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The Main Trends and Challenges in The Development of the Different Industries During The COVID-19 Pandemic

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The purpose of the research in this article is to investigate the main trends in the development of the different industries during the COVID-19 pandemic, to identify the main problems facing the different industries in the context of the global crisis, as well as to form the basic concepts necessary for a real recovery of the global industry. The authors identify the main problems facing the aviation industry in the developing world crisis and possible ways to solve them. As a working hypothesis, it is proposed to form the basic concepts necessary for preparing and implementing operational measures to restore passenger and cargo aviation. Considering the main threats facing the aviation industry during COVID-19, the article proposes the organizational and economic mechanisms to restore the industry. Furthermore, several recovery scenarios are considered, considering the relevant factors that have a particular impact. Next, a novel mathematical model for pharmaceutical products, which are the most important in COVID-19 pandemics, is proposed. Moreover, the model considers the uncertainty, and a robust optimization approach is applied. The study is based on a comprehensive analysis of documentary data provided by government agencies in several European countries. An analysis of global and Russian passenger traffic for Q1-Q4 (quartile) of 2020 and a development forecast for Q1-Q2 of 2021 is provided. The scenario problems facing the aviation industry in the context of the COVID-19 crisis are identified. There are key concepts necessary to prepare and implement effective measures to restore the aviation industry.
Rocznik
Strony
209--231
Opis fizyczny
Bibliogr. 28 poz., rys., tab.
Twórcy
  • Department Human Resource Management, PhD, Associate Professor, Moscow Aviation Institute
  • Department Management and Marketing High-Tech Industries of the Industry, Candidate of Economical Sciences, Moscow Aviation Institute, Russian Federation
  • Department Human Resource Management, Doctor of Technical Sciences, Associate Professor, Moscow Aviation Institute Moscow, Russia
  • Department Economical Sciences Transport and Telecommunication Institute
Bibliografia
  • [1] Batkovskiy A.M., Semenova E.G., Trofimets V. Ya., Trofimets E.N., Fomina A.V. (2017). Statistical Simulation of the Break-Even Point in the Margin Analysis of the Company // Journal of Applied Economic Sciences. Romania: European Research Centre of Managerial Studies in Business Administration. Volume XII, Issue 2 (48), Spring 2017. Р 558-571
  • [2] Button, K. Aviation. In: Schintler, L., McNeely C. (2020). Encyclopedia of Big Data. Springer, Cham, https://doi.org/10.1007/978-3-319-32001-4_233-1.
  • [3] Chaika, N.K. (2019). The agreement granting the use of exclusive rights as a tool to recover creation costs. IOP Conference Series: Materials Science and Engineering, 537 (4), No042058.
  • [4] Chursin, A.A., Grosheva, P.Yu. & Yudin, A.V. (2020). Fundamentals of the economic growth of engineering enterprises in the face of challenges of the XXI century. IOP Conference Series: Materials Science and Engineering, 862(4), 042049.
  • [5] Feshina, S.S., Konovalova, O.V. & Sinyavsky, N.G. (2019). Industry 4.0—transition to new economic reality. Studies in Systems, Decision and Control, 169, 111-120.
  • [6] Goli, A., & Malmir, B. (2020). A Covering Tour Approach for Disaster Relief Locating and Routing with Fuzzy Demand. International Journal of Intelligent Transportation Systems Research, 18(1), 140-152.
  • [7] Sharifi, A., Ahmadi, M., & Ala, A. (2021). The impact of artificial intelligence and digital style on industry and energy post-COVID-19 pandemic. Environmental Science and Pollution Research, 28(34), 46964-46984.
  • [8] Goli, A., Tirkolaee, E.B., & Aydin, N. S. (2021). Fuzzy integrated cell formation and production scheduling considering automated guided vehicles and human factors. IEEE Transactions on Fuzzy Systems.
  • [9] Goli, A., Zare, H. K., Moghaddam, R., & Sadeghieh, A. (2018). A comprehensive model of demand prediction based on hybrid artificial intelligence and metaheuristic algorithms: A case study in dairy industry.
  • [10] Goli, A., Zare, H.K., Tavakkoli-Moghaddam, R., & Sadeghieh, A. (2019). Application of robust optimization for a product portfolio problem using an invasive weed optimization algorithm. Numerical Algebra, Control & Optimization, 9(2), 187.
