Skip to main content

Analysis of Intelligent Software Implementations in Air Cargo Using Fermatean Fuzzy CODAS Method

  • Chapter
  • First Online:
Intelligent and Fuzzy Techniques in Aviation 4.0

Abstract

The chapter focuses on the problem of analyzing and selecting intelligent software in Air Cargo in the concept of Aviation 4.0. First, the notions, problems and challenges linked to air cargo are discussed. Recent developments, ongoing innovative projects and unfilled gaps in the area of intelligent air cargo software are presented. Next, the proposed method to analyze a select software to be used by air cargo companies is described. It is a modified version of one of the recent multi-criteria decision-making methods, called CODAS. Its original, crisp version and its existing fuzzy extensions are first presented. Next, an original extension of the method, using Fermatean fuzzy sets, is proposed. In the application section a logistics company is considered, which is facing the problem of selecting software supporting the air cargo process. The criteria are selected by experts holding various positions in the company, and three alternatives of air cargo software provider are determined. Then, the proposed method is applied to solve the intelligent software selection problem. Finally, conclusion and future research perspectives are given.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Valdes, R.A., Comendador, V.F.G., Sanz, A.R., Castan, J.P.: Aviation 4.0 more safety through automation and digitization. Aircraft Technol. 2(4), 25–41 (2018). https://doi.org/10.5772/intechopen.73688

  2. Durak, G., Tolga, A.C.: Process robot automation selection with MADM in airline cargo sector. In: International Conference on Intelligent and Fuzzy Systems, pp. 525–533. Springer, Cham (2020)

    Google Scholar 

  3. Tabares, D.A., Mora-Camino, F., Drouin, A.: A multi-time scale management structure for airport ground handling automation. J. Air Transp. Manage. 90, 101959 (2021)

    Article  Google Scholar 

  4. Wong, E.Y., Mo, D.Y., So, S.: Closed-loop digital twin system for air cargo load planning operations. Int. J. Comput. Integr. Manuf. 1–13 (2020). https://doi.org/10.1080/0951192X.2020.1775299

  5. Tolga, A.C., Durak, G.: Evaluating innovation projects in air cargo sector with fuzzy COPRAS. In: International Conference on Intelligent and Fuzzy Systems, pp. 702–710. Springer, Cham (2019)

    Google Scholar 

  6. Gumzej, R., Komkhao, M., Sodsee, S.: Design of an intelligent, safe and secure transport unit for the physical internet. In: International Conference on Computing and Information Technology, pp. 60–69. Springer, Cham (2020)

    Google Scholar 

  7. Cimato, S., Gianini, G., Sepehri, M., Asal, R., Damiani, E.: A cryptographic cloud-based approach for the mitigation of the airline cargo cancellation problem. J. Inform. Secur. Appl. 51, 102462 (2020)

    Google Scholar 

  8. Fang, Z., Mao, J.: Energy-efficient elevating transfer vehicle routing for automated multi-level material handling systems. IEEE Trans. Autom. Sci. Eng. 17(3), 1107–1123 (2019)

    Article  Google Scholar 

  9. Delgado, F., Sirhan, C., Katscher, M., Larrain, H.: Recovering from demand disruptions on an air cargo network. J. Air Transp. Manage. 85, 101799 (2020)

    Article  Google Scholar 

  10. Emde, S., Abedinnia, H., Lange, A., Glock, C.H.: Scheduling personnel for the build-up of unit load devices at an air cargo terminal with limited space. OR Spectrum 42(2), 397–426 (2020)

    Article  MathSciNet  Google Scholar 

  11. Giusti, I., Cepolina, E.M., Cangialosi, E., Aquaro, D., Caroti, G., Piemonte, A.: Mitigation of human error consequences in general cargo handler logistics: impact of RFID implementation. Comput. Ind. Eng. 137, 106038 (2019)

    Article  Google Scholar 

  12. Zhang, W., Chen, Y.: Intelligent technology related to warehousing and distribution in intelligent logistics. In: 2020 International Conference on Wireless Communications and Smart Grid (ICWCSG), pp. 175–177. IEEE (2020)

    Google Scholar 

  13. Wang, J., Lim, M.K., Zhan, Y., Wang, X.: An intelligent logistics service system for enhancing dispatching operations in an IoT environment. Transp. Res. Part E Logist. Transp. Rev. 135, 101886 (2020)

    Article  Google Scholar 

  14. Liu, R., Li, H.: Intelligent logistics service combination algorithm based on internet of things. J Intel Fuzzy Syst (Preprint), 1–8 (2020). https://doi.org/10.3233/JIFS-179854

