A research process model for determining the location and priorities of virtual store drone warehouse

Abstract

Today, e-commerce is growing and developing in direct proportion with the development of the internet. With these developments in e-commerce and the changing marketing world, businesses have started to produce new solutions in this sector. The establishment of virtual store warehouses in certain points of the city and the delivery of orders to customers via drone vehicles are the most important of these solutions. The aim of the research is to present and test a research model for determining the location and priorities of the virtual store warehouses (Virtual Store Drone Warehouse) that provide retail product transportation with drone in metropolises. Naturally, distance and density data are used in the analysis of such problems. In the study, London, which is one of the biggest metropolises of the world, is taken as an example. Depending on the model of the research, in the first stage, the points indicating the density in the metropolis were determined. In the next stage, the location data (latitude-longitude) of these points in London Zone-2 was collected via Google maps. In the next stage of the model, the location data of the densely populated areas were grouped using x-means clustering algorithm, one of the data mining techniques. Then, the performance evaluation of the clusters was carried out using TOPSIS, one of the Multi-Criteria Decision-Making (MCDM) methods

Keywords

• E-commerce,Virtual Store Drone Warehouse,Clustering Algorithm,Multi-Criteria Decision Making,Warehouse Site Selection

Sanal mağaza drone depo yer ve önceliklerinin tespitine yönelik bir araştırma süreci modeli

Abstract

Günümüzde e-ticaret, İnternet’in gelişimi ile doğru orantılı olarak büyüme ve gelişme göstermektedir. E-ticarette yaşanan bu gelişmeler ve değişen pazarlama dünyası ile birlikte işletmeler bu sektörde yeni çözümler üretmeye başlamışlardır. Şehrin belirli noktalarına sanal mağaza depolarının kurulması ve müşterilere siparişlerin bu depolardan drone araçlar ile ulaştırılması bu çözümlerden en önemlisidir. Araştırmanın amacı, metropollerde drone ile perakende ürün taşımacılığı sağlayan sanal mağaza depolarının (Sanal Mağaza Drone Depo) yer ve önceliklerini tespitine yönelik bir araştırma modeli sunmak ve test etmektir. Doğal olarak bu tür problemlerin çözümünde uzaklık ve yoğunluk verilerinin analizine başvurulmaktadır. Çalışmada dünyanın en büyük metropollerinden olan Londra örnek alınmıştır. Araştırmanın modeline bağlı olarak, ilk aşamada metropoldeki yoğunluğu işaret eden noktalar belirlenmiştir. Sonraki aşamada, Google haritalar üzerinde, Londra Zone-2 içerisinde yer alan, bu noktaların konum verileri (enlem-boylam) toplanmıştır. Modelin bir sonraki aşamasında yoğun yerleşim bölgelerinin konum verileri, veri madenciliği tekniklerinden x-ortalamalar kümeleme algoritması kullanılarak gruplandırılmıştır. Ardından Çok kriterli Karar Verme (ÇKKV) yöntemlerinden TOPSIS kullanılarak kümelerin performans değerlendirmesi yapılmıştır

Keywords

• E-ticaret,Sanal Mağaza Drone Depo,Kümeleme Algoritmaları,Çok Kriterli Karar Verme,Depo Yeri Seçimi

References

1. Aktepe, A., ve Ersöz, S. (2014). AHP-VIKOR ve MOORA yöntemlerinin depo yeri seçim probleminde uygulanması. Journal of Industrial Engineering (Turkish Chamber of Mechanical Engineers), 25.
2. Alborzi, M., (2008), Augmenting system dynamics with genetic algorithm and TOPSIS multivariate ranking module for multi-criteria optimization, Islamic Azad University ,Sience and Research Branch, Atisaz, Evin, Tehran, Iran, s.1-11.
3. Armstrong, J. J., Zhu, M., Hirdes, J. P., ve Stolee, P. (2012). K-means cluster analysis of rehabilitation service users in the Home Health Care System of Ontario: examining the heterogeneity of a complex geriatric population. Archives of Physical Medicine and Rehabilitation, 93(12), 2198-2205.
4. Ashrafzadeh, M., Rafiei, F. M., Isfahani, N. M., ve Zare, Z. (2012). Application of fuzzy TOPSIS method for the selection of warehouse location: A case study. Interdisciplinary Journal of Contemporary Research in Business, 3(9), 655-671.
5. Benoit, G. (2002). Data mining. Annual Review Of Information Science and Technology, 36(1), 265-310.
6. Brust, M. R., Akbaş, M. I., ve Turgut, D. (2016, April). VBCA: A virtual forces clustering algorithm for autonomous aerial drone systems. In 2016 Annual IEEE Systems Conference (SysCon) (pp. 1-6). IEEE.
7. Can, M. B., Eren, Ç., Koru, M., Özkan, Ö., ve Rzayeva, Z. (2012). Veri kümelerinden bilgi keşfi: veri madenciliği. Başkent Üniversitesi Tıp Fakültesi XIV. Öğrenci Sempozyumu, Ankara.
8. Chakraborty, D. (2001). Structural quantization of vagueness in linguistic expert opinions in an evaluation programme. Fuzzy Sets and Systems, 119(1), 171-186.

