Recently, the paper "A Fast Aircraft Stand Allocation Algorithm Based on Progressive Metaheuristic Search," co-authored by Dr. Rui Rui Li, Chief Architect at the Genesis AI Lab of Futong Dongfang, Algorithm Engineer Xiaodong Wu, and experts from the Capital Airport, has been selected for the 2021 China Automation Conference (CAC2021) and is displayed on the conference's official website.

Screenshot of the CAC2021 official website

The CAC2021, themed "The 60th Anniversary of the Chinese Association of Automation and the 110th Anniversary of Xuesen Qian's Birth," will provide a high-end academic platform for global experts, scholars, and industry peers in the fields of automation, information, and intelligent science to showcase innovative achievements and look forward to future development. It aims to strengthen the integration of different disciplines and lead the development of automation, information, and intelligent science and technology.

01 A Fast Aircraft Stand Allocation Algorithm Based On Progressive Metaheuristic Search

With the rapid development of China's civil aviation industry, the number of aircraft takeoffs and landings has been increasing rapidly year by year. Due to the shortage of near-gate parking stands, the gate utilization rate is difficult to maintain. To enhance the passenger experience, airport management personnel need to optimize the aircraft stand allocation methods to improve the quality and efficiency of allocation. Currently, large airports have many business rules that change frequently, making it difficult for existing stand allocation algorithms to model dynamically and solve quickly.

In response to these issues, the paper "A Fast Aircraft Stand Allocation Algorithm Based on Progressive Metaheuristic Search" proposes a flexible dynamic constraint modeling method and defines a novel allocation satisfaction objective function that can significantly increase the gate utilization rate while maximizing compliance with the rules. To address the issue of low solution efficiency in stand allocation models, a progressive metaheuristic search method is proposed.

Additionally, the paper validates the effectiveness of the proposed method on simulation datasets from actual airports. Compared to optimization methods such as tabu search, simulated annealing, conventional metaheuristic search, and genetic algorithms, the progressive metaheuristic search algorithm achieves a higher gate utilization rate and allocation satisfaction within the same amount of time. Conversely, when the gate utilization rate and allocation satisfaction are the same, the progressive metaheuristic search algorithm is faster in finding solutions.

02 Application Case Of The Intelligent Resource Management System At The Capital Airport

In response to the business innovation and upgrade demands of the Capital Airport, Futong Dongfang has built an Intelligent Resource Management System (iRMS system) for it. On the basis of ensuring the safety and controllability of airport operations, the traditional airport ground resource optimization mechanism has been changed, and intelligent airport operation resources have been realized with full-process visualization and automated management in a complex environment with many conflicting rules. The paper features a personalized design for the Capital Airport, employing a fast aircraft stand allocation algorithm based on progressive metaheuristic search. This algorithm comprehensively integrates 179 rules for stand and gate allocation, 161 rules for check-in counter allocation, and 66 rules for baggage carousel allocation. It enables intelligent linked allocation of stands, gates, duty counters, and baggage carousels within 180 seconds for over 1000 flights per day, resulting in a 3%-5% increase in the usage rate of passenger airbridges.

Based on AI technology combined with the operational habits of resource allocators, personalized settings are achieved to realize the intelligent automatic allocation of resources, significantly reducing the allocation time. By introducing 24 types of collaborative decision-making data such as the number of passengers on the flight, arrival and departure times, and on-block and off-block times, dynamic adjustments to resource allocation are assisted, achieving real-time optimization of ground resources, improving operational efficiency and passenger satisfaction, and providing strong support for real-time decision-making and safe operation for management personnel.

03 Artificial Intelligence Research + Industry Innovation Applications

As an AI company, Futong Dongfang has insisted on independent innovation and research and development for many years, obtaining multiple invention patents. It integrates artificial intelligence technology with various industries, applying cutting-edge technologies such as algorithm models, machine learning (ML), neural networks, and deep learning to innovative business practices within those industries.

Currently, Futong Dongfang's AI products have been implemented in industries such as healthcare, transportation, and manufacturing, helping numerous enterprise clients to achieve digital transformation and business innovation.

In the future, Futong Dongfang will continue to dedicate itself to research in the AI field, fully leveraging its technological strengths and the advantages of transforming research outcomes into practical applications, contributing to the digital transformation, intelligent application, and innovative development across various industries.