Robust Scheduling with Logic-Based Benders Decomposition

Elvin Coban (elvin.coban@ozyegin.edu.tr), Aliza Heching (ahechi@us.ibm.com), J. N. Hooker (jh38@andrew.cmu.edu) and Alan Scheller-Wolf (awolf@andrew.cmu.edu)
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
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DOI: 10.1007/978-3-319-28697-6_15

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