Efficient ground handling at airports greatly adds to the performance of the entire air transportation network. In this network, airports are connected via aircraft that rely on passenger and crew connections, successful local airport operations, and efficient ground handling resource management. In addition, airport stakeholders’ decision-making processes must take into account various time scales (look-ahead times), process estimates, and both limited and multiple-dependent solution spaces. Most airlines have created integrated hub and operations control centers to monitor and adapt tactical operations. Despite this, decisions in such control centers should be made quickly in case of disruption. The decisions should also include the interests of various airline departments and local stakeholders.
Taking into account the Airport Collaborative Decision Making (A-CDM) concept, the joint venture between Airports Council International Europe (ACI EUROPE) - European Organization for the Safety of Air Navigation (EUROCONTROL) - International Air Transport Association (IATA) - Civil Air Navigation Services Organization (CANSO), this study creates different tools to manage turnaround in normal and disrupted contexts, hence facilitating decision-making in an Airport Operations Control Center (AOCC) and a Hub Control Center (HCC). This research focuses on the airline role in the collaborative decision-making process.
Regarding A-CDM milestones, turnaround time estimation is computed by four modeling methodologies, namely Critical Path Method (CPM), Project Evaluation and Review Technique (PERT), Fuzzy Critical Path Method (FCPM), and Analytical Convolution in deterministic and nondeterministic domains. In addition, the study develops mathematical models to return the airline schedule to its original plan in the event of delays. Chance-constrained and Robust optimization are also created for optimal decision-making when airlines confront uncertainty during real-world operations.
The study also develops a novel Hybrid Shuffled Frog-Leaping Algorithm (SFLA)-Grasshopper Optimization Algorithm (GOA) to expedite the process of finding recovery solutions, allowing AOCC and HCC for real-time applications to send this information to the relevant departments.
In comparison to common linear solvers, the solution process is sped up by 18 percent and the quality of the solutions is enhanced by 24 percent on average. Initial results are generated in less than 2 minutes, and global optimal results are achieved in near 15 minutes allowing the system to be applied in real-time applications.:Abstract
1 Introduction
1.1 Problem Description
1.1.1 Decision Scope
1.1.2 Airport Collaborative Decision Making (A-CDM)
1.1.3 Total Airport Management
1.1.4 Ground Handlers
1.1.5 Turnaround Management
1.2 Aims and Objectives
1.3 Thesis Contribution
1.4 Structure
2 Literature Review
2.1 Turnaround
2.2 Ground Handling
2.3 Flights and Networks
2.4 Apron and Gate Assignment
2.5 Scopes Combination
2.5.1 Gate Assignment and Turnaround
2.5.2 Gate Assignment and Flights
2.5.3 Gate Assignment and Ground Handling
2.5.4 Turnaround and Flights
2.5.5 Turnaround and Ground Handling
2.5.6 Flights and Ground Handling
2.6 Turnaround Operations
2.7 Conclusion
3 Turnaround Definition
3.1 Turnaround in A-CDM System
3.2 Turnaround and Ground Handling
3.3 Turnaround Operations
3.3.1 In-Block (INB) and Acceptance (ACC)
3.3.2 Deboarding (DEB) and Boarding (BOA)
3.3.3 Fueling (FUE)
3.3.4 Catering (CAT)
3.3.5 Cleaning (CLE)
3.3.6 Unloading (UNL) and Loading (LOA)
3.3.7 Water service (WAT) and Toilette (TOI)
3.3.8 Finalization (FIN)
4 Total Turnaround Time (TTT) Calculation
4.1 Critical Path Method (CPM)
4.2 Project Evaluation and Review Technique (PERT)
4.3 Fuzzy Critical Path Method (FCPM)
4.3.1 Fuzzy Numbers and Fuzzy Sets
4.3.2 Fuzzy Membership Functions of Turnaround Tasks
4.3.3 Probability-possibility Transformation of Turnaround Tasks
4.3.4 Fuzzy Critical Path Method (FCPM) in Total Turnaround Time (TTT) Calculation
4.3.5 Discussion
4.4 Analytical Convolution
4.4.1 Convolution Method
4.4.2 Monte Carlo (MC) Simulation Evaluation
4.4.3 Application of Convolution in Turnaround Control
5 Disruption Management
5.1 Airline Disruption Management
5.1.1 Airport Operations Control Center (AOCC)
5.1.2 Delay in the Airline Networks
5.1.3 Recovery Options
5.2 Deterministic Model
5.2.1 Mathematical Model
5.2.2 Solution Approaches
5.2.3 Problem Setting
5.3 Non Deterministic Model
5.3.1 Stochastic Arrivals
5.3.2 Stochastic Duration
6 Conclusion
6.1 Discussion around Research Questions
6.1.1 Integration of All Actors
6.1.2 Turnaround Time Prediction
6.1.3 Quick and Robust Reaction
6.2 Future Research
6.2.1 Scope Development
6.2.2 Algorithm Development
6.2.3 Parameter Development
List of Acronyms
List of Figures
List of Tables
Bibliography
Acknowledgement
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:90860 |
| Date | 15 April 2024 |
| Creators | Asadi, Ehsan |
| Contributors | Fricke, Hartmut, Kolisch, Rainer, Technische Universität Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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