Resilience of Robotic Solutions in Extreme Conditions

Authors

  • Dariusz Sala AGH University of Krakow
  • Pavlo Pikulin AGH University of Krakow
  • Valentyn Sobchuk Taras Shevchenko National University of Kyiv
  • Igor Kotsan AGH University of Krakow

DOI:

https://doi.org/10.7494/dmms.2024.18.6054

Abstract

The study is devoted to the problems of using modern advanced technologies by logistics companies aimed at increasing the speed of technological operations and transforming business processes aimed at reducing financial costs, increasing the efficiency of the use of labour resources, and minimizing risks. Today, this is a decisive factor in increasing the company's competitiveness in the market, increasing profitability, and realizing long-term leadership. Innovative logistics is an effective tool for streamlining flow processes through the introduction of high-tech innovations in the operational and strategic management of market structures, which are aimed at improving the quality of customer service, increasing the efficiency of flow processes and reducing the total cost of their implementation in order to achieve key business objectives.
The paper examines approaches to the automation of business processes in the logistics sector, in the context of robotization of technological operations, taking into account the features due to the functioning of enterprises under the conditions of constant exposure to extreme risks. The concept of robotization of processes has been developed, which will increase the productivity and efficiency of business, help reduce operating costs, reduce the likelihood of personnel errors and contribute to improving business security. The results are implemented in the practice of a number of logistics companies in the real sector of the economy.

References

Altiparmak F., Gen M., Lin L. & Paksoy T. (2006). A genetic algorithm approach for multi-objective optimization of supply chain networks. Computers & Industrial Engineering, 51(1)1, pp. 196–215. DOI: https://doi.org/10.1016/j.cie.2006.07.011.

Aqlan F. & Lam S.S. (2015). A fuzzy-based integrated framework for supply chain risk assessment. International Journal of Production Economics, 161, pp. 54–63. DOI: https://doi.org/10.1016/j.ijpe.2014.11.013.

Atzeni G., Vignali G., Tebaldi L. & Bottani E. (2021). A bibliometric analysis on collaborative robots in Logistics 4.0 environments. Procedia Computer Science, 180, pp. 686–695. DOI: https://doi.org/10.1016/j.procs.2021.01.291.

Baghalian A., Rezapour S. & Zanjirani Farahani R. (2013). Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case. European Journal of Operational Research, 227(1), pp. 199–215. DOI: https://doi.org/10.1016/j.ejor.2012.12.017.

Barabash O., Sobchuk V., Musienko A., Laptiev O., Bohomia V. & Kopytko S. (2023). System analysis and method of ensuring functional sustainability of the information system of a critical infrastructure object. In: M. Zgurovsky & N. Pankratova (Eds.), System Analysis and Artificial Intelligence, Springer Nature, Cham, Switzerland, pp. 177–192. DOI: https://doi.org/10.1007/978-3-031-37450-0_11.

Bernardo R., Sousa J.M.C. & Gonçalves P.J.S. (2022). Survey on robotic systems for internal logistics. Journal of Manufacturing Systems, 66, pp. 339–350. DOI: https://doi.org/10.1016/j.jmsy.2022.09.014.

Cardoso S.R., Barbosa-Póvoa A.P.F.D. & Relvas S. (2013). Design and planning of supply chains with integration of reverse logistics activities under demand uncertainty. European Journal of Operational Research, 226(3,) pp. 436–451. DOI: https://doi.org/10.1016/j.ejor.2012.11.035.

Cimin C., Lagorio A., Cavalieri S., Riedel O., Pereira C.E. & Wang J. (2022). Human-technology integration in smart manufacturing and logistics: current trends and future research directions. Computers & Industrial Engineering, 169, art. no. 108261. DOI: https://doi.org/10.1016/j.cie.2022.108261.

Dzedzickis A., Subačiu¯te˙-Žemaitienė J., Šutinys E., Samukaitė-Bubnienė U. & Bučinskas V. (2022). Advanced applications of industrial robotics: New trends and possibilities. Applied Sciences, 12(1), art. no. 135. DOI: https://doi.org/ 10.3390/app12010135.

