Optimization of short-haul aircraft schedule recovery problems using a hybrid multiobjective genetic algorithm

doi:10.1016/j.eswa.2009.07.068

Expert Systems with Applications

Volume 37, Issue 3, 15 March 2010, Pages 2307-2315

https://doi.org/10.1016/j.eswa.2009.07.068 Get rights and content

Abstract

A hybrid multiobjective genetic algorithm is presented in this paper to find an efficient solution for the daily short-haul aircraft schedule recovery problems which usually happen due to some disturbance events and require a time-sensitive solution to meet various hard constraints and soft objectives. The proposed algorithm employs an adaptive evaluated vector (AEV) to guide the solution search and uses the method of inequality-based multiobjective genetic algorithm to provide the multiobjective solution. A simulated disturbance experiment, temporal airport closure, is made and shown that the hybrid method can provide a very efficient short-haul schedule recovery solution under various performance indices.

Introduction

Aircraft schedule recovery problems arise when the original flight plan is disrupted by various occasional events, e.g. severe weather and mechanical problems, and should be rescheduled by delaying certain flights, swapping aircraft or taking other actions to minimize the negative effects. The operation coordinators who manage the daily airline situations have to carefully consider the practical conditions and try to make a suitable plan to narrow down the disturbance impact. Usually, a practical trade-off should be considered among the decision criteria (Luo & Yu, 1998) such as minimizing the number of delayed flights, minimizing the maximum delay, minimizing the total passenger delay, and so on. The difficulty for solving most of them is NP-complete, that is, no polynomial algorithms exist for the solutions.

Most studies for this kind of recovery problems only solve the objectives (or criteria) one by one or propagate a linear-sum on objectives to convert them into the single one. For example, Arguello, Jonathan, and Gang (1997) provided a greedy randomized adaptive search procedure (GRASP) which uses a randomized neighborhood search technique to reconstruct aircraft routings. Gu and Chung (1999) used a genetic algorithm to search the extra minimum delay time on the gate reassignments. Rosenberger, Johnson, and Nemhauser (2003) provided a set-packing model and solve it by an aircraft selection heuristic (ASH) method. Andersson (2006) presents two meta-heuristics, a tabu search and a simulated annealing approach, to solve the recovery problems efficiently. However, the single objective solution can not reflect the true cases with practical multiobjective needs, as stated by Fieldsend and Singh (2005).

Multiobjective evolutionary algorithms with global search capabilities have been successfully applied in aircraft schedule planning cases (Adachi et al., 2004, Chou et al., 2008). But, there are very few literature to study the multiobjective optimization problems of the aircraft schedule recovery. Although Lee, Lee, and Tan (2007) ever considered the schedule recovery problem under disturbance as a multiobjective optimization problem, and developed a multiobjective genetic algorithm (MOGA) to improve the robustness and operational cost of an existing flight schedule based on a simulation tool, SIMAIR 2.0, however, this approach consumes quite long computation time and may mainly be applicable for monthly schedules according to their conclusion. Clearly, it can not suit the short-haul recovery problems which require a short-time response in the daily work.

Summing up the above statements and reasons, the purpose of this paper is to formulate the daily short-haul recovery problems into multiobjective combinational optimization equations, and to develop a hybrid evolutionary algorithm which employs an adaptive evaluated vector (AEV) to guide the solution search and uses the method of inequality-based multiobjective genetic algorithm (MMGA) for globally searching for the Pareto solutions with regard to the performance. Besides, a simulated disturbance experiment, temporal airport closure, is made to validate the proposed approach.

Section snippets

Problem definition and formulation

The aircraft schedule recovery problem considered in this paper contains several hard and soft constraints. Flight connection is the main hard constraint which ensures the arrival airport does not have conflicts with the next flight departure airport. Turn-around period is another hard constraint which requests the legal minimization time gap between two adjacent flights of an aircraft. In addition, the soft constraints can be formulated into objectives which contain the minimal total delay

Solution by using hybrid MMGA (HMMGA)

Genetic algorithms, first introduced by Holland (Holland, 1975), were later improved by many researchers (Leung and Wang, 2001, Tsai et al., 2004) to serve as the global explorer for the single objective (Tsai, Chou, & Liu, 2006) or multiobjective optimization problems. For most well-designed multiobjective genetic algorithms (Chou et al., 2008, Deb et al., 2002, Horn et al., 1994, Zitzler and Thiele, 1999), Pareto-based or similar scenario guides the search for the variety and tries to explore

HMMGA algorithm

The flow chart of the hybrid algorithm is summarized in Fig. 1. Just like the general multiobjective genetic algorithm (MOGA), the evolutionary population should be operated by iterations through initialization, fitness computation, multiobjective evaluation, crossover to generate offspring, mutation and selection for elimination. However, the method used to measure objectives differs greatly from the general MOGA.

