Robust Scheduling with Logic-Based Benders Decomposition

Elvin Coban (), Aliza Heching (), J. N. Hooker () and Alan Scheller-Wolf ()
Additional contact information
Elvin Coban: Özyeǧin University
Aliza Heching: IBM Thomas J. Watson Research Center
J. N. Hooker: Carnegie Mellon University
Alan Scheller-Wolf: Carnegie Mellon University

A chapter in Operations Research Proceedings 2014, 2016, pp 99-105 from Springer

Abstract: Abstract We study project scheduling at a large IT services delivery center in which there are unpredictable delays. We apply robust optimization to minimize tardiness while informing the customer of a reasonable worst-case completion time, based on empirically determined uncertainty sets. We introduce a new solution method based on logic-based Benders decomposition. We show that when the uncertainty set is polyhedral, the decomposition simplifies substantially, leading to a model of tractable size. Preliminary computational experience indicates that this approach is superior to a mixed integer programming model solved by state-of-the-art software.

Keywords: Robust Optimization; Master Problem; Bender Decomposition; Agent Class; Tardiness Cost (search for similar items in EconPapers)
Date: 2016
References: Add references at CitEc
Citations: View citations in EconPapers (1)

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:oprchp:978-3-319-28697-6_15

Ordering information: This item can be ordered from
http://www.springer.com/9783319286976

DOI: 10.1007/978-3-319-28697-6_15

Access Statistics for this chapter

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