Implementation of logistics and transport processes in an enterprise operating on Polish territory in the face of COVID-19

• Autorzy: Łukasik Z. , Kuśmińska-Fijałkowska A. , Olszańska S. , Roman M.

Identyfikatory

DOI

10.12716/1001.16.01.09

• Języki publikacji: EN

Abstrakty

EN

Logistics managers are responsible for efficient functioning of transport companies. This article will allow logistics managers to better understand the essence of logistics projects and the implementation of alternative routes. The aim of this article is to show the process of implementation of new routing alternatives. The analysis was conducted for the period January ÷ April 2020, which marked the emergence of the first COVID-19 cases, as well as for the months January ÷ April 2021. Alternative routes were proposed and implemented to improve the quality of services in a transport company operating in Poland. The impact of the pandemic on the selected transport company was evaluated. The results on new sections of transport corridors were presented together with an in-depth verification of how the introduced changes affected the provided transport services. In the end, the most important benefits connected with the analysis and the implemented project were presented.

Słowa kluczowe

EN

COVID-19; logistics; transport processes; transport corridors; transport companies; European transport enterprise; Polish territory

• Wydawca: Faculty of Navigation, Gdynia Maritime University
• Czasopismo: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
• Rocznik: 2022
• Tom: Vol. 16 No. 1
• Strony: 89--96
• Opis fizyczny: Bibliogr. 20 poz., rys., tab.

Twórcy

autor

Łukasik Z.

• University of Technology and Humanities in Radom, Radom, Poland

autor

Kuśmińska-Fijałkowska A.

• University of Technology and Humanities in Radom, Radom, Poland

autor

Olszańska S.

• University of Information Technology and Management in Rzeszów, Rzeszów, Poland

autor

Roman M.

• University of Information Technology and Management in Rzeszów, Rzeszów, Poland

Bibliografia

• 1. Abeysekara, B.: Application of Fuzzy Set Theory to Evaluate Large Scale Transport Infrastructure Risk Assessment and Application of Best Practices for Risk Management. In: 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). pp. 385–389 (2020). https://doi.org/10.1109/IEEM45057.2020.9309957.
• 2. Ćirović, G. et al.: Green logistic vehicle routing problem: Routing light delivery vehicles in urban areas using a neuro-fuzzy model. Expert Systems with Applications. 41, 9, 4245–4258 (2014). https://doi.org/10.1016/j.eswa.2014.01.005.
• 3. Ebben, M.J.R. et al.: Dynamic transport scheduling under multiple resource constraints. European Journal of Operational Research. 167, 2, 320–335 (2005). https://doi.org/10.1016/j.ejor.2004.03.020.
• 4. Evangelista, D.G.D. et al.: Approximate Optimization Model on Routing Sequence of Cargo Truck Operations through Manila Truck Routes using Genetic Algorithm. In: 2020 IEEE 12th International Conference on 96 Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM). pp. 1–5 (2020). https://doi.org/10.1109/HNICEM51456.2020.9400044.
• 5. Gharehgozli, A. et al.: The role of standardisation in European intermodal transportation. Maritime Business Review. 4, 2, 151–168 (2019). https://doi.org/10.1108/MABR-09-2018-0038.
• 6. Guze, S. et al.: Multi-Criteria Optimisation of Liquid Cargo Transport According to Linguistic Approach to the Route Selection Task. Polish Maritime Research. 24, s1, 89–96 (2017). https://doi.org/10.1515/pomr-2017-0026.
• 7. Idri, A. et al.: Design and Implementation Issues of a Time-dependent Shortest Path Algorithm for Multimodal Transportation Network. In: TD-LSG@PKDD/ECML. (2017).
• 8. Javadi, A. et al.: Solving a multi-objective vehicle scheduling-routing of interurban transportation fleet with the purpose of minimizing delays by Using the Differential Evolutionary Algorithm. Uncertain Supply Chain Management. 2, 3, 125–136 (2014). https://doi.org/10.5267/j.uscm.2014.5.005.
• 9. Kong, Y. et al.: Construction of the Optimization Model of Cargo Transport Network. E3S Web Conf. 261, (2021). https://doi.org/10.1051/e3sconf/202126103013.
• 10. Liu, Z. et al.: Double Path Optimization of Transport of Industrial Hazardous Waste Based on Green Supply Chain Management. Sustainability. 13, 9, (2021). https://doi.org/10.3390/su13095215.
• 11. Memon, M.A. et al.: Time and Quantity Based Hybrid Consolidation Algorithms for Reduced Cost Products Delivery. Computers, Materials \& Continua. 69, 1, (2021). https://doi.org/10.32604/cmc.2021.017653.
• 12. Monti, C. a. U. et al.: Optimization of Wood Supply: The Forestry Routing Optimization Model. An. Acad. Bras. Ciênc. 92, (2020).
• 13. Pamučar, D. et al.: Transport spatial model for the definition of green routes for city logistics centers. Environmental Impact Assessment Review. 56, 72–87 (2016). https://doi.org/10.1016/j.eiar.2015.09.002.
• 14. Pasha, J. et al.: An Optimization Model and Solution Algorithms for the Vehicle Routing Problem With a “Factory-in-a-Box.” IEEE Access. 8, 134743–134763 (2020). https://doi.org/10.1109/ACCESS.2020.3010176.
• 15. Qiang, X. et al.: Route optimization cold chain logistic distribution using greedy search method. OPSEARCH. 57, 4, 1115–1130 (2020). https://doi.org/10.1007/s12597-020-00459-4.
• 16. Skitsko, V., Voinikov, M.: Solving four-index transportation problem with the use of a genetic algorithm. Logforum. 16, 3, 6 (2020).
• 17. Smarsly, K., Mirboland, M.: BIM-based simulation of intelligent transportation systems. In: 2020 European Navigation Conference (ENC). pp. 1–10 (2020). https://doi.org/10.23919/ENC48637.2020.9317505.
• 18. Wang, Q.-Y., Mao, B.-H.: Impacts of Science and Technology on Transportation. Journal of Transportation Systems Engineering and Information Technology. 20, 06, 1-8+36 (2020). https://doi.org/10.16097/j.cnki.1009-6744.2020.06.001.
• 19. Zubkov, V.: Detailing the Impact Structure of the Participants of the Complex Transport Service. In: Mottaeva, A. (ed.) Technological Advancements in Construction. pp. 225–234 Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-030-83917-8_21.
• 20. Zubkov, V., Sirina, N.: Information and Intelligent Models in the Management of Transport and Logistics Systems. In: Mottaeva, A. (ed.) Technological Advancements in Construction. pp. 433–445 Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-030-83917-8_39.