Reducing airport emissions with coordinated pushback processes: A case study

Branko Bubalo, Frederik Schulte*, Stefan Voß

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review


Empirical research has shown that airside ground operations imply a significant percentage of overall airport-related emissions. Among those operations, taxiing is one of the most emission-intensive processes, directly related to the initial pushback process that has a significant impact on the taxiing duration of departing flights. Possible approaches for an effective management of pushbacks at an airport are simulation and optimization models. Airside operations at major airports involve a complex interplay of many operations and parties and therefore need to be planned in a coordinated fashion. Yet, existing approaches have not been applied in a comprehensive planning environment for airside operations. In this work, we develop an algorithm-based relocation approach for pushback vehicles that enables an effective minimization of delays and emissions during the taxiing process. As a result alternative sequences of departing flights are evaluated against each other to find the ones with least total emissions and delay. These algorithms are applied in a simulation environment and evaluated against real-world cases. Preliminary results demonstrate that we are able to solve the underlying pushback routing problem in appropriate computational times for dynamic decision support needed at airports.

Original languageEnglish
Title of host publicationComputational Logistics - 8th International Conference, ICCL 2017, Proceedings
EditorsStefan VoB, Tolga Bektas, Stefano Coniglio, Antonio Martinez-Sykora
Number of pages15
ISBN (Print)9783319684956
Publication statusPublished - 1 Jan 2017
Externally publishedYes
Event8th International Conference on Computational Logistics, ICCL 2017 - Southampton, United Kingdom
Duration: 18 Oct 201720 Oct 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10572 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference8th International Conference on Computational Logistics, ICCL 2017
CountryUnited Kingdom


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