  15. Xue, F., Dong, T., You, S., Liu, Y., Tang, H., Chen, L., Li, J. et al.: A hybrid many-objective competitive swarm optimization algorithm for large-scale multirobot task allocation problem. Int. J. Mach. Learn. Cybern. 1–15 (2020). https://doi.org/10.1007/s13042-020-01213-4

  16. Tang, Y., Zhang, J., Yuan, X., Hao, H., Wang, J., Zuo, Y., Wan, Y. et al.: Intelligent logistics system architecture design based on edge computing. In: 2019 Chinese Automation Congress (CAC), pp. 1682–1685. IEEE (2019)

    Google Scholar 

  17. Vijay, R., Prabhakar, T.V., Hegde, V., Rao, V.S., Prasad, R.V.: A heterogeneous PLC with BLE Mesh network for reliable and real-time smart cargo monitoring. In: 2019 IEEE International Symposium on Power Line Communications and its Applications (ISPLC), pp. 1–6. IEEE (2019)

    Google Scholar 

  18. Brandt, F., Nickel, S.: The air cargo load planning problem-a consolidated problem definition and literature review on related problems. Eur. J. Oper. Res. 275(2), 399–410 (2019)

    Article  Google Scholar 

  19. URL-1, https://www.iso.org/ics/55.180.20/x/

  20. Li, Z.Z.: Based on value analysis process optimization of air cargo transport. in advanced materials research, vol. 468, pp. 689–693. Trans Tech Publications Ltd. (2012). https://doi.org/10.4028/www.scientific.net/AMR.468-471.689

  21. URL-2, https://www.twill.net/blog/freight-documents/

  22. URL-3, https://www.shipafreight.com/learn-more/documents-list/

  23. Tian, C., Zhang, H., Li, F., Liu, T.: Air cargo load planning system: a rule-based optimization approach. In: 2009 IEEE/INFORMS International Conference on Service Operations, Logistics and Informatics, pp. 454–459. IEEE (2009)

    Google Scholar 

  24. Schäfer, J.D.: Luftfracht: Akteure–Prozesse–Märkte–Entwicklungen. Springer Fachmedien Wiesbaden GmbH (2019)

    Google Scholar 

  25. URL-4, https://lufthansa-cargo.com/eservices/efreight

  26. Brett, D.: ANA Cargo expands digital booking capabilities. Aircargo news (2020). https://www.aircargonews.net/airlines/ana-cargo-expands-digital-booking-capabilities/?fbclid=IwAR2x7gmQhO6ViNSRJrXOT4VATkYRhCd38rcYQJ_QfX3OrUCc_fjjuXhSPBM

  27. Brett, D.: Cargo.one the latest to integrate with IBS. Aircargo news (2020). https://www.aircargonews.net/technology/cargo-one-the-latest-to-integrate-with-ibs/

  28. Brett, D.: Nallian to offer RFS slot booking app at Brussels Airport. Aircargo news (2020). https://www.aircargonews.net/cargo-airport/nallian-to-offer-rfs-slot-booking-app-at-brussels-airport/

  29. Harry, R.: Kale logistics offers free trial of air waybill processing tool. Aircargo news (2020). https://www.aircargonews.net/technology/e-air-waybill/kale-logistics-offers-free-trial-of-air-waybill-processing-tool/

  30. Harry, R.: Air cargo community system launched at Atlanta Airport. Aircargo news (2020). https://www.aircargonews.net/technology/logistics-automation/air-cargo-community-system-launched-at-atlanta-airport/#:~:text=17%20%2F%2012%20%2F%202019&text=Hartsfield%E2%80%93Jackson%20Atlanta%20International%20Airport,cargo%20community%20system%20(ACCS)

  31. Harry, R.: AirAsia launches blockchain-based cargo booking platform. Aircargo news (2020). https://www.aircargonews.net/technology/logistics-automation/airasia-launches-blockchain-based-cargo-booking-platform/

  32. Harry, R.: Peli biothermal software update enables temperature tracking of mass shipments. Aircargo news (2020). https://www.aircargonews.net/technology/peli-biothermal-software-update-enables-temperature-tracking-of-mass-shipments/

  33. Chen, S.L.: Aerial logistics management for carrier onboard delivery. Naval Postgraduate School Monterey United States (2016)

    Google Scholar 

  34. Azadian, F., Murat, A., Chinnam, R.B.: An unpaired pickup and delivery problem with time dependent assignment costs: application in air cargo transportation. Eur. J. Oper. Res. 263(1), 188–202 (2017)

