Investigation of hidden markov model for the tuning of metaheuristics in airline scheduling problems

Aoun, Oussama; Sarhani, Malek; Afia, Abdellatif El

doi:10.1016/j.ifacol.2016.07.058

Cited by 29 publications

(5 citation statements)

References 12 publications

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“…In 2006, Barry C. Smith [14] proposed a robust aircraft type assignment model from the perspective of airport purity, which required that the airports involved in a certain aircraft type be concentrated as much as possible. Oussama Aoun [15] used a hidden Markov model to estimate the most likely state to achieve the optimal allocation of crew members, which is an optimization of the subsequent problem of aircraft type assignment. Domestically, Zhu Xinghui [16] drew on Barry C. Smith's theory and established a robust aircraft type assignment model based on flight purity.…”

Section: Model Assignmentmentioning

confidence: 99%

Overview of Flight Planning Optimization Research

Long,

Wen,

Qiang

2022

VFC

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From the analysis of different time stages of flight planning optimization, flight planning optimization can be divided into three aspects: static flight planning optimization, flight planning dynamic feedback optimization based on flight delay prediction, and flight planning dynamic adjustment based on airport collaborative decision -making (A-CDM). Then, it analyzes the static scheduling and optimization of flight plans from the preparation links of flight schedules, aircraft type assignments, and flight frequencies; then uses the optimization method of delay spread prediction and data mining prediction to analyze the correlation of flight plan dynamic feedback optimization based on flight delay prediction the study. Finally, according to the complexity analysis of flight plan optimization, the development trend and future research directions of flight plan optimization are given.

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Section: Model Assignmentmentioning

confidence: 99%

Overview of Flight Planning Optimization Research

Long,

Wen,

Qiang

2022

VFC

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“…Fourthly, when looking at model selection and the performance of algorithms, there are techniques used to tune parameters such as Fuzzy Logic controller for Ant Colony System (ACS) epsilon parameter [12]. Also, [13] and [14] used Hidden Markov Model (HMM) algorithm to tune the Particle Swarm optimization population size and acceleration factors parameters.…”

Section: Introductionmentioning

confidence: 99%

Adapted Branch-and-Bound Algorithm Using SVM With Model Selection

Kabbaj¹,

Abdellatif²

2019

IJECE

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Branch-and-Bound algorithm is the basis for the majority of solving methods in mixed integer linear programming. It has been proving its efficiency in different fields. In fact, it creates little by little a tree of nodes by adopting two strategies. These strategies are variable selection strategy and node selection strategy. In our previous work, we experienced a methodology of learning branch-and-bound strategies using regression-based support vector machine twice. That methodology allowed firstly to exploit information from previous executions of Branch-and-Bound algorithm on other instances. Secondly, it created information channel between node selection strategy and variable branching strategy. And thirdly, it gave good results in term of running time comparing to standard Branch-and-Bound algorithm. In this work, we will focus on increasing SVM performance by using cross validation coupled with model selection.

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“…HMMs success is due to ability to deal with the variability by means of stochastic modeling. It was used to enhance the behavior of metaheuristics by estimating their best configuration [19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

A Self-Tuned Simulated Annealing Algorithm Using Hidden Markov Model

Limam¹,

Afia²,

Chiheb³

2018

IJECE

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Simulated Annealing algorithm (SA) is a well-known probabilistic heuristic. It mimics the annealing process in metallurgy to approximate the global minimum of an optimization problem. The SA has many parameters which need to be tuned manually when applied to a specific problem. The tuning may be difficult and time-consuming. This paper aims to overcome this difficulty by using a self-tuning approach based on a machine learning algorithm called Hidden Markov Model (HMM). The main idea is allowing the SA to adapt his own cooling law at each iteration, according to the search history. An experiment was performed on many benchmark functions to show the efficiency of this approach compared to the classical one.

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Investigation of hidden markov model for the tuning of metaheuristics in airline scheduling problems

Cited by 29 publications

References 12 publications

Overview of Flight Planning Optimization Research

Overview of Flight Planning Optimization Research

Adapted Branch-and-Bound Algorithm Using SVM With Model Selection

A Self-Tuned Simulated Annealing Algorithm Using Hidden Markov Model

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