Agent-Based Distributed Control for Improving Complex Resource Scheduling: Application to Airport Ground Handling Operations

Ansola, Pablo García; Higuera, Andrés García; Otamendi, F. Javier; Morenas, Javier de las

doi:10.1109/jsyst.2013.2272248

Cited by 17 publications

(3 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…But building a knowledge base of human and material resources is limited to but not excluded from all content except information disclosure. Information can be published in a fixed format that computers can understand [10]. Machine learning works by computing the correlation between a set of features derived from these data and its predictions, training data.…”

Section: Tensorflow Deep Learning Frameworkmentioning

confidence: 99%

A Resource Scheduling Method for Enterprise Management Based on Artificial Intelligence Deep Learning

Zhu

Huang

2022

Mobile Information Systems

View full text Add to dashboard Cite

Under the current trend of economic globalization and international competition, more and more production enterprises are introducing the project management model, that is, customer or order projection, and using the concept of project management to manage operations. In an enterprise, there are multiple customers at the same time, and the production line can also generate multiple orders from different customers at the same time. That is to say, multiple projects are running at the same time, resulting in continuous changes in management processes, heavy project coordination tasks, and a serious waste of corporate resources. The resources that can be used are limited. In the production business using project management, how to rationally utilize the limited business scheduling resources has become the focus of research. Aiming at the method of business management resource scheduling, this paper investigates the application of enhanced particle swarm algorithm on the cloud deep learning platform, and the mapping problem between virtual machines and physical machines. As a heuristic algorithm, particle swarm optimization is suitable for solving combinatorial optimization problems. By improving the diversity of particle hosts and setting parameters, it improves the integration speed and accuracy of the algorithm. Through the analysis of the current situation of multi-project business management, it examines the allocation of resources. In terms of business management, it has established a resource allocation model for multiple projects. The results show that the method improves the efficiency of the enterprise by 35% compared to the traditional method with a 20% reduction in personnel. A better configuration scheme is simulated by MATLAB, which verifies the scientificity and effectiveness of the method studied in this paper.

show abstract

Section: Tensorflow Deep Learning Frameworkmentioning

confidence: 99%

A Resource Scheduling Method for Enterprise Management Based on Artificial Intelligence Deep Learning

Zhu

Huang

2022

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…In addition, their spatial diversity provides added capabilities for tasks like formation-keeping [1][2][3], escorting or guarding [4,5], surveillance and feature tracking [6][7][8][9], object manipulation [10], and more specialized tasks such as automatic lighting [11] reconfigurable sparse antenna arrays [12], and minimally invasive surgery [13,14]. New research is developing techniques for diverse task-oriented groups of robots to work together to perform broader and more sophisticated missions [15,16]; as the mission evolves, the tasks may be preserved even though the environment, the performance objectives, and the assignment of robots may change.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Layer, Multi-Robot Control Architecture for Long-Range, Dynamic Communication Links

Shepard,

Kitts

2023

Multi-Robot Systems - New Advances

View full text Add to dashboard Cite

A unified motion control architecture is presented for dynamic, long-range multi-robot communications networks, incorporating task abstraction that disassociates goals from implementation. In the task space, communication link states are specified, directly measured, and explicitly controlled yielding well-behaved task state trajectories. The control architecture uses task-level compensation to generate multi-robot formation mobility commands, and a cluster space controller transforms those formation commands to mobility commands for individual robots. The number of robots are selected to meet communications requirements and controlled through a multi-task coordination capability incorporated within the architecture. Robustness to performance commands, system configuration parameters, and external disturbances is demonstrated through a variety of simulations and experiments. These show how robots are dynamically positioned and switched into or out of operation in order to meet communications requirements.

show abstract

“…At present, the optimization of airport ground handling processes has become a research hotspot [2,3]. The optimal scheduling and allocation of various ground resources of airport including support vehicles, gates and runways have been studied, such as ground support vehicle scheduling problem [4], gate assignment problem [5,6] and runway assignment problem [7,8].…”

Section: Introductionmentioning

confidence: 99%

Bi-Objective Collaborative Scheduling Optimization of Airport Ferry Vehicle and Tractor

Zhao¹,

Gao²,

Han³

et al. 2019

Int. j. simul. model.

View full text Add to dashboard Cite

With the continuous growth of aviation business, the flight ground support capability of airport is facing great challenges. The resources of ferry vehicle and tractor are important factors that restrict the flight service level of the airport. This paper analyses the collaborative scheduling of airport ferry vehicle and tractor through innovatively constructing a bi-objective mixed integer programming model, one objective is to minimize the number of ferry vehicles and tractors, and the other is to balance the vehicle usage. To deal with this problem, two methods based on standard particle swarm optimization are adopted: the lexicographic method and Pareto method, and virtual flights are introduced for the convenience of particle coding. The effectiveness and comparison of two methods are illustrated by employing the real flight data of Beijing Capital International Airport. The results of this study may provide reference for the evaluation and optimization of the airport ground support vehicles.

show abstract

Agent-Based Distributed Control for Improving Complex Resource Scheduling: Application to Airport Ground Handling Operations

Cited by 17 publications

References 32 publications

A Resource Scheduling Method for Enterprise Management Based on Artificial Intelligence Deep Learning

A Resource Scheduling Method for Enterprise Management Based on Artificial Intelligence Deep Learning

A Multi-Layer, Multi-Robot Control Architecture for Long-Range, Dynamic Communication Links

Bi-Objective Collaborative Scheduling Optimization of Airport Ferry Vehicle and Tractor

Contact Info

Product

Resources

About