Volume 21, Issue 4 (IJIEPM 2011)                   2011, 21(4): 117-130 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Zare Mehrjerdi Y, Resaye H, Ghasemi Gajvan A A. Evaluation of Advanced Manufacturing Technologies using Chance-Constraints Linear Programming and Fuzzy Multi-Criteria Decision-Making. Journal title 2011; 21 (4) :117-130
URL: http://ijiepm.iust.ac.ir/article-1-495-en.html
, Yazm2000@yahoo.com
Abstract:   (9524 Views)

  The competition enhancement and demand increases have directed many producers to employ advanced manufacturing technologies. For this purpose, the selection of best alternative among various manufacturing technologies is the topic of high importance. Many articles have discussed the insufficiency of general financial measures for investment justification in advanced manufacturing technologies. Many analysts have claimed that at the time of reviewing various alternatives, in addition to the economical measures, it is highly important that strategic measures of quality, productivity, and flexibility enhancement that are not of quantity type also take into consideration. To economically evaluate various manufacturing technologies, a linear programming model with stochastic constraints is suggested in this article. With this model, we can calculate the present worth of each of present projects by taking their characteristics of productivity and quality, for instance, into consideration. Thereafter, with the help of a fuzzy multi criterion decision making model, where its criterions are a mix of strategic objectives and economical criterions, the best alternative will be selected.

Full-Text [PDF 543 kb]   (2670 Downloads)    

Add your comments about this article : Your username or Email:

Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.