A Tabu-Based Multi-objective Particle Swarm Optimization for Irregular Flight Recovery Problem

Air transportation is eminent for its fast speed and low cargo damage rate among other ways. However, it is greatly limited by emergent factors like bad weather and current COVID-19 epidemic, where irregular flights may occur. Confronted with the negative impact caused by irregular flight, it is vital to rearrange the preceding schedule to reduce the cost. To solve this problem, first, we established a multi-objective model considering cost and crew satisfaction simultaneously. Secondly, due to the complexity of irregular flight recovery problem, we proposed a tabu-based multi-objective particle swarm optimization introducing the idea of tabu search. Thirdly, we devised an encoding scheme focusing on the characteristic of the problem. Finally, we verified the superiority of the tabu-based multi-objective particle swarm optimization through the comparison against MOPSO by the experiment based on real-world data. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

An Improved Particle Swarm Optimization Algorithm for Irregular Flight Recovery Problem

As with the rapid development of air transportation and potential uncertainties caused by abnormal weather and other emergencies, such as Covid-19, irregular flights may occur. Under this situation, how to reduce the negative impact on airlines, especially how to rearrange the crew for each aircraft, becomes an important problem. To solve this problem, firstly, we established the model by minimizing the cost of crew recovery with time-space constraints. Secondly, in view of the fact that crew recovery belongs to an NP-hard problem, we proposed an improved particle swarm optimization (PSO) with mutation and crossover mechanisms to avoid prematurity and local optima. Thirdly, we designed an encoding scheme based on the characteristics of the problem. Finally, to verify the effectiveness of the improved PSO, the variant and the original PSO are used for comparison. And the experimental results show that the performance of the improved PSO algorithm is significantly better than the comparison algorithms in the irregular flight recovery problem covered in this paper. © 2022, Springer Nature Switzerland AG.