Artificial Neural Networks-Based Route Selection Model for Multimodal Freight Transport Network During Global Pandemic

Year 2023, Volume: 11 Issue: 3, 163 - 173, 30.09.2023

Yaşanur Kayıkcı , Elif Cesur

https://doi.org/10.21541/apjess.1294957

Abstract

The global pandemic caused major disruptions in all supply chains. Road transport has been particularly affected by the challenges posed by the COVID-19 pandemic. The selection of an efficient and effective route in multimodal freight transport networks is a crucial part of transport planning to combat the challenges and sustain supply chain continuity in the face of the global pandemic. This study introduces a novel optimal route selection model based on integrated fuzzy logic approach and artificial neural networks. The proposed model attempts to identify the optimal route from a range of feasible route options by measuring the performance of each route according to transport variables including, time, cost, and reliability. This model provides a systematic method for route selection, enabling transportation planners to make smart decisions. A case study is conducted to exhibit the proposed model's applicability to real pandemic conditions. According to the findings of the study, the proposed model can accurately and effectively identify the best route and provides transportation planners with a viable option to increase the efficiency of multimodal transport networks. In conclusion, by proposing an innovative and efficient strategy for route selection in complex transport systems, our research significantly advances the field of transportation management.

Keywords

Artificial Neural Network, Fuzzy Logic, Route Selection, Freight Transportation, Multimodal Transportation