Case study

In this study, a larger disturbance event, airport temporal closure in the afternoon due to bad weather (which usually happens in the summer in Taiwan), is simulated by directly assigning a disruption time period on the flight schedule. The simulated scenario from a practice expects to prioritize the total flight delay time objective first and then measure the other objectives. This makes the recovery problem become more complex containing hard constraints, one prioritized objective and four

Experimental results

The customized parameters are chosen through repeating experiments for good performance: population size 100, off-spring size 80, crosser rate 1.0, mutation rate 0.3, and AEV separation ratio 0.8. For the hardware and software environment, the specifications are an Intel P4 3.2G CPU, 1G memory, XP SP2 OS and Dev-C++4.9.9.2 complier.

Conclusions

Aircraft recovery problems usually involve many practical objectives and should be solved within the short time limitations. Most multiobjective evolutionary algorithms mainly search for the Pareto solutions prior to considering performance constraints. Such methods may not fit the practical recovery needs especially when the response time is required to be short enough. Hence, a hybrid multiobjective genetic algorithm is proposed in this paper to provide an optimal or sub-optimal recovery

Acknowledgement

This work was supported by the National Science Council, Taiwan, Republic of China, under Grant numbers NSC 96-2221-E-327-027, NSC 96-2221-E-327-005-MY2, and NSC 96-2628-E-327-004-MY3.

References (21)

L.H. Lee et al.
A multiobjective genetic algorithm for robust flight scheduling using simulation
European Journal of Operational Research
(2007)
N. Adachi et al.
Application of genetic algorithm to flight schedule planning
Systems and Computers in Japan
(2004)
T. Andersson
Solving the flight perturbation problem with meta heuristics
Journal of Heuristics
(2006)
M.F. Arguello et al.
A grasp for aircraft routing in response to groundings and delays
Journal of Combinatorial Optimization
(1997)
Baker, J. E. (1987). Reducing bias and inefficiency in the selection algorithm. In Proceedings of the second...
T.Y. Chou et al.
Method of inequality-based multiobjective genetic algorithm for domestic daily aircraft routing
IEEE Transaction on System, Man, Cybernetic, Part A
(2008)
K. Deb et al.
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transaction on Evolution Computation
(2002)
J.E. Fieldsend et al.
Pareto evolutionary neural networks
IEEE Transaction on Neural Network
(2005)
D.E. Goldberg
Genetic algorithms in search optimization & machine learning
(1989)
Grefenstette, J., Gopal, R., Rosimaita, B., & Gucht, D. V. (1985). Genetic algorithms for the traveling salesman...

There are more references available in the full text version of this article.

Cited by (24)