    Article  MathSciNet  Google Scholar 

  35. Harry, R.: CargoLogicAir operations boosted with CHAMP’s load planning tool. Aircargo news (2020). https://www.aircargonews.net/airlines/cargologicair-operations-boosted-with-champs-load-planning-tool/

  36. Keshavarz Ghorabaee, M., Zavadskas, E.K., Turskis, Z., Antucheviciene, J.: A new combinative distance-based assessment (CODAS) method for multi-criteria decision-making. Econ. Comput. Econ. Cybern. Stud. Res. 50(3) (2016)

    Google Scholar 

  37. Ghorabaee, M.K., Amiri, M., Zavadskas, E.K., Hooshmand, R., Antuchevičienė, J.: Fuzzy extension of the CODAS method for multi-criteria market segment evaluation. J. Bus. Econ. Manage. 18(1), 1–19 (2017)

    Article  Google Scholar 

  38. Dahooei, J.H., Zavadskas, E.K., Vanaki, A.S., Firoozfar, H.R., Keshavarz-Ghorabaee, M.: An evaluation model of business intelligence for enterprise systems with new extension of codas (CODAS-IVIF) (2018)

    Google Scholar 

  39. Ren, J.: Sustainability prioritization of energy storage technologies for promoting the development of renewable energy: a novel intuitionistic fuzzy combinative distance-based assessment approach. Renew. Energy 121, 666–676 (2018)

    Article  Google Scholar 

  40. Bolturk, E., Kahraman, C.: Interval-valued intuitionistic fuzzy CODAS method and its application to wave energy facility location selection problem. J. Intel. Fuzzy Syst. 35(4), 4865–4877 (2018)

    Article  Google Scholar 

  41. Yeni, F.B., Özçelik, G.: Interval-valued Atanassov intuitionistic Fuzzy CODAS method for multi criteria group decision making problems. Group Decis. Negot. 28(2), 433–452 (2019)

    Article  Google Scholar 

  42. Roy, J., Das, S., Kar, S., Pamučar, D.: An extension of the CODAS approach using interval-valued intuitionistic fuzzy set for sustainable material selection in construction projects with incomplete weight information. Symmetry 11(3), 393 (2019)

    Article  Google Scholar 

  43. Karagoz, S., Deveci, M., Simic, V., Aydin, N., Bolukbas, U.: A novel intuitionistic fuzzy MCDM-based CODAS approach for locating an authorized dismantling center: a case study of Istanbul. Waste Manage. Res. 38(6), 660–672 (2020)

    Article  Google Scholar 

  44. Seker, S.: A novel interval-valued intuitionistic trapezoidal fuzzy combinative distance-based assessment (CODAS) method. Soft. Comput. 24(3), 2287–2300 (2020)

    Article  MathSciNet  Google Scholar 

  45. Atanassov, K.: Intuitionistic fuzzy sets. Int. J. Bioauto. 20(1) (2016)

    Google Scholar 

  46. Bolturk, E.: Pythagorean fuzzy CODAS and its application to supplier selection in a manufacturing firm. J. Enterp. Inf. Manag. 31(4), 550–564 (2018)

    Article  Google Scholar 

  47. Bolturk, E., Kahraman, C.: A modified interval-valued pythagorean fuzzy CODAS method and evaluation of AS/RS technologies. J. Multiple-Valued Logic Soft Comput. 33, 415–429 (2019)

    Google Scholar 

  48. Peng, X., Ma, X.: Pythagorean fuzzy multi-criteria decision making method based on CODAS with new score function. J. Intell. Fuzzy Syst. 38(3), 3307–3318 (2020)

    Article  MathSciNet  Google Scholar 

  49. He, T., Zhang, S., Wei, G., Wang, R., Wu, J., Wei, C.: CODAS method for 2-tuple linguistic Pythagorean fuzzy multiple attribute group decision making and its application to financial management performance assessment. Technol. Econ. Develop. Econ. 26(4), 920–932 (2020)

    Article  Google Scholar 

  50. Yager, R.R.: Pythagorean membership grades in multicriteria decision making. IEEE Trans. Fuzzy Syst. 22(4), 958–965 (2013)

    Article  Google Scholar 

  51. GϋNDOĞDU, F.K., Kahraman, C.: Extension of CODAS with spherical fuzzy sets. J. Multiple-Valued Logic Soft Comput. 33, 481–505 (2019)

    Google Scholar 

  52. Karaşan, A., Boltürk, E., Gündoğdu, F.K.: Assessment of livability indices of suburban places of istanbul by using spherical fuzzy CODAS method. In: Decision Making with Spherical Fuzzy Sets, pp. 277–293. Springer, Cham (2020)