References

• T. M. Choi, “Innovative ‘Bring-Service-Near-Your-Home’ operations under Corona-Virus (COVID-19/SARS-CoV-2) outbreak: Can logistics become the Messiah?,” Transp. Res. E Logist. Transp. Rev., vol. 140, 2020, doi: 10.1016/j.tre.2020.101961.
• R. Kiani Mavi, N. Kiani Mavi, D. Olaru, S. Biermann, and S. Chi, “Innovations in freight transport: a systematic literature evaluation and COVID implications,” Int. J. Logist. Manag., vol. 33, no. 4, 2022, doi: 10.1108/IJLM-07-2021-0360.
• D. Ivanov, “Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case,” Transp. Res. E Logist. Transp. Rev., vol. 136, 2020, doi: 10.1016/j.tre.2020.101922.
• D. Ivanov, “Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic,” Ann. Oper. Res., vol. 319, no. 1, 2022, doi: 10.1007/s10479-020-03640-6.
• M. Mitręga and T. M. Choi, “How small-and-medium transportation companies handle asymmetric customer relationships under COVID-19 pandemic: A multi-method study,” Transp. Res. E Logist. Transp. Rev., vol. 148, Apr. 2021, doi: 10.1016/j.tre.2021.102249.
• D. Vrabac, M. Shang, B. Butler, J. Pham, R. Stern, and P. E. Pare, “Capturing the Effects of Transportation on the Spread of COVID-19 with a Multi-Networked SEIR Model,” IEEE Control. Syst. Lett., vol. 6, pp. 103–108, 2022, doi: 10.1109/LCSYS.2021.3050954.
• A. Bombelli, “Integrators’ global networks: A topology analysis with insights into the effect of the COVID-19 pandemic,” J. Transp. Geogr., vol. 87, 2020, doi: 10.1016/j.jtrangeo.2020.102815.
• A. Mutlu, Y. Kayikci, and B. Çatay, “Planning Multimodal Freight Transport Operations: a Literature Review,” 22nd ISL 2017 Ljubljana, no. July, 2017.
• Y. Kayıkcı, A. Mutlu, and B. Çatay, “A Decision Support System and its Application for International Sea-Rail Multimodal Freight Transportation Operations,” Uludağ Uni. J. Faculty Eng., vol. 23, no. 4, pp. 99–116, Dec. 2018, doi: 10.17482/uumfd.455356.
• Y. Kayıkcı, “Analysis of Cost Allocation Methods in International Sea-Rail Multimodal Freight Transportation,” J. Yaşar Uni, vol. 15, no. 57, 2020, doi: 10.19168/jyasar.568692.
• Y. Kayikci and V. Stix, “Causal mechanism in transport collaboration,” Expert Syst Appl., vol. 41, no. 4, 2014, doi: 10.1016/j.eswa.2013.08.053.
• A. Liu, Z. Li, W. L. Shang, and W. Ochieng, “Performance evaluation model of transportation infrastructure: Perspective of COVID-19,” Transp. Res. Part. A Policy Pract., vol. 170, 2023, doi: 10.1016/j.tra.2023.103605.
• S. J. Ho, W. Xing, W. Wu, and C. C. Lee, “The impact of COVID-19 on freight transport: Evidence from China,” MethodsX, vol. 8, Jan. 2021, doi: 10.1016/j.mex.2020.101200.
• Y. Kayikci and B. Çatay, “Revenue-based slot allocation and pricing framework for multimodal transport networks,” 22nd ISL 2017 Ljubljana, no. July, 2017.
• L. A. Tavasszy, “Modal Shift Target for Freight Transport Above 300 km: An Assessment,” 2011. https://www.acea.auto/files/SAG_17.pdf
• Ulaştırma Denizcilik ve Haberleşme Bakanlığı, Türkiye Kombine Taşımacılık Strateji Belgesi. 2014.
• UN, “United Nations Convention On International Multimodal Transport Of Goods,” Unif. Law Rev., vol. os-8, no. 2, 1980, doi: 10.1093/ulr/os-8.2.157.
• L. Qu and Y. Chen, “A hybrid MCDM method for route selection of multimodal transportation network,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008. doi: 10.1007/978-3-540-87732-5_42.
• Z. Lv, Y. Li, H. Feng, and H. Lv, “Deep Learning for Security in Digital Twins of Cooperative Intelligent Transportation Systems,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 9, 2022, doi: 10.1109/TITS.2021.3113779.
• Z. Zhao and Y. Liang, “A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewards,” Transp. Res. Part C Emerg. Technol., vol. 149, 2023, doi: 10.1016/j.trc.2023.104079.
• A. Abedalla et al., “MTRecS-DLT: Multi-Modal Transport Recommender System using Deep Learning and Tree Models,” in 2019 6th International Conference on Social Networks Analysis, Management and Security, SNAMS 2019, 2019. doi: 10.1109/SNAMS.2019.8931864.
• A. Liu, Z. Li, W.-L. Shang and W. Ochieng, “Performance Evaluation Model of Transportation İnfrastructure: Perspective of COVID-19,” Transp. Res. Part A Policy Pract., vol. 170, 1036, 2023, doi: 10.1016/j.tra.2023.103605
• C. Barnhart and H. D. Ratliff, “Modeling Intermodal Routing,” J. Bus. Logist., vol. 14, no. 1, 1993.
