Optimizing Meta-heuristics for the Time-Dependent TSP Applied to Air Travels

Duque, Diogo; Cruz, José Fernando A.; Cardoso, Henrique Lopes; Oliveira, Eugénio

doi:10.1007/978-3-030-03493-1_76

Cited by 2 publications

(3 citation statements)

References 13 publications

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“… Duque et al (2018) proposed an algorithm for the management of optimal routes that also used the TSP in addition to the ant colony optimization (ACO) strategy. The objective of this study was to determine the best flights for visiting certain cities.…”

Section: Related Workmentioning

confidence: 99%

COVID-19 spread algorithm in the international airport network-DetArpds

Guevara

Coronel

Maldonado

et al. 2023

PeerJ Computer Science

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Due to COVID-19, the spread of diseases through air transport has become an important issue for public health in countries globally. Moreover, mass transportation (such as air travel) was a fundamental reason why infections spread to all countries within weeks. In the last 2 years in this research area, many studies have applied machine learning methods to predict the spread of COVID-19 in different environments with optimal results. These studies have implemented algorithms, methods, techniques, and other statistical models to analyze the information in accuracy form. Accordingly, this study focuses on analyzing the spread of COVID-19 in the international airport network. Initially, we conducted a review of the technical literature on algorithms, techniques, and theorems for generating routes between two points, comprising an analysis of 80 scientific papers that were published in indexed journals between 2017 and 2021. Subsequently, we analyzed the international airport database and information on the spread of COVID-19 from 2020 to 2022 to develop an algorithm for determining airport routes and the prevention of disease spread (DetARPDS). The main objective of this computational algorithm is to generate the routes taken by people infected with COVID-19 who transited the international airport network. The DetARPDS algorithm uses graph theory to map the international airport network using geographic allocations to position each terminal (vertex), while the distance between terminals was calculated with the Euclidian distance. Additionally, the proposed algorithm employs the Dijkstra algorithm to generate route simulations from a starting point to a destination air terminal. The generated routes are then compared with chronological contagion information to determine whether they meet the temporality in the spread of the virus. Finally, the obtained results are presented achieving a high probability of 93.46% accuracy for determining the entire route of how the disease spreads. Above all, the results of the algorithm proposed improved different computational aspects, such as time processing and detection of airports with a high rate of infection concentration, in comparison with other similar studies shown in the literature review.

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Section: Related Workmentioning

confidence: 99%

COVID-19 spread algorithm in the international airport network-DetArpds

Guevara

Coronel

Maldonado

et al. 2023

PeerJ Computer Science

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“…Kiwi.com released a challenge in 2017, called Travelling Salesman Challenge [12]. This was a previous version of the current Kiwi.com challenge, Travelling Salesman Challenge 2.0, which is the subject of this study.…”

Section: B Optimisation In Air Travelmentioning

confidence: 99%

“…The main difference between the two challenges, is that while in the former version the traveller had to visit a number of cities, in the current version cities are divided into a number of areas from which exactly one city has to be visited. In [12], Simulated Annealing (SA), Ant Colony Optimisation (ACO), and a hybrid algorithm combining SA and ACO were applied to solve the problem on large instances up to 100 cities. Additionally, they parallelised the ACO algorithm and meta-optimised the parameters of SA and ACO algorithms with the aid of a genetic algorithm.…”

Section: B Optimisation In Air Travelmentioning

confidence: 99%

A Reinforcement Learning Hyper-heuristic for the optimisation of Flight Connections

Pylyavskyy

Kheiri

Ahmed

2020

2020 IEEE Congress on Evolutionary Computation (CEC)

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Many combinatorial computational problems have been effectively solved by means of hyper-heuristics. In this study, we focus on a problem proposed by Kiwi.com and solve this problem by implementing a Reinforcement Learning (RL) hyperheuristic algorithm. Kiwi.com proposed a real-world NP-hard minimisation problem associated with air travelling services. The problem shares some characteristics with several TSP variants, such as time-dependence and time-windows that make the problem more complex in comparison to the classical TSP. In this work, we evaluate our proposed RL method on kiwi.com problem and compare its results statistically with common random-based hyper-heuristic approaches. The empirical results show that RL method achieves the best performance between the tested selection hyper-heuristics. Another significant achievement of RL is that better solutions were found compared to the best known solutions in several problem instances.

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Optimizing Meta-heuristics for the Time-Dependent TSP Applied to Air Travels

Cited by 2 publications

References 13 publications

COVID-19 spread algorithm in the international airport network-DetArpds

COVID-19 spread algorithm in the international airport network-DetArpds

A Reinforcement Learning Hyper-heuristic for the optimisation of Flight Connections

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