Efficient matching between carriers and shippers is crucial in the logistics industry to optimize resource utilization and minimize costs. This paper puts forth a smart recommendation framework dependent on multilabel classification techniques to improve the carrier-shipper matching process. The method makes use of machine learning techniques to predict multiple relevant carrier options for a given shipment request. We present a comprehensive literature review on related works in carrier-shipper matching and multilabel classification methodologies. Our proposed system offers significant improvements over traditional methods by considering multiple factors simultaneously, resulting in more accurate and personalized recommendations. Experimental results demonstrate the effectiveness and feasibility of the proposed approach in enhancing the efficiency and effectiveness of carrier-shipper matching processes.

Keywords:Carrier-Shipper Matching, Multilabel Classification, Recommendation System, Logistics, Machine Learning

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1. Kato T, Higuchi M, Ohyama H, Kawamura Y, Ohno R. Container shipping route recommendation system by modeling logistics system with the ambient calculus. In: Proceedings of the International Conference on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), Australia. IEEE. 2022. Dec 17–19.
2. Santhiya R, GeethaPriya C. Machine Learning Techniques for Intelligent Transportation Systems-An overview. 12th International Conference on Computing Communication and Networking Technologies (ICCCNT),Kharagpur.IEEE.2021,July 06–08.
3. Berdeddouch A, Yahyaouy A, Benanni Y, Verde R. Deep based recommender system for relevant K pick-up points. International Conference on Intelligent Systems and Computer Vision (ISCV), Morocco.IEEE.2020,June 09–11.
4. Macheret PD, Savchuk RR, Shkuratov GI. Intelligent transport systems: analysis of the current state and prospects of development. International Conference on Quality Management, Transport and Information Security, Information Technologies (IT&QM&IS), Yaroslavl, Russian Federation.IEEE.2021, Sept. 06–10.
5. Malik MZ, Nazir S, Khan HU. Artificial Intelligence Based System on Enhancing the Capabilities of Transport System: A Systemic Literature Review. I Symposium on Industrial Electronics & Applications (ISIEA), Malaysia.IEEE.2023, July 15–16.
6. Efimenko DB, Filatov SA, Barabanova ES. Transport Management Systems as a Tool to Ensure the Flexibility and Efficiency of Cargo Delivery. I Intelligent Technologies and Electronic Devices in Vehicle and Road Transport Complex (TIRVED), Moscow, Russian Federation.IEEE.2022, Nov 10–11.
7. Lavanya R, Singh U, Tyagi V. A comprehensive survey on movie recommendation systems. International Conference on Artificial Intelligence and Smart Systems (ICAIS), Coimbatore India.IEEE.2021, March 25–27.
8. Roy D, Dutta M. A systematic review and research perspective on recommender systems. J. Big Data. 2022;9(1): 1–36p.
9. Tarasova VN, Koshcheeva EO, Ruchkin VB, Lyapina SY. Model of intelligent system for quality evaluation of services on design stage (transport and logistic business as an example). International Conference Quality Management, Transport and Information Security, Information Technologies (IT&QM&IS), Russia.IEEE.2020, Sept 07–11.
10. Fatemidokht H, Rafsanjani MK, Gupta BB, Hsu CH. Efficient and secure routing protocol based on artificial intelligence algorithms with UAV-assisted for vehicular ad hoc networks in intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems, Virginia USA.IEEE.2022.Oct 27–28.
11. Guo W, Atasoy B, van Blokland WB, Negenborn RR. A dynamic shipment matching problem in hinterland synchromodal transportation. Decision Support Systems. 2020;134: 1–13p.

Efficient matching between carriers and shippers is crucial in the logistics industry to optimize resource utilization and minimize costs. This paper puts forth a smart recommendation framework dependent on multilabel classification techniques to improve the carrier-shipper matching process. The method makes use of machine learning techniques to predict multiple relevant carrier options for a given shipment request. We present a comprehensive literature review on related works in carrier-shipper matching and multilabel classification methodologies. Our proposed system offers significant improvements over traditional methods by considering multiple factors simultaneously, resulting in more accurate and personalized recommendations. Experimental results demonstrate the effectiveness and feasibility of the proposed approach in enhancing the efficiency and effectiveness of carrier-shipper matching processes.

Keywords:Carrier-Shipper Matching, Multilabel Classification, Recommendation System, Logistics, Machine Learning

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Kato T, Higuchi M, Ohyama H, Kawamura Y, Ohno R. Container shipping route recommendation system by modeling logistics system with the ambient calculus. In: Proceedings of the International Conference on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), Australia. IEEE. 2022. Dec 17–19.
2. Santhiya R, GeethaPriya C. Machine Learning Techniques for Intelligent Transportation Systems-An overview. 12th International Conference on Computing Communication and Networking Technologies (ICCCNT),Kharagpur.IEEE.2021,July 06–08.
3. Berdeddouch A, Yahyaouy A, Benanni Y, Verde R. Deep based recommender system for relevant K pick-up points. International Conference on Intelligent Systems and Computer Vision (ISCV), Morocco.IEEE.2020,June 09–11.
4. Macheret PD, Savchuk RR, Shkuratov GI. Intelligent transport systems: analysis of the current state and prospects of development. International Conference on Quality Management, Transport and Information Security, Information Technologies (IT&QM&IS), Yaroslavl, Russian Federation.IEEE.2021, Sept. 06–10.
5. Malik MZ, Nazir S, Khan HU. Artificial Intelligence Based System on Enhancing the Capabilities of Transport System: A Systemic Literature Review. I Symposium on Industrial Electronics & Applications (ISIEA), Malaysia.IEEE.2023, July 15–16.
6. Efimenko DB, Filatov SA, Barabanova ES. Transport Management Systems as a Tool to Ensure the Flexibility and Efficiency of Cargo Delivery. I Intelligent Technologies and Electronic Devices in Vehicle and Road Transport Complex (TIRVED), Moscow, Russian Federation.IEEE.2022, Nov 10–11.
7. Lavanya R, Singh U, Tyagi V. A comprehensive survey on movie recommendation systems. International Conference on Artificial Intelligence and Smart Systems (ICAIS), Coimbatore India.IEEE.2021, March 25–27.
8. Roy D, Dutta M. A systematic review and research perspective on recommender systems. J. Big Data. 2022;9(1): 1–36p.
9. Tarasova VN, Koshcheeva EO, Ruchkin VB, Lyapina SY. Model of intelligent system for quality evaluation of services on design stage (transport and logistic business as an example). International Conference Quality Management, Transport and Information Security, Information Technologies (IT&QM&IS), Russia.IEEE.2020, Sept 07–11.
10. Fatemidokht H, Rafsanjani MK, Gupta BB, Hsu CH. Efficient and secure routing protocol based on artificial intelligence algorithms with UAV-assisted for vehicular ad hoc networks in intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems, Virginia USA.IEEE.2022.Oct 27–28.
11. Guo W, Atasoy B, van Blokland WB, Negenborn RR. A dynamic shipment matching problem in hinterland synchromodal transportation. Decision Support Systems. 2020;134: 1–13p.