• T. S. Chang, “Best routes selection in international intermodal networks,” Comput. Oper. Res., vol. 35, no. 9, 2008, doi: 10.1016/j.cor.2006.12.025.
• H. Min, “International intermodal choices via chance-constrained goal programming,” Transp. Res. Part A Policy Pract., vol. 25, no. 6, 1991, doi: 10.1016/0191-2607(91)90013-G.
• J. Woxenius, “Generic framework for transport network designs: Applications and treatment in intermodal freight transport literature,” Transp. Rev., vol. 27, no. 6, 2007, doi: 10.1080/01441640701358796.
• P. Campigotto, C. Rudloff, M. Leodolter, and D. Bauer, “Personalized and Situation-Aware Multimodal Route Recommendations: The FAVOUR Algorithm,” IEEE Trans. Intell. Transp. Syst., vol. 18, no. 1, 2017, doi: 10.1109/TITS.2016.2565643.
• R. Abduljabbar, H. Dia, S. Liyanage, and S. A. Bagloee, “Applications of artificial intelligence in transport: An overview,” Sustainability, vol. 11, no. 1. 2019. doi: 10.3390/su11010189.
• J. I.-Z. Chen and J.-T. Chang, “Route Choice Behaviour Modeling using IoT Integrated Artificial Intelligence,” J. Artif. Intell. Capsul. Netw., vol. 2, no. 4, 2021, doi: 10.36548/jaicn.2020.4.006.
• N., Koohathongsumrit and W. Chankham, “Route Selection in Multimodal Supply Chains: A Fuzzy Risk Assessment Model-BWM-MARCOS Framework,” Appl. Soft Comput., vol. 137, 110167, 2023, doi: 10.1016/j.asoc.2023.110167
• A. Kengpol, W. Meethom, and M. Tuominen, “The development of a decision support system in multimodal transportation routing within Greater Mekong sub-region countries,” Int. J. Prod. Econ., 2012. doi: 10.1016/j.ijpe.2011.02.024.
• H. Altınkaya and M. Yılmaz, “Estimation of the Daily Production Levels of a Run-of-River Hydropower Plant Using the Artificial Neural Network,” Acad. Platf. J. Eng. and Smart Syst., vol. 11, no. 2, pp. 62-72, 2023, doi:10.21541/apjess.1223119
• E. Yıldız, M. M. Kelek , F. O. Hocaoğlu and Y. Oğuz , “Forecasting The Impact of Vaccination on Daily Cases in Turkey for Covid-19,” Acad. Platf. J. Eng. and Smart Syst., vol. 11, no. 1, pp. 19-26, 2023, doi:10.21541/apjess.1137177
• E. Sabeur and G. Denis, “Human behavior and social network simulation: Fuzzy sets/logic and agents-based approach,” in Agent Directed Simulation Symposium, ADS 2007 - Proceedings of the 2007 Spring Simulation Multiconference, SpringSim 2007, 2007.
• J. Tang, X. Liu, and W. Wang, “COVID-19 medical waste transportation risk evaluation integrating type-2 fuzzy total interpretive structural modeling and Bayesian network,” Expert. Syst. Appl., vol. 213, Mar. 2023, doi: 10.1016/j.eswa.2022.118885.
• M. Deveci, D. Pamucar, I. Gokasar, D. Delen, and L. Martínez, “A fuzzy Einstein-based decision support system for public transportation management at times of pandemic,” Knowl. Based Syst., vol. 252, Sep. 2022, doi: 10.1016/j.knosys.2022.109414.
• A. T. Özden and E. Celik, “Analyzing the service quality priorities in cargo transportation before and during the Covid-19 outbreak,” Transp. Policy (Oxf), vol. 108, pp. 34–46, Jul. 2021, doi: 10.1016/j.tranpol.2021.04.025.
• E. H. Mamdani and S. Assilian, “An experiment in linguistic synthesis with a fuzzy logic controller,” Int. J. Man. Mach. Stud., vol. 7, no. 1, 1975, doi: 10.1016/S0020-7373(75)80002-2.
• T. Takagi and M. Sugeno, “Fuzzy Identification of Systems and Its Applications to Modeling and Control,” IEEE Trans Syst Man Cybern, vol. SMC-15, no. 1, 1985, doi: 10.1109/TSMC.1985.6313399.
• Saaty T, The Analytic Hierarchy Process, vol. 45, no. 2–3. New York: McGraw-Hill., 1980. doi: 10.1016/0377-2217(90)90209-t.
• M. Becher, “Simultaneous capacity and price control based on fuzzy controllers,” Int. J. Prod. Econ., vol. 121, no. 2, 2009, doi: 10.1016/j.ijpe.2006.09.014.
• P. H. Sydenham and R. Thorn, Handbook of Measuring System Design. Wiley, 2005.
• Z. H. Zhou, “Rule extraction: Using neural networks or for neural networks?,” J. Comput. Sci. Technol., vol. 19, no. 2, 2004, doi: 10.1007/BF02944803.
• Y. Chen and J. Li, “Recurrent Neural Networks algorithms and applications,” in Proceedings - 2021 2nd International Conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2021, 2021. doi: 10.1109/ICBASE53849.2021.00015.
• A. Mahmoudi, H. Shavandi, and K. Nouhi, “Analysing Price, Quality and Lead Time Decisions with the Hybrid Solution Method of Fuzzy Logic and Genetic Algorithm,” J. Optim. Ind. Eng., vol. 10, no. 1–9, 2012, [Online]. Available: www.SID.ir