A Survey Paper in Transportation Logistics based on Artificial Intelligence

Document Type: Review Paper

Authors

1 Faculty of Computers and Artificial Intelligence, Information Systems Department, Helwan University, Cairo, Egypt

2 Faculty of Commerce and Business Administration, Business Information Systems Department, Helwan University, Cairo, Egypt

3 Nanees.Nabil21@commerce.helwan.edu.eg

10.22034/IJSOM.2021.4.6

Abstract

In the recent era, Transportation considers the most powerful component of the business logistics system. Likewise, there is an interdependent relationship between the transportation and logistics systems. This paper aims to make a comparative study of logistics transportation problems based on intelligence algorithms. The researchers surveyed the previous studies conducted in the Artificial Intelligent field to solve complex problems. In this research study, the authors focused on techniques that are mostly applied in transportation and logistics systems, especially, Artificial Neural Network, Genetic Algorithm, and Fuzzy Logic models. Also, a proposed model and algorithm was done to obtain customers’ and organizations’ satisfaction. Artificial Neural Network uses as a decision tool that combines the system stat sets and the operation state-dependent sets. As well, the genetic algorithm combines the best parameters as a method to finds the best evaluation solutions. And fuzzy logic uses a fuzzy set to help decision-makers in making the best decisions in multiple fields. Finally, authors recommended to work in two new areas which are FGA, NFGA Algorithms to solve complex and multimodal problems that faces transportation logistics sector.

Keywords


Abdullah, Malak , and Shawqi. (2012). Using fuzzy sets in controlling on some products of general company of wool industry/ Al-Kadhmain factor .master thesis, college of administration and economic , department of statistics , Baghdad University.

 

Al-Ubaidi Marwan and Abdel Hameed Ashwur (2009). the problem of fuzzy liner program , journal of economic and administration sciences, Vol. 15(56), pp.181-200.

 

Anuradha, D., and Sobana, V. E. (2017). A survey on fuzzy transportation problems. In IOP Conference Series: Materials Science and Engineering, Vol. 263(4), p. 042105.‏

 

Anukokila, P., Anju, A., and Radhakrishnan, B. (2019). Optimality of intuitionistic fuzzy fractional transportation problem of type-2, Arab Journal of Basic and Applied Sciences, Vol. 26(1), pp. 519-530.‏

 

Chou, C. C., and Lin, K. S. (2019). A fuzzy neural network combined with technical indicators and its application to Baltic Dry Index forecasting. Journal of Marine Engineering & Technology, Vol. 18(2), pp. 82-91.‏

 

Dey, S., and Ghose, D. (2020). Artificial Neural Network: An Answer to Right Order Quantity. In Proceedings of the Global AI Congress (pp. 529-533). Springer, Singapore.‏

 

Gajović, V., Kerkez, M., and Kočović, J. (2018). Modeling and simulation of logistic processes: risk assessment with a fuzzy logic technique. Simulation, Vol. 94(6), pp. 507-518.‏

 

Gharehbaghi, K. (2016)..‏ Artificial neural network for transportation infrastructure systems. In MATEC web of conferences (Vol. 81, p. 05001). EDP Sciences

 

Göçmen, E., and Erol, R. (2018). The problem of sustainable intermodal transportation: A case study of an international logistics company, turkey. Sustainability, Vol. 10(11), p. 4268.‏

 

Gürbüz, F., Eski, İ., Denizhan, B., and Dağlı, C. (2019). Prediction of damage parameters of a 3PL company via data mining and neural networks. Journal of Intelligent Manufacturing, Vol. 30(3), pp. 1437-1449.‏

 

Haddow, B. P., and Tufte, G. (2010). Goldberg DE Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co. In In Proceedings of the 2000 Congress on Evolutionary Computation CEC00.‏

 

Hamada, A. A. (2018). Solve the Fuzzy Transport Problems (FTP) to Reduce Transport Costs Using a Modern Method (An Empirical Study)., Journal of advance research in dynamical and control system, Vol. 10(13), pp. 1959-1972.‏

 

Hendalianpour, A., and Razmi, J. (2017). Customer satisfaction measurement using fuzzy neural network. Decision Science Letters, Vol. 6(2), pp. 193-206.‏

 

Holland, J. H. (1992). Genetic Algorithms, Computer Programs that "evolve" in ways that resemble natural selection can solve complex problems even their creators do not fully understand.International Journal of Engineering Innovation & Research, Vol. 6(4), pp. 174-178.‏

 

Izdebski, M., Jacyna-Gołda, I., Wasiak, M., Jachimowski, R., Kłodawski, M., Pyza, D., and Żak, J. (2018). The application of the genetic algorithm to multi-criteria warehouses location problems on the logistics network, Transport, Vol. 33(3), pp. 741-750.‏

 

Jafarzadeh, H., Moradinasab, N., Eskandari, H., and Gholami, S. (2017). Genetic algorithm for a generic model of reverse logistics network, International Journal of Engineering Innovation & Research, Vol. 6(4), pp. 174-178.‏

 

Joshi, H., and Singh, D. K. (2018). Optimal Transportation Cost Using Genetic Algorithm.Journal of Aeronautical and Automotive Engineering (JAAE), Vol. 5(1), pp. 17-19.‏

 

Jovčić, S., Průša, P., Dobrodolac, M., and Švadlenka, L. (2019). A proposal for a decision-making tool in third-party logistics (3PL) provider selection based on multi-criteria analysis and the fuzzy approach. Sustainability,  Vol. 11(15), p. 4236.‏

 

LAWAL, S., and AKINTOLA, K. (2021). A Product Backorder Predictive Model Using Recurrent Neural Network.‏ IRE Journals, Vol. 4(8), pp. 49-57.‏

 

Lesiak, P., and Bojarczyk, P. (2015). Application of genetic algorithms in design of public transport network. Logistics and Transport, Vol.26., pp. 75-82.‏

 

Levchenko, N. G., Glushkov, S. V., Sobolevskaya, E. Y., and Orlov, A. P. (2018). Application of fuzzy neural network technologies in management of transport and logistics processes in Arctic. In Journal of Physics: Conference Series (см. в книгах) (Vol. 1015, pp. 032085-032085).

