Cited by 64Air Traffic Management and OptimizationVehicle Routing Optimization MethodsScheduling and Timetabling Solutions

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Optimal Solutions to a Real-World Integrated Airline Scheduling Problem

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Cacchiani, Valentina, Salazar-González, Juan-José·2017·Transportation Science

DOI 10.1287/trsc.2015.0655

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Abstract

We study an integrated airline scheduling problem for a regional carrier. It integrates three stages of the planning process (i.e., fleet assignment, aircraft routing, and crew pairing) that are typically solved in sequence. Aircraft maintenance is also taken into account. The objective function aims at minimizing a weighted sum of the number of aircraft routes, the number of crew pairings, and the waiting times of crews between consecutive flights. In addition, it aims at maximizing the robustness of the solution by also minimizing the number of times that crews need to change aircraft. We present two mixed integer linear programming models for the integrated problem. The first formulation, called the path-path model, can be considered as the “natural model” in which both the crew pairings and the aircraft routes are represented by path-based variables. The other formulation, called the arc-path model, is a novel model in which the aircraft routes are represented by arc-based variables and the crew pairings by path-based variables. We propose two exact methods (called path-path method and arc-path method) for solving the integrated problem, each one based on one of the proposed models. Both methods consist of three phases. In the first phase, the linear programming relaxation of the corresponding model is solved to optimality by column generation on the path-based variables, thus providing a lower bound. The second phase computes a heuristic solution (upper bound) by using o…

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(1998) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(98)90683-6 · Cited by 350
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Authors: Cacchiani, V., & Salazar-González, J.-J.
Affiliations: University of Bologna (IT)
DMEIO, Universidad de La Laguna, 38271 La Laguna, Tenerife, Spain
Corresponding author: Cacchiani, Valentina
ORCID: Cacchiani, Valentina
Venue: Transportation Science
Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
Published: 2017-02
Volume: 51
Issue: 1
Pages: 250-268
ISSN: 0041-1655 (ISSN-L)
1526-5447
DOI: 10.1287/trsc.2015.0655
OpenAlex: W2331684445
Cited by: 64
References: 30
Citations by year: 2026: 3 · 2025: 7 · 2024: 9 · 2023: 10 · 2022: 4 · 2021: 7 · 2020: 9 · 2019: 5 · 2018: 6 · 2017: 2
FWCI: 6.11
Type: journal article
Language: en
Open access: green
hdl.handle.net
Licence: cc-by-nc
Locations: Transportation Science (publishedVersion)
Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, cc-by-nc)
Lancaster EPrints (Lancaster University) (submittedVersion)
Topics: Physical Sciences › Engineering › Aerospace Engineering › Air Traffic Management and Optimization
Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Vehicle Routing Optimization Methods
Social Sciences › Decision Sciences › Management Science and Operations Research › Scheduling and Timetabling Solutions
Keywords: airline scheduling, integrated scheduling, fleet assignment, aircraft routing, crew pairing, aircraft maintenance, mixed-integer linear programming, column generation

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Optimal Solutions to a Real-World Integrated Airline Scheduling Problem

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