and
the most suitable suppliers for cooperation can be
obtained. Goal programming (GP) is applied next to
allocate the most appropriate amount of orders to
each of the selected suppliers. In the future, a case
study will be carried out to examine the practicality
of the proposed model. The results shall be a
reference for selecting and allocating orders to the
best green and low carbon suppliers.
ACKNOWLEDGMENTS
This work was supported in part by the National
Science Council in Taiwan under Grant NSC 99-
2632-H-216-001-MY2-2-4.
REFERENCES
Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy
Sets and Systems, 17(3), 233-247.
Chang, C. T. (2007). Multi-choice goal programming.
Omega: The International Journal of Management
Science, 35(4), 389-396.
Charnes, A., Cooper, W. W. (1961). Management Model
and Industrial Application of Linear Programming.
New York: Wiley.
Friedman, T. L., 2008. Hot, Flat, & Crowded: Why We
Need a Green Revolution and How It Can Renew
America. New Kensington: Christian Large.
Kang, H. Y., Lee, A. H. I., Yang, C. Y. (2010). A fuzzy
ANP model for supplier selection as applied to IC
packaging. Journal of Intelligent Manufacturing. on-
line.
Lee, A. H. I. (2009). A fuzzy supplier selection model
with the consideration of benefits, opportunities, costs
and risks. Expert Systems with Applications, 6, 2879-
2893.
Lee, A. H. I., Chen, H. H., Tong, Y., 2008. Developing
new products in a network with efficiency and
innovation. International Journal of Production
Research, 48(17), 4687-4707.
Lee, A. H. I., Kang, H. Y., Chang, C. T. (2009). Fuzzy
multiple goal programming applied to TFT-LCD
supplier selection by downstream manufacturers.
Expert Systems with Applications, 36, 6318-6325.
Lee, A. H. I., Wang, W. M., Lin, T. Y. (2010). An
evaluation framework for technology transfer of new
equipment in high technology industry. Technological
Forecasting & Social Change, 77, 135-150.
Saaty, T. L. (1980). The Analytic Hierarchy Process. New
York: McGraw-Hill.
Todeva, E., Knoke, D. (2005). Strategic alliances and
models of collaboration. Management Decision, 43(1),
123-148.
Wang, G., Huang, S. H., Dismukes, J. P. (2004). Product-
driven supply chain selection using integrated multi-
criteria decision-making methodology. International
Journal of Production Economics, 91, 1-15.
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