Cargo Company Recommendation Study Based on Probabilistic Linguistic Term Set

Abstract

The global economic structure is the main reason for changes in consumption habits and consumer behavior. Developing information technologies direct producers and consumers to e-commerce. Cargo services are an important link in the chain in the fast and effective operation of e-commerce. The growth in e-commerce has a driving force in the development of cargo services and cargo companies. Cargo companies can survive in global competition by being preferred by customers and increasing their number of customers. The change in the number of customers occurs by communicating the satisfaction or dissatisfaction with the cargo company to potential customers. This study deals with the preference levels of cargo companies serving in Turkey according to customer suggestions. The data obtained from the survey evaluations are processed and recommendation ranking calculations are made for cargo companies. Probabilistic Linguistic Term Sets (PLTS) are used to eliminate customer ambiguities in survey evaluations. Alternative cargo company recommendations are ranked based on the customers' past service experiences from cargo companies. Aras Cargo, MNG Cargo, PTT Cargo, Surat Cargo, UPS Cargo, Yurtiçi Cargo companies are evaluated according to price, personnel, speed, reliability and network qualities. The maximum deviation optimization method based on the Lagrangian function is used to calculate the weights of the cargo companies' attributes. The probabilistic linguistic cosine similarity method compares cargo companies pairwise under attributes and a similarity matrix is obtained for six cargo companies. The similarity matrix defines the alternative cargo company recommendation ranking based on customers' past experiences.

Keywords

• Probabilistic Linguistic Term Sets
• Cargo service
• E-commerce
• Recommendation

References

1. [1] A. Gülbaşı, “E-ticaret kullanıcılarına ait e-hizmet kalitesinin müşteri memnuniyeti üzerine etkisi”, Toplum Ekonomi ve Yönetim Dergisi, vol. 3, no. 1, pp. 22–39, 2022.
2. [2] C. Cui, M. Wei, L. Che, S. Wu, and E. Wang, “Hotel recommendation algorithms based on online reviews and probabilistic linguistic term sets”, Expert Systems with Applications, vol. 210, p. 118503, 2022.
3. [3] H. Hwangbo, Y. S. Kim, and K. J. Cha, “Recommendation system development for fashion retail e-commerce”, Electronic Commerce Research and Applications, vol. 28, pp. 94–101, 2018.
4. [4] M. Zihayat, A. Ayanso, X. Zhao, H. Davoudi, and A. An, “A utility-based news recommendation system”, Decision support systems, vol. 117, pp. 14–27, 2019.
5. [5] J. Lin, H. Pu, Y. Li, and J. Lian, “Intelligent recommendation system for course selection in smart education”, Procedia Computer Science, vol. 129, pp. 449–453, 2018.
6. [6] Z. Abbasi-Moud, H. Vahdat-Nejad, and J. Sadri, “Tourism recommendation system based on semantic clustering and sentiment analysis”, Expert Systems with Applications, vol. 167, p. 114324, 2021.
7. [7] Y. Liu, C. Lyu, Z. Liu, and J. Cao, “Exploring a large-scale multi-modal transportation recommendation system”, Transportation Research Part C: Emerging Technologies, vol. 126, p. 103070, 2021.
8. [8] Z. Cui et al., “Personalized recommendation system based on collaborative filtering for IoT scenarios”, IEEE Transactions on Services Computing, vol. 13, no. 4, pp. 685–695, 2020.