    Google Scholar 

  53. Kutlu Gündoğdu, F., Kahraman, C.: Spherical fuzzy sets and spherical fuzzy TOPSIS method. J. Intel. Fuzzy Syst. 36(1), 337–352 (2019)

    Article  Google Scholar 

  54. Yalçın, N., Yapıcı Pehlivan, N.: Application of the fuzzy CODAS method based on fuzzy envelopes for hesitant fuzzy linguistic term sets: a case study on a personnel selection problem. Symmetry 11(4), 493 (2019)

    Article  Google Scholar 

  55. Karasan, A., Zavadskas, E.K., Kahraman, C., Keshavarz-Ghorabaee, M.: Residential construction site selection through interval-valued hesitant fuzzy CODAS method. Informatica 30(4), 689–710 (2019)

    Article  Google Scholar 

  56. Torra, V.: Hesitant fuzzy sets. Int. J. Intell. Syst. 25(6), 529–539 (2010)

    MATH  Google Scholar 

  57. Liu, H., Rodríguez, R.M.: A fuzzy envelope for hesitant fuzzy linguistic term set and its application to multicriteria decision making. Inf. Sci. 258, 220–238 (2014)

    Article  MathSciNet  Google Scholar 

  58. Boltürk, E., Karaşan, A.: Interval valued neutrosophic CODAS method for renewable energy selection. In: Liu, J., Lu, J., Xu, Y., Martinez, L., Kerre, E. (eds.) Data Science and Knowledge Engineering for Sensing Decision Support, pp. 1026–1033 (2018)

    Google Scholar 

  59. Karasan, A., Bolturk, E., Kahraman, C.: An integrated methodology using neutrosophic CODAS & fuzzy inference system: assessment of livability index of urban districts. J. Intel. Fuzzy Syst. 36(6), 5443–5455 (2019)

    Article  Google Scholar 

  60. Rivieccio, U.: Neutrosophic logics: prospects and problems. Fuzzy Sets Syst. 159(14), 1860–1868 (2008)

    Article  MathSciNet  Google Scholar 

  61. Tüysüz, N., Kahraman, C.: CODAS method using Z-fuzzy numbers. J. Intel. Fuzzy Syst. 38(2), 1649–1662 (2020)

    Article  Google Scholar 

  62. Zadeh, L.A.: A note on Z-numbers. Inf. Sci. 181(14), 2923–2932 (2011)

    Article  Google Scholar 

  63. Peng, X., Garg, H.: Algorithms for interval-valued fuzzy soft sets in emergency decision making based on WDBA and CODAS with new information measure. Comput. Ind. Eng. 119, 439–452 (2018)

    Article  Google Scholar 

  64. Ahmad, B., Kharal, A.: On fuzzy soft sets. Adv. Fuzzy Syst. 586507 (2009). https://doi.org/10.1155/2009/586507

  65. Senapati, T., Yager, R.R.: Fermatean fuzzy weighted averaging/geometric operators and its application in multi-criteria decision-making methods. Eng. Appl. Artif. Intell. 85, 112–121 (2019)

    Article  Google Scholar 

  66. Du, W.S.: Weighted power means of q-rung orthopair fuzzy information and their applications in multiattribute decision making. Int. J. Intell. Syst. 34(11), 2835–2862 (2019)

    Article  Google Scholar 

  67. Liu, D., Liu, Y., Chen, X.: Fermatean fuzzy linguistic set and its application in multicriteria decision making. Int. J. Intell. Syst. 34(5), 878–894 (2019)

    Article  MathSciNet  Google Scholar 

  68. Senapati, T., Yager, R.R.: Some new operations over Fermatean fuzzy numbers and application of Fermatean fuzzy WPM in multiple criteria decision making. Informatica 30(2), 391–412 (2019)

    Article  Google Scholar 

  69. Senapati, T., Yager, R.R.: Fermatean fuzzy sets. J. Ambient Intel. Human. Comput. 11(2), 663–674 (2020)

    Article  Google Scholar 

  70. Buyukozkan, G., Göçer, F.: Prioritizing the strategies to enhance smart city logistics by intuitionistic fuzzy CODAS. In: 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (EUSFLAT 2019). Atlantis Press (2019)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Irem Ucal Sari .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ucal Sari, I., Kuchta, D., Sergi, D. (2022). Analysis of Intelligent Software Implementations in Air Cargo Using Fermatean Fuzzy CODAS Method. In: Kahraman, C., Aydın, S. (eds) Intelligent and Fuzzy Techniques in Aviation 4.0. Studies in Systems, Decision and Control, vol 372. Springer, Cham. https://doi.org/10.1007/978-3-030-75067-1_7

Download citation

Publish with us

Policies and ethics