A Relative Robust Optimization Approach for Full Factorial Scenario Design of Data Uncertainty and Ambiguity

Tiravat Assavapokee (), Matthew J. Realff () and Jane C. Ammons ()
Additional contact information
Tiravat Assavapokee: University of Houston
Matthew J. Realff: Georgia Institute of Technology
Jane C. Ammons: Georgia Institute of Technology

A chapter in Optimization and Logistics Challenges in the Enterprise, 2009, pp 145-174 from Springer

Abstract: Summary This chapter presents a relative robust optimization algorithm for two-stage decision making under uncertainty (ambiguity) where the structure of the first-stage problem is a mixed integer linear programming model and the structure of the second-stage problem is a linear programming model. In the structure of the considered problem, each uncertain parameter can take its value from a finite set of real numbers with unknown probability distribution independently of other parameters’ settings. This structure of parametric uncertainty is referred to in this chapter as the full-factorial scenario design of data uncertainty. The algorithm is shown to be efficient for solving large-scale relative robust optimization problems under this structure of the parametric uncertainty. The algorithm coordinates three computational stages to efficiently solve the overall optimization problem. Bi-level programming formulations are the main components in two of these three computational stages. The main contributions of this chapter are the theoretical development of the robust optimization algorithm and its applications in robust strategic decision making under uncertainty (e.g., supply chain network infrastructure design problems).

Keywords: Uncertain Parameter; Mixed Integer Linear Programming; Robust Optimization; Facility Location Problem; Robust Solution (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:spochp:978-0-387-88617-6_4

Ordering information: This item can be ordered from
http://www.springer.com/9780387886176

DOI: 10.1007/978-0-387-88617-6_4

Access Statistics for this chapter

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