Delayed Column Generation: Solving Large-Scale Optimization Models From the Airline Industry
Adithya Patil () and
Milind G. Sohoni ()
Additional contact information
Adithya Patil: Indian School of Business
Milind G. Sohoni: Indian School of Business
Chapter Chapter 9 in Optimization Essentials, 2024, pp 279-296 from Springer
Abstract:
Abstract Over the past several decades, the airline industry has widely used sophisticated large-scale optimization models and algorithms to improve operational efficiency, increase revenue, and improve profitability. This chapter discusses one such large-scale optimization technique called Delayed Column Generation. The approach is commonly used to solve instances of several challenging optimization models involving strategic business and operational processes. For example, the classical aircraft rotation set partitioning optimization model is routinely solved using delayed column generation. This chapter delves into the details of the delayed column generation and branch-and-price computational procedures. To illustrate their applicability in real-world airline models, we describe a simple application to the aircraft rotation model. Additionally, we provide a few references to articles describing other applications in the airline industry.
Keywords: Large-scale optimization; Airline optimization; Column-generation; Set-partitioning models (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_9
Ordering information: This item can be ordered from
http://www.springer.com/9789819954919
DOI: 10.1007/978-981-99-5491-9_9
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 ().