Vehicle routing software selection for last mile delivery companies using Fermatean fuzzy-based model

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Kara K., Yalçın G. C., Simic V., GÜROL P., Pamucar D.

Engineering Applications of Artificial Intelligence, cilt.131, 2024 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 131
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1016/j.engappai.2023.107813
  • Dergi Adı: Engineering Applications of Artificial Intelligence
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Anahtar Kelimeler: Alternative ranking order method accounting for two-step normalization, Fermatean fuzzy sets, Last mile delivery, Preference selection index, Vehicle routing software selection
  • İstanbul Ticaret Üniversitesi Adresli: Evet

Özet

Vehicle routing software (VRS) is utilized by last mile delivery (LMD) companies for route optimization. The problem of VRS selection is of paramount importance for LMD companies. In this research, a VRS selection model tailored to LMD companies is developed and proposed. This model is based on Fermatean fuzzy sets (FFS). The FFS-preference selection index (PSI) method is proposed for weighting the criteria. The FFS-alternative ranking order method accounting for two-step normalization (AROMAN) method is defined for ranking the VRS alternatives. This hybrid approach, developed as FFS-PSI-AROMAN, incorporates the FFYWA operator based on Yager t-norm and t-conorm operations as the aggregation operator to enhance the strength of aggregation operations. Additionally, an algorithm has been developed for the model. The developed model is applied through a real-life case study conducted in an LMD company operating in Turkey. An expert group is formed, criteria are defined, alternative VRS options are identified, and the proposed algorithm is employed to make the optimal VRS selection. Sensitivity analysis scenarios are created, and robustness tests are conducted to evaluate the model's reliability. Comprehensive implications for both the research and managerial insights are provided, along with recommendations for future research endeavors.