  • [11] Mocenco, D. (2015). Supply chain features of the aerospace industry. particular case airbus and Boeing. Scientific Bulletin-Economic Sciences, 14(2): 17-25.
  • [12] ICAO. Effects of Novel Coronavirus (Covid-19) on Civil Aviation: Economic Impact Analysis, 2020, URL.https://www.icao.int/sustainability/Documents/COVID19/ICAO_Coronavirus_Econ_Impact.pdf.
  • [13] IMF. World Economic Forum. Chapter 1. The Great Lockdown. International Monetary Fund. URL: https://www.imf.org/en/Publications/WEO/Issues/2020/04/ 14/weo-april-2020.
  • [14] Kuzmina-Merlino, I., Saksonova, S., Djakonova, K. (2020). Airport Charges Policy as a Tool for Achieving Competitive Advantage in the Aviation Market. Lecture Notes in Networks and Systems, 117, 543-551.
  • [15] Margarov, G. (2016). Information security - Basis of the education system for digital Generation Z. Meeting Security Challenges Through Data Analytics and Decision Support, 317–325.
  • [16] Pahlevan, S.M., Hosseini, S.M. S., & Goli, A. (2021). Sustainable supply chain network design using products’ life cycle in the aluminum industry. Environmental Science and Pollution Research, 1-25.
  • [17] Queiroz, M.M., Ivanov, D., Dolgui, A. (2020). Impacts of epidemic outbreaks on supply chains: mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Ann Oper Res., https://doi.org/10.1007/s10479-020-03685-7
  • [18] Sivaram, M., Lydia, E.L. & Pustokhina, I.V. (2020). An Optimal Least Square Support Vector Machine Based Earnings Prediction of Blockchain Financial Products, IEEE Access, 8, 120321-120330, 9127981. doi: 10.1109/ACCESS.2020.3005808
  • [19] Stefano, I., Fabrizio N., Carlos S. & Spyratos, S. (2020). Estimating and Projecting Air Passenger Traffic during the COVID-19 Coronavirus Outbreak and its Socio-Economic Impact.
  • [20] Suau-Sanchez, P., Voltes-Dorta, A. & Cugueró-Escofet, N. (2020). An early assessment of the impact of COVID-19 on air transport: Just another crisis or the end of aviation as we know it? Journal of Transport Geography. https://doi.org/102749. 10.1016/j.jtrangeo.2020.102749.
  • [21] Tikhonov, A.I., Sazonov, A.A. & Boginsky, A.I. (2020). Planning, Development, and Quality Systems of Helicopters Production in Russia. Lecture Notes in Networks and Systems. Springer. 650-662. DOI: https://doi.org/10.1007/978-3-030-40749-0_78
  • [22] Tikhonov, A.I., Sazonov, A.A. & Chursin, A.A. (2020). The Analysis of Foreign Planning, Development, and Quality Systems for the Production of Helicopter Technology in the World Market. Lecture Notes in Networks and Systems. Springer. 663-674. DOI: https://doi.org/10.1007/978-3-030-40749-0_79.
  • [23] Tuite, A.R., Ng, V. & Rees, E. (2020). Estimation of COVID-19 outbreak size in Italy. Lancet Infect. Dis., https://doi.org/10.1016/S1473-3099(20)30227-9.
  • [24] Vasilev, V.L., Gapsalamov, A.R. & Akhmetshin, E.M. (2020). Digitalization peculiarities of organizations: A case study. Entrepreneurship and Sustainability, Issues, 7(4), 3173-3190.
  • [25] Wu, J.T., Leung, K. & Leung, G.M. (2020). Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study, Lancet 395 (10225), 689-697.
  • [26] Zelentsova, L.S., Shalamova, N.G. & Vorontsova, Y.V. (2020). A mechanism for preventively assessing the competitiveness of high-tech products in conditions of digital transformations. Lecture Notes in Networks and Systems, 115, 159-167.
  • [27] Zhuang, Z., Zhao, S. & Lin, Q. (2020). Preliminary estimation of the novel coronavirus disease (COVID19) cases in Iran: a modelling analysis based on overseas cases and air travel data. Int. J. Infect., Dis. 94, 29-31.
  • [28] Joshi, A., Dey, N., & Santosh, K. C. (Eds.). (2020). Intelligent systems and methods to combat covid-19. Springer.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-20e6b579-e701-4ca2-9d26-593c71776376
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