A Product Development for Green Logistics Model by Integrated Evaluation of Design and Manufacturing and Green Supply Chain

A product development for green logistics model using
the fuzzy analytic network process method is presented for evaluating
the relationships among the product design, the manufacturing
activities, and the green supply chain. In the product development
stage, there can be alternative ways to design the detailed components
to satisfy the design concept and product requirement. In different
design alternative cases, the manufacturing activities can be different.
In addition, the manufacturing activities can affect the green supply
chain of the components and product. In this research, a fuzzy analytic
network process evaluation model is presented for evaluating the
criteria in product design, manufacturing activities, and green supply
chain. The comparison matrices for evaluating the criteria among the
three groups are established. The total relational values between the
three groups represent the relationships and effects. In application, the
total relational values can be used to evaluate the design alternative
cases for decision-making to select a suitable design case and the green
supply chain. In this presentation, an example product is illustrated. It
shows that the model is useful for integrated evaluation of design and
manufacturing and green supply chain for the purpose of product
development for green logistics.





References:
<p>[1] C. A. Weber, J. R. Current, and W. C. Benton, &ldquo;Vendor Selection Criteria
and Methods,&rdquo; European Journal of Operational Research, vol. 50, pp.
2-18, 1991.
[2] R. G. Kasilingam, and C. P. Lee, &ldquo;Selection of Vendors: A Mixed-Integer
Programming Approach,&rdquo; Computers and Industrial Engineering, vol.
31, no. 1-2, pp. 347-350, 1996.
[3] P. K. Humphreys, Y. K., Wong, and F. T. S. Chan, &ldquo;Integrating
environmental criteria into the supplier selection process,&rdquo; Journal of
Materials Processing Technology, vol. 138, pp.349-356, 2003.
[4] G. Akyuz, and E., T. Erman, &ldquo;Supply chain performance measurement: a
literature review,&rdquo; International Journal of Production Research, vol. 48,
no. 17, pp. 5137-5155, 2010.
[5] F. Schultmann, M. Zumkeller, and O. Rentz, &ldquo;Modeling reverse logistic
tasks within closed-loop supply chains: An example from the automotive
industry,&rdquo; European Journal of Operational Research, vol. 171. no. 3,
pp. 1033-1050, 2006.
[6] A. Alshamrani, K. Athur, and R. H. Ballou, &ldquo;Reverse logistics:
simultaneous design of delivery routes and returns strategies,&rdquo; Computers
&amp; Operations Research, vol. 34, no. 2, pp. 595-619, 2007.
[7] Y. Y. Lu, C. H. Wu, and T. C. Kuo, &ldquo;Environmental principles applicable
to green supplier evaluation by using multi-objective decision analysis,&rdquo;
International Journal of Production Research, vol. 45, no. 18, pp.
4317-4331, 2007.
[8] H. J. Ko, and G. W. Evans, &ldquo;A genetic algorithm-based heuristic for the
dynamic integrated forward/reverse logistics network for 3PLs,&rdquo;
Computers &amp; Operations Research, vol. 34, no. 2, pp. 346-366, 2007.
[9] G. F. Yang, Z. P. Wang, and X. Q. Li, &ldquo;The optimization of the
closed-loop supply chain network,&quot; Transportation Research Part E:
Logistics and Transportation Review, vol. 45, no. 1, pp. 16-28, 2009.
[10] G. Kannan, P. Sasikumar, and K. Devika, &ldquo;A genetic algorithm approach
for solving a closed loop supply chain model: A case of battery
recycling,&rdquo; Applied Mathematical Modelling, vol. 34, pp.655-670, 2010.
[11] J. J. Buckley, &ldquo;Ranking Alternatives Using Fuzzy Number,&rdquo; Fuzzy Sets
and systems, vol. 15, pp. 21-31, 1985.
[12] T. L. Saaty, &ldquo;Decision Making with Dependence and Feedback: The
Analytic Network Process,&rdquo; Pittsburgh, PA: RWS Publications, 1996.</p>