Study on an Airport Gate Reassignment Method and Its Application

Li, Bo; Zhao, Huimin

doi:10.3390/sym9110258

Cited by 7 publications

(3 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traditionally, different classical algorithms have been developed to solve small allocation problems. However, by increasing the number of flights and gates, solving the problems will take exponentially in time for classical computers [40,41]. By utilizing the advantage of quantum computers, it is feasible to develop an efficient algorithm for NISQ devices that can be applied to airline management gate allocation systems in the near future to optimize combinations of gates and flights in a reasonable amount of time.…”

Section: Case Study 1: Flight Gate Allocationmentioning

confidence: 99%

Solving Multi-Coloring Combinatorial Optimization Problems Using Hybrid Quantum Algorithms

Oh,

Mohammadbagherpoor,

Dreher

et al. 2019

Preprint

View full text Add to dashboard Cite

The design of a good algorithm to solve NP-hard combinatorial approximation problems requires specific problem domain knowledge and often needs a trial-and-error problem solving approach. Graph coloring is one approach that can provide an efficient solution for certain types of combinatorial applications. Some optimization algorithms have been proposed to solve these type of multi-coloring graph problems such as flight scheduling, frequency allocation in networking, and register allocation. For many of these examples a simple searching method to find an optimal solution can be the best option. Although this approach may provide a path to a solution for small sized problems, the computation cost will increase exponentially as the graph size and the number of colors increase. To mitigate such intolerable overhead, we present a hybrid quantum computing approach for multi-coloring graph problems that utilizes the variational quantum eigensolver (VQE) and quantum approximate optimization algorithm (QAOA) techniques. Our approach transforms each combinatorial problem into a corresponding Ising model formulation by using the calculated Hamiltonian matrices that are fed into the VQE and QAOA algorithms to produce the best solutions in polynomial time. We have applied this approach to three combinatorial applications and the implementation results demonstrate that our hybrid approach can find an optimal solution for each problem. The proposed technique can be applicable for finding optimal solutions to other combinatorial problems in various fields.

show abstract

Section: Case Study 1: Flight Gate Allocationmentioning

confidence: 99%

Solving Multi-Coloring Combinatorial Optimization Problems Using Hybrid Quantum Algorithms

Oh,

Mohammadbagherpoor,

Dreher

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…With the rapid development of new computer and information technologies, the applications of the optimization theories and methods have become more and more widely applicable in a large number of fields. Many of the latest developments depend on the improvement and innovation of numerical techniques for calculating optimization problems, and these methods have become popular research topics at home and abroad [3][4][5][6]. Therefore, the new optimization theory and method are studied, which not only have important theoretical significance, but also have wide application value.…”

Section: Introductionmentioning

confidence: 99%

Study on an Adaptive Co-Evolutionary ACO Algorithm for Complex Optimization Problems

2018

Self Cite

View full text Add to dashboard Cite

The ant colony optimization (ACO) algorithm has the characteristics of positive feedback, essential parallelism, and global convergence, but it has the shortcomings of premature convergence and slow convergence speed. The co-evolutionary algorithm (CEA) emphasizes the existing interaction among different sub-populations, but it is overly formal, and does not form a very strict and unified definition. Therefore, a new adaptive co-evolutionary ant colony optimization (SCEACO) algorithm based on the complementary advantages and hybrid mechanism is proposed in this paper. Firstly, the pheromone update formula is improved and the pheromone range of the ACO algorithm is limited in order to achieve the adaptive update of the pheromone. The elitist strategy and co-evolutionary idea are used for reference, the symbiotic mechanism and hybrid mechanism are introduced to better utilize the advantages of the CEA and ACO. Then the multi-objective optimization problem is divided into several sub-problems, each sub-problem corresponds to one population. Each ant colony is divided into multiple sub-populations in a common search space, and each sub-population performs the search activity and pheromone updating strategy. The elitist strategy is used to retain the elitist individuals within the population and the min-max ant strategy is used to set pheromone concentration for each path. Next, the selection, crossover, and mutation operations of individuals are introduced to adaptively adjust the parameters and implement the information sharing of the population and the co-evolution. Finally, the gate assignment problem of a hub airport is selected to verify the optimization performance of the SCEACO algorithm. The experiment results show that the SCEACO algorithm can effectively solve the gate assignment problem of a hub airport and obtain the effective assignment result. The SCEACO algorithm improves the convergence speed, and enhances the local search ability and global search capability.

show abstract

“…It showed that the chaotic forecast method was an effective method for the traffic conflict flow [13]. Deng Zhongyi studied the chaotic forecast method and the applied study [14]. The author analyzed in detail the current commonly used chaotic forecast methods.…”

Section: Introductionmentioning

confidence: 99%

The Research on the Inventory Prediction in Supply Chain based on BP-GA Chaos Prediction Algorithm

Liu¹

2015

IJUNESST

View full text Add to dashboard Cite

show abstract

Study on an Airport Gate Reassignment Method and Its Application

Cited by 7 publications

References 50 publications

Solving Multi-Coloring Combinatorial Optimization Problems Using Hybrid Quantum Algorithms

Solving Multi-Coloring Combinatorial Optimization Problems Using Hybrid Quantum Algorithms

Study on an Adaptive Co-Evolutionary ACO Algorithm for Complex Optimization Problems

The Research on the Inventory Prediction in Supply Chain based on BP-GA Chaos Prediction Algorithm

Contact Info

Product

Resources

About