Mixed-Integer Programming Modeling Strategies for Scheduling-Based Optimization Problems

J. Cole Smith () and Robert M. Curry ()
Additional contact information
J. Cole Smith: 223 Link Hall, Syracuse University
Robert M. Curry: 4166 Bell Engineering Center, University of Arkansas

Chapter Chapter 3 in Optimization Essentials, 2024, pp 65-112 from Springer

Abstract: Abstract We provide a tutorial treatment of basic techniques for modeling scheduling optimization problems as mixed-integer linear programs, presented for readers having at least a limited background in linear programming formulations and algorithms for solving mathematical optimization problems. Although the scope of scheduling applications is far too broad to cover with a single chapter, many scheduling formulations employ a common set of modeling strategies that can be directly used, modified, or combined to formulate novel scheduling problems. We briefly cover these fundamental scheduling principles and illustrate how they can be adapted to model several modern applications arising in the field of scheduling optimization. These applications include optimizing radar pulse interleaving, identifying schedules that are robust to changes in data, and scheduling path flows in a dynamic flow optimization problem.

Keywords: Scheduling; Mixed-integer programming; Optimization applications; Robust optimization (search for similar items in EconPapers)
Date: 2024
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:isochp:978-981-99-5491-9_3

Ordering information: This item can be ordered from
http://www.springer.com/9789819954919

DOI: 10.1007/978-981-99-5491-9_3

Access Statistics for this chapter

More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().