 

Liu, H. (2015, April). Forecasting Model of Supply Chain Management Based on Neural Network. In 2015 International Conference on Automation, Mechanical Control and Computational Engineering. Atlantis Press.‏

 

Medvediev, I., Muzylyov, D., Shramenko, N., Nosko, P., Eliseyev, P., and Ivanov, V. O. (2020). Design logical linguistic models to calculate necessity in trucks during agricultural cargoes logistics using fuzzy logic. International scientific journal about logestics, Vol. 7(3), pp. 155-166.‏

 

Narayanamoorthy, S., Saranya, S., and Maheswari, S. (2013). A method for solving fuzzy transportation problem (ftp) using fuzzy russell's method, International Journal of Intelligent Systems and Applications, Vol. 5(2), pp. 71-75.‏

 

Oudani, M., El Hilali Alaoui, A., and Boukachour, J. (2014). An efficient genetic algorithm to solve the intermodal terminal location problem, International journal of supply and operations management, Vol.1(3), pp. 279-296.‏

 

Pagani, P., Colling, D., and Furmans, K. (2018). A Neural Network-Based Algorithm with Genetic Training for a Combined Job and Energy Management for AGVs. Logistics Journal: Proceedings, Vol. 2018(1) , pp. 1-9.‏‏

 

Rahman, A., Shahruddin, N. S., and Ishak, I. (2019, November). Solving the Goods Transportation Problem Using Genetic Algorithm with Nearest-Node Pairing Crossover Operator, In Journal of Physics: Conference Series (Vol. 1366, No. 1, p. 012073). IOP Publishing,‏ Vol. 1366(1), pp. 1-6.‏

 

Rajasekaran, S., and Pai, G. V. (2003). Neural networks, fuzzy logic and genetic algorithm: synthesis and applications (with cd). PHI Learning Pvt. Ltd.

 

Rangel, H. R., Puig, V., Farias, R. L., and Flores, J. J. (2017). Short-term demand forecast using a bank of neural network models trained using genetic algorithms for the optimal management of drinking water networks. Journal of Hydroinformatics, Vol. 19(1), pp. 1-16.‏

 

Shamma, M. N. E. D. A., Shawki, K. M., and Bassioni, H. A. (2017). Optimization of Construction Logistics Planning Cost in Egypt Using Genetic Algorithms, J Inform Tech Softw Eng, Vol. 7(4), pp. 205-217.‏

 

Sharma, G., Sharma, V., Pardasani, K. R., and Alshehri, M. (2020). Soft Set Based Intelligent Assistive Model for Multiobjective and Multimodal Transportation Problem. IEEE Access, Vol. 8, pp. 102646-102656.‏

 

Shekh Rasel, M., Bhuiyan, M. H., and Nahar, K. (2018). Optimization of an Emergency Relief Supply Model using Genetic Algorithm along with a Framework for Structuring Humanitarian Logistics Distribution Network.‏ International Conference on Mecanical, industrial and energy engineerin, 5th.

 

Shramenko, N., Muzylyov, D., and Karnaukh, M. (2018). The principles of the choice of management decisions based on fuzzy logic for cargo delivery of grain to the seaport. International Journal of Engineering & Technology, Vol. 7(4.3), pp. 211-216.‏

 

Shramenko, N., and Muzylyov, D. (2019, June). Forecasting of overloading volumes in transport systems based on the fuzzy-neural model. In Design, Simulation, Manufacturing: The Innovation Exchange (pp. 311-320). Springer, Cham.‏

Sreenivas, M., and Srinivas, T. (2014). The role of transportation in logistics chain.‏

 

Thompson, R. G., and Macharis, C. (2015). Application Of Genetic Algorithms In Optimizing The Logistics Network In An Urban Bicycle Delivery System 2.‏Conference: Transportation research board, 94th, pp. 1-15.‏

 

Valluru, S. K., and Rao, T. N. (2010). Introduction to Neural Networks, Fuzzy Logic and Genetic Algorithms,First edition. Mumbai: Jaico Publishing House.

 

Vas, P. (1999). Artificial-intelligence-based electrical machines and drives: application of fuzzy, neural, fuzzy-neural, and genetic-algorithm-based techniques (Vol. 45). Oxford university press.‏

 

Wang, C. Y., and Zhu, A. D. (2018, April). A Novel GA-BP Based Bidding Prediction Algorithm for Contract Logistics of Road Freight Transportation. In 2018 International Conference on Education Reform and Management Science (ERMS 2018) (pp. 357-362). Atlantis Press.‏

 

Yildiz, T. (2014). Optimization of Logistics: Theory & Practice, ISBN: 1500173606 ISBN-13: 978-1500173609,  Turkay Yildiz.

 

Zhang, Q., Jiang, C., Zhang, J., and Wei, Y. (2014). Application of genetic algorithm in functional area layout of railway logistics park, Procedia-Social and Behavioral Sciences, Vol. 138(1), pp. 269-278.‏

 

Zubir, S. N., Shariff, S. S. R., and Zahari, S. M. (2020). Application of artificial neural network to predict amount of carried weight of cargo train in rail transportation system. IAES International Journal of Artificial Intelligence, Vol. 9(3), pp. 480-487.‏