The aircraft recovery problem: A systematic literature review
2023, EURO Journal on Transportation and Logistics
Events that cause disruptions to air company flight schedules transporting passengers and cargo are commonplace in practice, and their operational and financial consequences are often relevant. The aircraft recovery problem (ARP) consists of recovering the flight schedules lost due to such events, determining new flight departure times and possible flight cancellations, as well as revising routes for different aircraft. In this study, the main characteristics considered in ARP papers to make the problem as realistic as possible are identified and a systematic literature review is carried out based on seminal studies of the problem in the 1980s. The literature papers are reviewed in terms of the ARP variants studied, the objectives chosen to be optimized and the practical constraints most often considered in real applications, as well as the networks and mathematical formulations to represent the problem and the solution approaches used to deal with it. The aim is to present an up-to-date review of the state-of-the-art of the ARP and to identify possible literature gaps and interesting opportunities for future research on this problem. It is noteworthy that, compared to the simplicity of the first ARP studies, the studies of the following decades tended to involve more complex variants of the problem as a way to become more adherent to current practical environments, requiring more elaborate formulations and solution methods to be properly addressed.
A reinforcement learning approach for multi-fleet aircraft recovery under airline disruption
2022, Applied Soft Computing
Citation Excerpt :
These assumptions were used to reflect the situation that aircraft operation is disrupted by airport closures. In addition, several studies also adopted these assumptions to simplify the problem [39,43]. We added Assumptions (11) and (12) for the following three reasons.
An airline scheduler plans flight schedules with efficient resource utilization. However, unpredictable airline disruptions, such as temporary closures of an airports, cause schedule perturbations. Therefore, recovering disrupted flight schedules is essential for airlines. Many previous studies have relied on copies of flight arcs, which could affect the quality of solutions, and have not addressed the key measure of airlines’ on-time performance as their objective. To fill these research gaps, we propose Q-learning and Double Q-learning algorithms using the reinforcement learning approach for aircraft recovery to support airline operations. We present an artificial environment of daily flight schedules and the Markov decision process for aircraft recovery. The proposed approach is first compared with existing algorithms on the benchmark instance. In comparison with other algorithms, the developed Q-learning and Double Q-learning algorithms obtain high-quality solutions within the proper computation time. To verify that the proposed approach can be applicable to a real-world case and can adapt to realistic conditions, we employ a domestic flight schedule from one of the airlines in South Korea. We evaluate the reinforcement learning approach on a set of experiments carried out on real-world data. Computational experiments show that reinforcement learning algorithms recover disrupted flight schedules effectively, and that our approaches flexibly adapt to various objectives and realistic conditions.
Integrated recovery of aircraft and passengers with passengers’ willingness under various itinerary disruption situations
2021, Computers and Industrial Engineering
This paper aims to study the aircraft rerouting problem considering passengers’ recovery with willingness under various itinerary disruption situation, which breaks thorough the traditional study assuming the passenger obey the airline assignment. We study how passengers’ willingness is considered in the optimization problem of aircraft rerouting and passengers’ recovery to reduce the dual losses of airlines and passengers. We consider passengers’ willingness under various itinerary disruption situations, composed of delay itineraries and disrupted itineraries. Passengers’ willingness mainly includes staying within the original itineraries, endorsing, and refunding tickets. We first construct an integer programming model with the following two objectives: to minimize airline recovery cost and reduce passenger recovery loss, and bounded rationality is applied to reformulate the passengers’ recovery loss. We then design an effective heuristic combined with multi-directional and stochastic variable neighborhood search algorithm to solve the problem. Finally, experiments based on real data are performed to show the stability and efficiency of the algorithm in various situations. Outcomes can provide theoretical and practical support for airline operations to improve passengers’ satisfaction.
Airline disruption management: A literature review and practical challenges
2021, Computers and Operations Research
Citation Excerpt :
Here the model is tested on data from China Airlines. Liu et al. (2010) presented a hybrid heuristic that combined an adaptive evaluated vector (AEV) and an inequality-based multi-objective genetic algorithm (GA) formulation that was used to search for Pareto solutions to the daily short-haul recovery problems. The AEV was used to guide the search and the GA was to provide the multi-objective solution.
Airline disruption management is an active field of research. In recent years, there has been an increase in publications, in particular, in of works integrating two or more resources (i.e., aircraft, crew, passengers) in the recovery analysis. Given that more than 50% of the papers have been published after the last literature review paper (Clausen et al., 2010), this paper provides a critical review and classification of the literature between 2009 and 2018 regarding airline disruption management, including aircraft, crew, passenger, and integrated recovery. Furthermore, this paper discusses various ways to close the gap between the reality faced in Airline Operations Control Centers (AOCCs) and defines a set of potential future lines of research.
Review on meta-heuristics approaches for airside operation research
2018, Applied Soft Computing Journal
The number of publications related to airside operation research is increasing and gaining in popularity. This paper aims to provide researchers with a comprehensive and extensive overview of meta-heuristics application for aviation research, with a particular focus on the airside operations. The scope of airside operation research covers airspace and air traffic flow management, aircraft operation in the terminal manoeuvring area and surface traffic operation. Based on the recent publications related to airside operations, the meta-heuristics approach is a promising approach to enhance the computational efficiency and achieve higher applicable in various decisions in airside operations. However, the literature on airside operation research is quite disjointed and disparate. Therefore, a general taxonomy framework for the airside information system is proposed in order to classify the research systematically and expedites related research and development of engineering applications in the aviation industry. To the best of our knowledge, this is the first review of the field using the meta-heuristics approach. The prominent findings of recent publication and the directions of future research are addressed throughout the review and analysis of the relevant studies.
Multiple objective solution approaches for aircraft rerouting under the disruption of multi-aircraft
2017, Expert Systems with Applications
Citation Excerpt :
But, there are very few literatures to study the multi-objective optimization problems of the aircraft schedule recovery. Liu, Chen, and Chou (2010) constructed a multi-objective combinational optimization model formulation for daily short-haul recovery problems, and developed a hybrid evolutionary algorithm composed of an adaptive evaluated vector and the method of inequality-based multi-objective genetic algorithm. A simulated disturbance experiment of daily domestic airline plans in Taiwan with only 7 aircraft and 39 flights was tested.
This paper considers a multi-objective aircraft recovery problem for airline disruption. An integer programming formulation is first established based on connection network with three conflicting objectives, where the first objective minimizes the total deviation from original flight schedules, the second objective minimizes the maximal flight delay time, and the third objective minimizes the number of aircraft actually attended in swapping. Optimal analysis is provided for a small scale aircraft recovery problem with the last two objectives and then one polynomial time algorithm for aircraft recovery problem after the disruption to multi-aircraft in a fleet at an airport. One heuristic combined ε-constraints method and neighborhood search algorithm is designed for large scale disruption recovery problem. The results obtained from computational experiment illustrate effectiveness and efficiency of the heuristic. The outcome of this research could provide theoretical and practical supports for airlines to reduce flight delays.

View all citing articles on Scopus

View full text

Optimization of short-haul aircraft schedule recovery problems using a hybrid multiobjective genetic algorithm

Abstract

Introduction

Section snippets

Problem definition and formulation

Solution by using hybrid MMGA (HMMGA)

HMMGA algorithm

Case study

Experimental results

Conclusions

Acknowledgement

European Journal of Operational Research

Application of genetic algorithm to flight schedule planning

Systems and Computers in Japan

Solving the flight perturbation problem with meta heuristics

Journal of Heuristics

A grasp for aircraft routing in response to groundings and delays

Journal of Combinatorial Optimization

Method of inequality-based multiobjective genetic algorithm for domestic daily aircraft routing

IEEE Transaction on System, Man, Cybernetic, Part A

A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transaction on Evolution Computation

Pareto evolutionary neural networks

IEEE Transaction on Neural Network

Genetic algorithms in search optimization & machine learning