Design of Near Optimal Decision Rules in Multistage Adaptive Mixed-Integer Optimization

Dimitris Bertsimas () and Angelos Georghiou ()
Additional contact information
Dimitris Bertsimas: Sloan School of Management and Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Angelos Georghiou: Automatic Control Laboratory, Swiss Federal Institute of Technology, Zürich CH-8092, Switzerland

Operations Research, 2015, vol. 63, issue 3, 610-627

Abstract: In recent years, decision rules have been established as the preferred solution method for addressing computationally demanding, multistage adaptive optimization problems. Despite their success, existing decision rules (a) are typically constrained by their a priori design and (b) do not incorporate in their modeling adaptive binary decisions. To address these problems, we first derive the structure for optimal decision rules involving continuous and binary variables as piecewise linear and piecewise constant functions, respectively. We then propose a methodology for the optimal design of such decision rules that have a finite number of pieces and solve the problem robustly using mixed-integer optimization. We demonstrate the effectiveness of the proposed methods in the context of two multistage inventory control problems. We provide global lower bounds and show that our approach is (i) practically tractable and (ii) provides high quality solutions that outperform alternative methods.

Keywords: adaptive optimization; decision rules; mixed-integer optimization (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (38)

Downloads: (external link)
http://dx.doi.org/10.1287/opre.2015.1365 (application/pdf)

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:inm:oropre:v:63:y:2015:i:3:p:610-627

Access Statistics for this article

More articles in Operations Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().