Robust resource planning for aircraft ground operations

Yagmur S. Gök; Daniel Guimarans; Peter J. Stuckey; Maurizio Tomasella; Cemalettin Ozturk

doi:10.1007/978-3-030-58942-4_15

Robust resource planning for aircraft ground operations

Yagmur S. Gök, Daniel Guimarans, Peter J. Stuckey, Maurizio Tomasella, Cemalettin Ozturk

Department of Data Science & AI

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

10 Citations (Scopus)

Abstract

Aircraft turnaround scheduling and airport ground services team/equipment planning directly concern both the airport operator and service providers. We first ensure airport-wide global optimality by solving a resource-constrained project scheduling problem (RCPSP) for minimal overall delays. We then support decentralized allocation of teams/vehicles to flights, independently by each service provider. Either a multiple traveling salesman problem with time-windows (mTSPTW), or a vehicle routing problem with time-windows (VRPTW) are solved for this purpose, by taking advantage of both constraint programming (CP) and mixed integer programming (MIP) solvers. We also exploit these models in a matheuristic approach based on large neighborhood search used to reach good solutions in reasonable time for real-world instances. Unlike the classical VRP objective of minimizing traveling time, we maximize the total slack time between team visits, and show that doing this fosters robustness of the generated plans. We assess the robustness of solutions through a discrete-event simulation model, and conclude by validating our approach with data provided by a major ground handling company for a day of operations at Barcelona El Prat Airport.

Original language	English
Title of host publication	Integration of Constraint Programming, Artificial Intelligence, and Operations Research
Subtitle of host publication	17th International Conference, CPAIOR 2020 Vienna, Austria, September 21–24, 2020 Proceedings
Editors	Emmanuel Hebrard, Nysret Musliu
Place of Publication	Cham Switzerland
Publisher	Springer
Pages	222-238
Number of pages	17
ISBN (Electronic)	9783030589424
ISBN (Print)	9783030589417
DOIs	https://doi.org/10.1007/978-3-030-58942-4_15
Publication status	Published - 2020
Event	International Conference on Integration of Artificial Intelligence and Operations Research Techniques in Constraint Programming for Combinatorial Optimization Problems 2020 - Vienna, Austria Duration: 21 Sept 2020 → 24 Sept 2020 Conference number: 17th https://link.springer.com/book/10.1007/978-3-030-58942-4 (Proceedings) https://cpaior2020.dbai.tuwien.ac.at (Website)

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	12296
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Integration of Artificial Intelligence and Operations Research Techniques in Constraint Programming for Combinatorial Optimization Problems 2020
Abbreviated title	CPAIOR 2020
Country/Territory	Austria
City	Vienna
Period	21/09/20 → 24/09/20
Internet address	https://link.springer.com/book/10.1007/978-3-030-58942-4 (Proceedings) https://cpaior2020.dbai.tuwien.ac.at (Website)

Keywords

Airport operations
Aviation
Optimization
Routing
Scheduling

Access to Document

10.1007/978-3-030-58942-4_15

Cite this

Gök, Y. S., Guimarans, D., Stuckey, P. J., Tomasella, M., & Ozturk, C. (2020). Robust resource planning for aircraft ground operations. In E. Hebrard, & N. Musliu (Eds.), Integration of Constraint Programming, Artificial Intelligence, and Operations Research: 17th International Conference, CPAIOR 2020 Vienna, Austria, September 21–24, 2020 Proceedings (pp. 222-238). (Lecture Notes in Computer Science ; Vol. 12296 ). Springer. https://doi.org/10.1007/978-3-030-58942-4_15

Gök, Yagmur S. ; Guimarans, Daniel ; Stuckey, Peter J. et al. / Robust resource planning for aircraft ground operations. Integration of Constraint Programming, Artificial Intelligence, and Operations Research: 17th International Conference, CPAIOR 2020 Vienna, Austria, September 21–24, 2020 Proceedings. editor / Emmanuel Hebrard ; Nysret Musliu. Cham Switzerland : Springer, 2020. pp. 222-238 (Lecture Notes in Computer Science ).

