Selection of Indoor Positioning Technology in Shipyards by Fuzzy MULTIMOORA and Fuzzy COPRAS Methods

Abstract

GPS (Global Positioning Systems), which have been successfully used to locate and track assets in open areas, cannot show the same performance in closed environments. In cases where GPS cannot provide accurate enough data indoors, indoor positioning systems (IMKS) are being developed. These technologies provide services based on Infrared, Ultrasonic sound and Radio frequency based technologies. Each of indoor positioning technologies suited to be used for specific purposes, although the areas of the shipyard in difficult conditions like all living and inanimate objects required for positioning and monitoring the accuracy, reliability, cost, energy consumption, scalability, and developed a technology that allows other requests could not be convenient. Some technologies perform very well in Energy consumption, while others may be better in terms of coverage area. Therefore, the choice of indoor positioning technology comes across as a multi-purpose decision problem. Among the IMK technologies for shipyard sites with heavy and large medal blocks and other signal interrupting obstacles, it seems that radio frequency-based systems may be more suitable from the point of view of the shipyard site than other technologies. Therefore, in this article, a multi-criteria decision model is being developed to determine which radio-based technologies will be used as Indoor Positioning Technology in Shipyards, and the problem is being solved with Fuzzy MULTIMOORA and Fuzzy COPRAS Methods. An application is being carried out at the SEDEF shipyard.

Keywords

• Shipyard
• Indoor positioning
• Fuzzy Multi-Criteria Group Decision Making Method
• Fuzzy MULTIMOORA
• Fuzzy COPRAS.

Bulanık MULTIMOORA ve Bulanık COPRAS Yöntemleriyle Tersanelerde İç Mekân Konumlandırma Teknolojisi Seçimi

Öz

Açık alanlarda varlıkların yerini bulmada ve takip etmede başarılı bir şekilde kullanılan GPS (Küresel Konumlandırma Sistemleri) aynı performansı kapalı ortamlarda gösterememektedir. İç mekanlarda GPS’in yeterince kesin veri sağlayamadığı durumlarda, iç mekan konumlandırma sistemleri (IMK) geliştirilmektedir. Bu teknolojiler Kızılötesi, Ultrasonik ses ve Radyo frekansı tabanlı teknolojilere dayalı olarak hizmet sunmaktadırlar. İç mekan konumlandırma teknolojilerinin her birinin belirli amaçlar için kullanılması uygun olsa da, tersane sahası gibi zorlu koşullarda canlı ve cansız tüm nesnelerin konumlandırılması ve takibi için gereken doğruluğu, güvenilirliği, maliyeti, enerji tüketimini, ölçeklenebilirliği ve diğer istekleri sağlayan uygun bir teknoloji geliştirilememiştir. Bazı teknolojiler Enerji tüketimimnde çok iyi performans sergilerken, bazıları kapsam alanı açısından daha iyi olabilmektedirler. Bu nedenle, İç mekan konumlandırma teknoloji seçimi çok amaçlı bir karar problemi olarak karşımıza çıkmaktadır. Ağır ve büyük medal blokların ve diğer sinyal kesici engellerin olduğu tersane sahaları için İMK teknolojiler içerisinde, radyo frekansı tabanlı sistemler diğer teknolojilere göre tersane sahası açısından daha uygun olabilecekleri görülmektedir. Bu nedenle bu makalede radyo tabanlı teknolojilerin hangisinin Tersanelerde İç Mekân Konumlandırma Teknolojisi olarak kullanılacağını belirlemek için çok ölçütlü bir karar modeli geliştirilmekte ve Bulanık MULTIMOORA ve Bulanık COPRAS Yöntemleriyle problem çözülmeye çalışılmaktadır. SEDEF tersanesinde bir uygulama gerçekleştirilmektedir.

Anahtar Kelimeler

• Tersane
• İç mekan konumlandırma
• Bulanık Çok Kriterli Grup Karar Verme
• Bulanık MULTIMOORA
• Bulanık COPRAS.

References

1. F. Zafari, A. Gkelias and K. K. Leung, "A Survey of Indoor Localization Systems and Technologies," in IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2568-2599, thirdquarter 2019, doi: 10.1109/COMST.2019.2911558.
2. C. Liu, H. Wang, M. Liu and P. Li, Research and Analysis of Indoor Positioning Technology, 2021 4th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE), 2021, pp. 1212-1217, doi: 10.1109/AEMCSE51986.2021.00248.
3. WCSS. Simoes, GS. Machado, AMA Sales, MM de Lucena, N Jazdi, VF de Lucena, A Review of Technologies and Techniques for Indoor Navigation Systems for the Visually Impaired. SENSORS. 2020;20(14):3935. doi:10.3390/s20143935
4. A. Hameed and H. A. Ahmed, Survey on indoor positioning applications based on different technologies, 2018 12th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS), 2018, pp. 1-5, doi: 10.1109/MACS.2018.8628462.
5. H.K. Lu, P.C, Lin K.C. Chu, et al. Development and evaluation of a Beacon-based indoor positioning and navigating system for the visually impaired. Journal of Intelligent & Fuzzy Systems. 2019;37(4):4665-4675. doi:10.3233/JIFS-179301
6. S.He, Chan, S.H.G. Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons. IEEE Commun. Surv. Tutor. 2016, 18, 466–490.
7. L. Fasano, I. Sergi, A. Almeida, A. B. Jayo, P. Rametta and L. Patrono, Performance Evaluation of Indoor Positioning Systems based on Smartphone and Wearable Device, 2020 5th International Conference on Smart and Sustainable Technologies (SpliTech), 2020, pp. 1-5, doi: 10.23919/SpliTech49282.2020.9243796.
8. J. C. R. Bırsan, F. Moldoveanu, A. Moldoveanu, M. Dascalu and A. MORAR, Key Technologies for Indoor Positioning Systems, 2019 18th RoEduNet Conference: Networking in Education and Research (RoEduNet), 2019, pp. 1-7, doi: 10.1109/ROEDUNET.2019.8909406.

