Abstract
Airline crew scheduling is a complex problem faced by airlines that assigns crew members into pairing schedules. The crew pairing problem involves a sequence of flight legs of the same fleet which begins and ends at the same crew base location. Also, airline transportation has to deal with disruptions and unpredicted situations caused by technical problems or weather conditions that affect crew pairing schedules and lead to delays between different resources such as aircraft and crews. Moreover, this leads to increased extra expenses. Airlines must have recovery plans in place to deal with these unexpected occurrences. However, airline crew scheduling cannot be effective without high quality flight pairings. Therefore, this thesis focuses on crew pairing problems that affect crew cost by addressing the deterministic crew pairing and the stochastic crew pairing problem. We propose novel models and solution methods to solve these problems.In this research, we develop an optimisation model based on a set partitioning model with the main objective to minimise total flying time of crew pairing schedules in order to maximise reduction of crew cost. Moreover, we propose heuristic and metaheuristic methods combined with biased randomisation technique to solve the deterministic crew pairing problem. These include Biased Randomised Iterated Greedy, Biased Randomised Iterated Greedy with Local Search and Biased Randomised Variable Neighbourhood Search.
Furthermore, we develop a stochastic optimisation model to handle delays and minimise crew swap flights and sim-optimisation methods that combine Monte-Carlo Simulation with biased randomisation methods to handle the delays, including Sim-Biased Randomised Iterated Greedy, Sim-Biased Randomised Iterated Greedy with Local Search, and Sim-Biased Randomised Variable Neighbourhood Search.
To the best of our knowledge, this is the first time that the proposed methods have been used to solve the deterministic and stochastic crew scheduling problems.
Extensive experiments were conducted to evaluate the performance of the proposed solution methods using reference problem from the literature and a real-life case study from an Airline company in Thailand. Our proposed methods outperformed results in the literature and the solution adopted by the Airline Company in Thailand.
| Date of Award | May 2019 |
|---|---|
| Original language | English |
| Supervisor | Djamila Ouelhadj (Supervisor) & Banafsheh Khosravi (Supervisor) |