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abstract = "Aircraft turnaround scheduling and airport ground services team/equipment planning directly concern both the airport operator and service providers. We first ensure airport-wide global optimality by solving a resource-constrained project scheduling problem (RCPSP) for minimal overall delays. We then support decentralized allocation of teams/vehicles to flights, independently by each service provider. Either a multiple traveling salesman problem with time-windows (mTSPTW), or a vehicle routing problem with time-windows (VRPTW) are solved for this purpose, by taking advantage of both constraint programming (CP) and mixed integer programming (MIP) solvers. We also exploit these models in a matheuristic approach based on large neighborhood search used to reach good solutions in reasonable time for real-world instances. Unlike the classical VRP objective of minimizing traveling time, we maximize the total slack time between team visits, and show that doing this fosters robustness of the generated plans. We assess the robustness of solutions through a discrete-event simulation model, and conclude by validating our approach with data provided by a major ground handling company for a day of operations at Barcelona El Prat Airport.",

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Gök, YS, Guimarans, D, Stuckey, PJ, Tomasella, M & Ozturk, C 2020, Robust resource planning for aircraft ground operations. in E Hebrard & N Musliu (eds), Integration of Constraint Programming, Artificial Intelligence, and Operations Research: 17th International Conference, CPAIOR 2020 Vienna, Austria, September 21–24, 2020 Proceedings. Lecture Notes in Computer Science , vol. 12296 , Springer, Cham Switzerland , pp. 222-238, International Conference on Integration of Artificial Intelligence and Operations Research Techniques in Constraint Programming for Combinatorial Optimization Problems 2020, Vienna, Austria, 21/09/20. https://doi.org/10.1007/978-3-030-58942-4_15

Robust resource planning for aircraft ground operations. / Gök, Yagmur S.; Guimarans, Daniel; Stuckey, Peter J. et al.
Integration of Constraint Programming, Artificial Intelligence, and Operations Research: 17th International Conference, CPAIOR 2020 Vienna, Austria, September 21–24, 2020 Proceedings. ed. / Emmanuel Hebrard; Nysret Musliu. Cham Switzerland : Springer, 2020. p. 222-238 (Lecture Notes in Computer Science ; Vol. 12296 ).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - Robust resource planning for aircraft ground operations

AU - Gök, Yagmur S.

AU - Guimarans, Daniel

AU - Stuckey, Peter J.

AU - Tomasella, Maurizio

AU - Ozturk, Cemalettin

N1 - Conference code: 17th

PY - 2020

Y1 - 2020

N2 - Aircraft turnaround scheduling and airport ground services team/equipment planning directly concern both the airport operator and service providers. We first ensure airport-wide global optimality by solving a resource-constrained project scheduling problem (RCPSP) for minimal overall delays. We then support decentralized allocation of teams/vehicles to flights, independently by each service provider. Either a multiple traveling salesman problem with time-windows (mTSPTW), or a vehicle routing problem with time-windows (VRPTW) are solved for this purpose, by taking advantage of both constraint programming (CP) and mixed integer programming (MIP) solvers. We also exploit these models in a matheuristic approach based on large neighborhood search used to reach good solutions in reasonable time for real-world instances. Unlike the classical VRP objective of minimizing traveling time, we maximize the total slack time between team visits, and show that doing this fosters robustness of the generated plans. We assess the robustness of solutions through a discrete-event simulation model, and conclude by validating our approach with data provided by a major ground handling company for a day of operations at Barcelona El Prat Airport.

AB - Aircraft turnaround scheduling and airport ground services team/equipment planning directly concern both the airport operator and service providers. We first ensure airport-wide global optimality by solving a resource-constrained project scheduling problem (RCPSP) for minimal overall delays. We then support decentralized allocation of teams/vehicles to flights, independently by each service provider. Either a multiple traveling salesman problem with time-windows (mTSPTW), or a vehicle routing problem with time-windows (VRPTW) are solved for this purpose, by taking advantage of both constraint programming (CP) and mixed integer programming (MIP) solvers. We also exploit these models in a matheuristic approach based on large neighborhood search used to reach good solutions in reasonable time for real-world instances. Unlike the classical VRP objective of minimizing traveling time, we maximize the total slack time between team visits, and show that doing this fosters robustness of the generated plans. We assess the robustness of solutions through a discrete-event simulation model, and conclude by validating our approach with data provided by a major ground handling company for a day of operations at Barcelona El Prat Airport.

KW - Airport operations

KW - Aviation

KW - Optimization

KW - Routing

KW - Scheduling

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BT - Integration of Constraint Programming, Artificial Intelligence, and Operations Research

A2 - Hebrard, Emmanuel

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PB - Springer

CY - Cham Switzerland

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Gök YS, Guimarans D, Stuckey PJ, Tomasella M, Ozturk C. Robust resource planning for aircraft ground operations. In Hebrard E, Musliu N, editors, Integration of Constraint Programming, Artificial Intelligence, and Operations Research: 17th International Conference, CPAIOR 2020 Vienna, Austria, September 21–24, 2020 Proceedings. Cham Switzerland : Springer. 2020. p. 222-238. (Lecture Notes in Computer Science ). doi: 10.1007/978-3-030-58942-4_15

Robust resource planning for aircraft ground operations

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Keywords

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