9. M. Rafiuzzaman, I. Çil, (2016). A fuzzy logic based agricultural decision support system for assessment of crop yield potential using shallow ground water table. International Journal of Computer Applications, 149(9), 20-31.
10. H. Palabıyık, I, Çil, and Toklu, M.C., Determining Firm Locations of Long-Term Interns with a Fuzzy Logic Approach: Application in the Applied Training Model. Academic Platform Journal of Engineering and Science, 8(1), 146-154.
11. M., Ünver, and I. Cil, (2020). Material selection by using fuzzy complex proportional assessment. Emerging Materials Research, 9(1), 93-98.
12. I. Cil, Y.S, Turkan, An ANP-based assessment model for lean enterprise transformation. Int J Adv Manuf Technol 64, 1113–1130 (2013). https://doi.org/10.1007/s00170-012-4047-x
13. S. Mun, M. Nam, J. Lee, K. Doh, G.Park, H. Lee, D. Kim, J. Lee, Sub-assembly welding robot system at shipyards; Proceedings of the 2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM); Busan, Korea. 7–11 July 2015; pp. 1502–1507.
14. S.F. Wong, Y. Zheng, The effect of metal noise factor to RFID location system; Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management; Bangkok, Thailand. 10–13 December 2013; pp. 310–314.
15. D.D. Deavours, Improving the near-metal performance of UHF RFID tags; Proceedings of the IEEE International Conference on RFID; Orlando, FL, USA. 14–16 April 2010; pp. 187–194.
16. M. Heiss, R. Hildebrant, High-temperature UHF RFID sensor measurements in a full-metal environment; Proceedings of the 2013 European Conference on Smart Objects, Systems and Technologies (SmartSysTech); Nuremberg, Germany. 11–12 June 2013.
17. M.Y. Kim, K. Ko, H.S. Cho, J. Kim, Visual sensing and recognition of welding environment for intelligent shipyard welding robots; Proceedings of the 13th IEEE/RSJ International Conference on Intelligent Robots and Systems; Takamatsu, Japan. 31 Oct–5 Nov 2000; pp. 2159–2165.
18. S. Kawakubo, A. Chansavang, S. Tanaka, T. Iwasaki, Wireless network system for indoor human positioning; Proceedings of the 1st International Symposium on Wireless Pervasive Computing; Phuket, Thailand. 16–18 January 2006.
19. C. Pérez-Garrido, F.J. González-Castaño, D. Chaves-Díeguez, P.S. Rodríguez-Hernández, Wireless remote monitoring of toxic gases in Shipbuilding. Sensors. 2014;14:2981–3000. doi: 10.3390/s140202981.
20. M.A. Do Amaral Bichet, E.K.Hasegawa, R.Solé, A. Núñez, Utilization of hyper environments for tracking and monitoring of processes and supplies in construction and assembly industries; Proceedings of the Symposium on Computing and Automation for Offshore Shipbuilding (NAVCOMP); Rio Grande, Brazil. 14–15 March 2013; pp. 81–86.
21. M. Lu, W. Chen, X. Shen, H.-C. Lam, J. Liu, Positioning and tracking construction vehicles in highly dense urban areas and building construction sites, Autom. Constr. 16 (5) (2007) 647–656, https://doi.org/10.1016/j.autcon.2006.11.0
22. C.T. LI, J. C. P. CHENG, K. CHEN, Top 10 technologies for indoor positioning on construction sites. Automation in Construction,1, 118, 2020. DOI 10.1016/j.autcon.2020.103309.
23. M.M. Fouladgar, A., Yazdani-Chamzini, A., Lashgari, E. K., Zavadskas, Z.Turskis, (2012). Maintenance strategy selection using AHP and COPRAS under fuzzy environment. International Journal of Strategic Property Management, 16(1): 85-104. B.,Oztaysi, C., Kahraman, S. C.,Onar, & I.Otay, (2020). Indoor location tracking technology evaluation by using spherial fuzzy TOPSIS method. In Developments of Artificial Intelligence Technologies in Computation and Robotics: Proceedings of the 14th International FLINS Conference (FLINS 2020) (pp. 173-181).
24. L.,Doulos, A.,Tsangrassoulis, & F. V. Topalis, (2014). Multi-criteria decision analysis to select the optimum position and proper field of view of a photosensor. Energy conversion and management, 86, 1069-1077.