9. [9] P. Nitu, J. Coelho, and P. Madiraju, “Improvising personalized travel recommendation system with recency effects”, Big Data Mining and Analytics, vol. 4, no. 3, pp. 139–154, 2021.
10. [10] X. Wang, Z. Xu, Q. Wen, and H. Li, “A multidimensional decision with nested probabilistic linguistic term sets and its application in corporate investment”, Economic Research-Ekonomska Istraživanja, pp. 1–19, 2021.
11. [11] G. Duran, “Kargo hizmetlerinin tüketici davranışlarına etkisi üzerine bir uygulama”, Strategic Public Management Journal, vol. 3, no. 5, pp. 109–123, 2017.
12. [12] Q. Pang, H. Wang, and Z. Xu, “Probabilistic linguistic term sets in multi-attribute group decision making”, Information Sciences, vol. 369, pp. 128–143, 2016.
13. [13] J.-Y. Dong, Y. Chen, and S.-P. Wan, “A cosine similarity based QUALIFLEX approach with hesitant fuzzy linguistic term sets for financial performance evaluation”, Applied Soft Computing, vol. 69, pp. 316–329, 2018.
14. [14] Y. Song and G. Li, “A large-scale group decision-making with incomplete multi-granular probabilistic linguistic term sets and its application in sustainable supplier selection”, Journal of the Operational Research Society, vol. 70, no. 5, pp. 827–841, 2019.
15. [15] D. Liang, Z. Dai, and M. Wang, “Assessing customer satisfaction of O2O takeaway based on online reviews by integrating fuzzy comprehensive evaluation with AHP and probabilistic linguistic term sets”, Applied Soft Computing, vol. 98, p. 106847, 2021.
16. [16] S. Luo, H. Zhang, J. Wang, and L. Li, “Group decision-making approach for evaluating the sustainability of constructed wetlands with probabilistic linguistic preference relations”, Journal of the Operational Research Society, vol. 70, no. 12, pp. 2039–2055, 2019.
17. [17] İ. Asoğlu and E. Tamer, “AHP, TOPSIS, PROMETHEE yöntemleri ile bir işletme için kargo şirketi seçimi”, Yalova Sosyal Bilimler Dergisi, vol. 8, no. 16, pp. 102–122, 2018.
18. [18] H. E. Atmaca and D. Tuğrul, “Kargo şirketi seçimine yönelik kriterlerin belirlenmesinde Türkiye genelinde bir saha araştırması”, Çukurova Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, vol. 19, no. 2, pp. 65–79, 2015.
19. [19] X. Gou and Z. Xu, “Double hierarchy linguistic term set and its extensions”, in Double Hierarchy Linguistic Term Set and Its Extensions, Springer, 2021, pp. 1–21.
20. [20] S.-H. Lee, “Using fuzzy AHP to develop intellectual capital evaluation model for assessing their performance contribution in a university”, Expert systems with applications, vol. 37, no. 7, pp. 4941–4947, 2010.
21. [21] B. C. Giri, M. U. Molla, and P. Biswas, “TOPSIS method for MADM based on interval trapezoidal neutrosophic number”, Neutrosophic Sets and Systems, vol. 22, pp. 151–167, 2018.
22. [22] S.-H. Yoon and J.-W. Park, “A study of the competitiveness of airline cargo services departing from Korea: Focusing on the main export routes”, Journal of Air Transport Management, vol. 42, pp. 232–238, 2015, doi: https://doi.org/10.1016/j.jairtraman.2014.11.004.
23. [23] S.-H. S. Huang and W.-K. K. Hsu, “Evaluating the service requirements of combination air cargo carriers”, Asia Pacific Management Review, vol. 21, no. 1, pp. 1–8, 2016, doi: https://doi.org/10.1016/j.apmrv.2015.05.001.
24. [24] Y. Park, J. K. Choi, and A. Zhang, “Evaluating competitiveness of air cargo express services”, Transportation Research Part E: Logistics and Transportation Review, vol. 45, no. 2, pp. 321–334, 2009, doi: https://doi.org/10.1016/j.tre.2008.09.004.
25. [25] B. Esra, S. Çizmecioğlu, and A. Çalık, “Kaos durumu altında hava kargo şirketi seçimi: Bütünleşik Bayesian BWM ve WASPAS çerçevesi”, Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 38, no. 3, pp. 1586–1600, 2023.
26. [26] M. Deste and A. G. Savaşkan, “E-ticaret işletmelerinin kargo firması seçimi üzerine VIKOR yöntemiyle bir uygulama”, Uluslararası Anadolu Sosyal Bilimler Dergisi, vol. 5, no. 1, pp. 4–21, 2021.
27. [27] A. Ulutaş, “SWARA tabanlı CODAS yöntemi ile kargo şirketi seçimi”, MANAS Sosyal Araştırmalar Dergisi, vol. 9, no. 3, pp. 1640–1647, 2020.
28. [28] O. Kulak, A. Genç, and M. E. Taner, “A decision making tool considering risk assessment of sub-contracting agents for an air cargo shipment planning problem”, Journal of Air Transport Management, vol. 69, pp. 123–136, 2018, doi: https://doi.org/10.1016/j.jairtraman.2018.02.005.