Ho W., Zheng T., Yildiz H. & Talluri S. (2015). Supply chain risk management: a literature review. International Journal of Production Research, 53(16), pp. 5031–5069. DOI: https://doi.org/10.1080/00207543.2015.1030467.

Laptiev O., Musienko A., Nakonechnyi V., Sobchuk A., Gakhov S. & Kopytko S. (2023). Algorithm for Recognition of Network Traffic Anomalies Based on Artificial Intelligence. In: 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). DOI: https://doi.org/10.1109/HORA58378.2023.10156702.

Lin H., Lin J. & Wang F. (2022). An innovative machine learning model for supply chain management. Journal of Innovation & Knowledge, 7(4), art. no. 100276. DOI: https://doi.org/10.1016/j.jik.2022.100276.

Nezamoddini N., Gholami A. & Aqlan F. (2020). A risk-based optimization framework for integrated supply chains using genetic algorithm and artificial neural

networks. International Journal of Production Economics, 225, art. no. 107569. DOI: https://doi.org/10.1016/j.ijpe.2019.107569.

Obidin D., Ardelyan V., Lukova-Chuiko N. & Musienko A. (2017). Estimation of functional stability of special purpose networks located on vehicles. In: 2017 IEEE 4th International Conference Actual Problems of Unmanned Aerial Vehicles Developments (APUAVD), October 17–19, 2017, Kyiv, Ukraine, National Aviation University, Kyiv, pp. 167–170. DOI: https://doi.org/10.1109/

APUAVD.2017.8308801.

Pichkur V., Laptiev O., Polovinkin I., Barabash A., Sobchuk A. & Salanda I. (2022). The method of managing man-generated risks of critical infrastructure systems based on ellipsoidal evaluation. In: 2022 IEEE 4th International Conference on Advanced Trends in Information Theory (ATIT), pp. 133–137. DOI: https://doi.org/10.1109/ATIT58178.2022.10024244.

Pichkur V.V. & Sobchuk V.V. (2021). Mathematical model and control design of a functionally stable technological process. Journal of Optimization, Differential Equations and Their Applications, 29(1), pp. 32–41. DOI: https://doi.org/10.15421/142102.

Sobchuk V., Olimpiyeva Y., Musienko A. & Sobchuk A. (2021). Ensuring the properties of functional stability of manufacturing processes based on the application of neural networks. In: V. Snytyuk, A. Anisimov, I. Krak, M. Nikitcheko, O. Marchenko, F. Mallet, V. Tsyganok, A. Chris, A. Pester, H. Tanaka, K. Henke, O. Chertov, S. Bozóki, V. Vovk (Eds.), Proceedings of the 7th International

Conference “Information Technology and Interactions” (IT&I-2020). Workshops Proceedings, Kyiv, Ukraine, December 2–3, 2020, pp. 106–116. URL: https://ceur-ws.org/Vol-2845/Paper_11.pdf.

Svynchuk O., Barabash A., Laptiev S. & Laptieva T. (2021). Modification of query processing methods in distributed databases using fractal trees. In: International Scientific and Practical Conference “Information Security and Information Technologies”, 13–19 September 2021, Kharkiv – Odesa, Ukraine. Conference Proceedings, Simon Kuznets Kharkiv National University of Economics, Kharkiv –Odesa, pp. 39–44. URL: https://drforum.science/wp-content/uploads/2021/12/proceedings_ibit-2021.pdf.

Yevseiev S., Khokhlachova Y., Ostapov S. & Laptiev O. (Eds.) (2023). Models of Socio-cyber-physical Systems Security. Monograph. PC TECHNOLOGY CENTRE, Kharkiv.

Yevseiev S., Ponomarenko V., Laptiev O. & Milov O. (Eds.) (2021). Synergy of Building Cybersecurity Systems. Monograph. PC TECHNOLOGY CENTER, Kharkiv.

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Published

2024-07-29

How to Cite

Sala, D., Pikulin, P., Sobchuk, V., & Kotsan, I. (2024). Resilience of Robotic Solutions in Extreme Conditions. Decision Making in Manufacturing and Services, 18, 5–18. https://doi.org/10.7494/dmms.2024.18.6054

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Articles
Received 2024-01-15
Accepted 2024-02-24
Published 2024-07-29