Solving the integrated airline recovery problem using column-and-row generation

Maher, Stephen (2016) Solving the integrated airline recovery problem using column-and-row generation. Transportation Science, 50 (1). pp. 216-239. ISSN 0041-1655

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Abstract

Airline recovery presents very large and difficult problems requiring high-quality solutions within short time limits. To improve computational performance, various solution approaches have been employed, including decomposition methods and approximation techniques. There has been increasing interest in the development of efficient and accurate solution techniques to solve an integrated airline recovery problem. In this paper, an integrated airline recovery problem is developed, integrating the schedule, crew, and aircraft recovery stages, and it is solved using column-and-row generation. A general framework for column-and-row generation is presented as an extension of current generic methods. This extension considers multiple secondary variables and linking constraints and is proposed as an alternative solution approach to Benders' decomposition. The application of column-and-row generation to the integrated recovery problem demonstrates the improvement in the solution run times and quality compared to a standard column generation approach. Column-and-row generation improves solution run times by reducing the problem size and thereby achieving faster execution of each linear programming solve. As a result of this evaluation, a number of general enhancement techniques are identified to further reduce the run times of column-and-row generation. This paper also details the integration of the row generation procedure with branch and price, which is used to identify integer optimal solutions.

Item Type:
Journal Article
Journal or Publication Title:
Transportation Science
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/3300/3313
Subjects:
ID Code:
127115
Deposited By:
Deposited On:
29 Aug 2018 08:34
Refereed?:
Yes
Published?:
Published
Last Modified:
26 Aug 2020 03:45