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An Innovative Multi-Objective Rescheduling System for Mitigating Pandemic Spread in Aviation Networks

Author

Listed:
  • Yujie Yuan

    (School of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China
    School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
    Department of Electronic and Electrical Engineering, Brunel University London, London UB8 3PH, UK)

  • Yantao Wang

    (School of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China)

  • Xiushan Jiang

    (School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Chun Sing Lai

    (Department of Electronic and Electrical Engineering, Brunel University London, London UB8 3PH, UK
    School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

Abstract

The novel coronavirus outbreak has significantly heightened environmental costs and operational challenges for civil aviation airlines, prompting emergency airport closures in affected regions and a substantial decline in ridership. The consequential need to reassess, delay, or cancel flight itineraries has led to disruptions at airports, amplifying the risk of disease transmission. In response, this paper proposes a spatial approach to efficiently address pandemic spread in the civil aviation network. The methodology prioritizes the use of a static gravity model for calculating route-specific infection pressures, enabling strategic flight rescheduling to control infection levels at airports (nodes) and among airlines (edges). Temporally, this study considers intervals between takeoffs and landings to minimize crowd gatherings, mitigating the novel coronavirus transmission rate. By constructing a discrete space–time network for irregular flights, this research generates a viable set of routes for aircraft operating in special circumstances, minimizing both route-specific infection pressures and operational costs for airlines. Remarkably, the introduced method demonstrates substantial savings, reaching almost 53.4%, compared to traditional plans. This showcases its efficacy in optimizing responses to pandemic-induced disruptions within the civil aviation network, offering a comprehensive solution that balances operational efficiency and public health considerations in the face of unprecedented challenges.

Suggested Citation

  • Yujie Yuan & Yantao Wang & Xiushan Jiang & Chun Sing Lai, 2024. "An Innovative Multi-Objective Rescheduling System for Mitigating Pandemic Spread in Aviation Networks," Clean Technol., MDPI, vol. 6(1), pages 1-16, January.
  • Handle: RePEc:gam:jcltec:v:6:y:2024:i:1:p:6-92:d:1320037
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    References listed on IDEAS

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    1. B. T. Grenfell & O. N. Bjørnstad & J. Kappey, 2001. "Travelling waves and spatial hierarchies in measles epidemics," Nature, Nature, vol. 414(6865), pages 716-723, December.
    2. Abdelghany, Khaled F. & Abdelghany, Ahmed F. & Ekollu, Goutham, 2008. "An integrated decision support tool for airlines schedule recovery during irregular operations," European Journal of Operational Research, Elsevier, vol. 185(2), pages 825-848, March.
    3. Du, Wen-Bo & Zhang, Ming-Yuan & Zhang, Yu & Cao, Xian-Bin & Zhang, Jun, 2018. "Delay causality network in air transport systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 118(C), pages 466-476.
    4. Abdi, M. Reza & Sharma, Sanjay, 2008. "Information system for flight disruption management," International Journal of Information Management, Elsevier, vol. 28(2), pages 136-144.
    5. Deyi Mou & Wanlin Zhao, 2013. "An Irregular Flight Scheduling Model and Algorithm under the Uncertainty Theory," Journal of Applied Mathematics, Hindawi, vol. 2013, pages 1-8, September.
    6. Kohl, Niklas & Larsen, Allan & Larsen, Jesper & Ross, Alex & Tiourine, Sergey, 2007. "Airline disruption management—Perspectives, experiences and outlook," Journal of Air Transport Management, Elsevier, vol. 13(3), pages 149-162.
    7. Kirsi Spoof-Tuomi & Seppo Niemi, 2020. "Environmental and Economic Evaluation of Fuel Choices for Short Sea Shipping," Clean Technol., MDPI, vol. 2(1), pages 1-19, January.
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