|Tackling aircraft routing uncertainties : adjustable cruise speed and fuzzy modelling approach
|Chung, S. H. Nick (ISE)
Chan, T. S. Felix (ISE)
|Airlines -- Management
Airplanes -- Maintenance and repair
Hong Kong Polytechnic University -- Dissertations
|Department of Industrial and Systems Engineering
|xiii, 182 pages : color illustrations
|Aircraft maintenance routing problem (AMRP) is crucial for airline planning due to its significant impact on aircraft utilization and schedule reliability. It is known that allowing flight flying time variability in aircraft re-routing can achieve improved flight connection opportunities, thus higher aircraft utilization and enhanced schedule flexibility. However, the similar impact on aircraft routing is under-explored. In this research, we develop a new AMRP model that incorporates cruise speed control to take advantage of flexibility in fight flying times. In the proposed model, each flight leg is assigned a cruise time window where several leg copies with different cruise times are placed and only one copy can be selected by one flight leg. The objective function is to minimize the sum of aircraft usage costs, idle time costs and fuel-burn related costs so that a critical trade-off between the aircraft utilization and fuel-burn related costs can be examined.
However, the combination of two intricate sets of decisions, i.e., cruise time for each flight leg and maintenance route for each aircraft, poses significant methodological challenges. To solve the problem efficiently, we first develop an improved ant colony optimization (IACO) algorithm with a new state transition mechanism and a new pheromone updating mechanism to enhance the search efficiency and precision. Then, we propose a matheuristic approach which is comprised of three components: the IACO algorithm, the set partitioning (SP) procedure and the neighborhood search (NS) procedure. The IACO algorithm serves as a route generator, populating a pool of routes with promising feasible aircraft maintenance routes. A SP model, which features the high-quality columns corresponding to the routes in the pool, is solved to produce a possible better solution. This solution is further improved by a NS procedure that iteratively solves the reduced instances to optimality.
Despite the extensive studies in the operational side of AMRP, robust AMRP (RAMRP) attracts more attention due to the prevalent and costly disruptions in operating environment. However, most studies focus on aircraft routing while the maintenance regulations are either disregarded or used as constraints. In fact, aircraft maintenance is an important source of disruption. Especially in current practice, a maintenance A-check program is divided into multiple task packages, each with varying tasks and durations, complicating its operation and thereby increasing the disruption risk. To alleviate the impact of maintenance disruption, we first accurately assess the disruption risk for each task package using fuzzy logic approach due to the different levels of risk associated with each package. Then, based on the assessment results, a new robustness enhancement strategy is developed, of which the core idea is to identify the appropriate buffer time allocation for task packages. Besides, a robustness measurement, namely the total risk score, is proposed to construct a new RAMRP model. Finally, a matheuristic approach is developed to effectively solve the RAMRP model.
|All rights reserved
As a bona fide Library user, I declare that:
- I will abide by the rules and legal ordinances governing copyright regarding the use of the Database.
- I will use the Database for the purpose of my research or private study only and not for circulation or further reproduction or any other purpose.
- I agree to indemnify and hold the University harmless from and against any loss, damage, cost, liability or expenses arising from copyright infringement or unauthorized usage.
By downloading any item(s) listed above, you acknowledge that you have read and understood the copyright undertaking as stated above, and agree to be bound by all of its terms.
Please use this identifier to cite or link to this item: