Interactive Multi-Objective Stochastic Programming Approaches for Designing Robust Supply Chain Networks

Amir Azaron (), Kai Furmans () and Mohammad Modarres ()
Additional contact information
Amir Azaron: University of Karlsruhe (TH)
Kai Furmans: University of Karlsruhe (TH)
Mohammad Modarres: Sharif University of Technology

Chapter 28 in Operations Research Proceedings 2008, 2009, pp 173-178 from Springer

Abstract: Summary Many attempts have been made to model and optimize supply chain design, most of which are based on deterministic approaches, see for example [3], [8], [4] and many others. In order to take into account the effects of the uncertainty in the production scenario, a two-stage stochastic model is proposed in this paper. There are a few research works addressing comprehensive (strategic and tactical issues simultaneously) design of supply chain networks using two-stage stochastic models including [6], [9], [1] and [7]. [2] developed a multi-objective stochastic programming approach for designing robust supply chains. Then, they used goal attainment technique, see [5] for details, to solve the resulting multi-objective problem. This method has the same disadvantages as those of goal programming; namely, the preferred solution is sensitive to the goal vector and the weighting vector given by the decision maker, and it is very hard in practice to get the proper goals and weights. To overcome this drawback, we use STEM method in this paper to solve this multi-objective model.

Keywords: Supply Chain; Supply Chain Network; Compromise Solution; Downside Risk; Supply Chain Design (search for similar items in EconPapers)
Date: 2009
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-00142-0_28

Ordering information: This item can be ordered from
http://www.springer.com/9783642001420

DOI: 10.1007/978-3-642-00142-0_28

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().