9. Chen, L. D., ve Tan, J. (2004). Technology adaptation in e-commerce:: key determinants of virtual stores acceptance. European Management Journal, 22(1), 74-86.
10. Chu, T. C. (2002). Facility location selection using fuzzy TOPSIS under group decisions. International Journal Of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(6), 687-701.
11. Chu, T. C. (2002). Facility location selection using fuzzy TOPSIS under group decisions. International journal of uncertainty, fuzziness and knowledge-based systems, 10(06), 687-701.
12. Dubes, R. ve Jain, A. K. (1976). Clustering techniques: the user's dilemma. Pattern Recognition, 8(4), 247-260.
13. Erturan, İ. E., ve Ergin, E. (2018). Büyük verinin muhasebe ve denetim alanlarına uyumu. Akademik Sosyal Araştırmalar Dergisi, 81(6), 208-222.
14. Ferrandez, S. M., Harbison, T., Weber, T., Sturges, R., ve Rich, R. (2016). Optimization of a truck-drone in tandem delivery network using k-means and genetic algorithm. Journal of Industrial Engineering and Management (JIEM), 9(2), 374-388.
15. Gomez-Muñoz, V. M., ve Porta-Gándara, M. A. (2002). Local wind patterns for modeling renewable energy systems by means of cluster analysis techniques. Renewable Energy, 25(2), 171-182.
16. Gözaydın, O., ve Can, T. (2013). Deprem yardım istasyonları için lojistik merkezi seçimi: Türkiye örneği. Journal of Aeronautics & Space Technologies/Havacilik ve Uzay Teknolojileri Dergisi, 6(2), 17-31.
17. İşlier, A. (1997). Tesis Planlaması: Mühendislik Mimarlık Fakültesi Endüstri Mühendisliği Bölümü Ders Notları. Eskişehir Osmangazi Üniversitesi Basım Evi, Eskişehir
18. Karmaker, C. ve Saha, M. (2015). Optimization of warehouse location through fuzzy multi-criteria decision making methods. Decision Science Letters, 4(3), 315-334.
19. Kengpol, A., Rontlaong, P., ve Tuominen, M. (2013). A decision support system for selection of solar power plant locations by applying fuzzy AHP and TOPSIS: An Empirical Study. Journal of Software Engineering and Applications, 6(9), 470-481.
20. Koçel, T. (2014). İşletme Yöneticiliği. İstanbul: Beta Basım Yayım.
21. Lellamo, S., Lehtomaki, J. J., ve Khan, Z. (2017, June). Placement of 5g drone base stations by data field clustering. In 2017 IEEE 85th Vehicular Technology Conference (VTC Spring) (pp. 1-5). IEEE.
22. Manokaran, E.., Subhashini, S., Senthilvel, S., Muruganandham, R ve Ravichandran, K., (2011). Application of multi criteria decision making tools and validation with optimization Technique-Case Study using TOPSIS, ANN and SAW. International Journal of Management and Business Studies, India, c.1, S.3, s.112-115.
23. Park, G. Y., Kim, H., Jeong, H. W., ve Youn, H. Y. (2013, March). A novel cluster head selection method based on k-means algorithm for energy efficient wireless sensor network. In Advanced Information Networking and Applications Workshops (WAINA), 2013 27th International Conference on (pp. 910-915). IEEE.
24. Pelleg, D., ve Moore, A. W. (2000, June). X-means: Extending k-means with efficient estimation of the number of clusters. In Icml (Vol. 1), 727-734.
25. Skondras, E., Siountri, K., Michalas, A., ve Vergados, D. D. (2018, July). A route selection scheme for supporting virtual tours in sites with cultural interest using drones. In 2018 9th International Conference on Information, Intelligence, Systems and Applications (IISA) (pp. 1-6). IEEE.
26. Sostaric, D., ve Mester, G. (2019). Drone Localization Using Ultrasonic Tdoa And Rss Signal–Integration Of. In Patrons Of The Conference (p. 11).
27. Tucker, C. S., Kim, H. M., Barker, D. E., ve Zhang, Y. (2010). A relieff attribute weighting and x-means clustering methodology for top-down product family optimization. Engineering Optimization, 42(7), 593-616.
28. Vrechopoulos, A. P., O’keefe, R. M., Doukidis, G. I., ve Siomkos, G. J. (2004). Virtual store layout: an experimental comparison in the context of grocery retail. Journal of Retailing, 80(1), 13-22.
29. Weatherill, G., ve Burton, P. W. (2009). Delineation of shallow seismic source zones using k-means cluster analysis, with application to the Aegean region. Geophysical Journal International, 176(2), 565-588.
30. Winters, J. C., Groenier, K. H., Sobel, J. S., Arendzen, H. H., ve Meyboom-de Jongh, B. (1997). Classification of shoulder complaints in general practice by means of cluster analysis. Archives of Physical Medicine and Rehabilitation, 78(12), 1369-1374.
31. Xu, L., ve Yang, J. B. (2001). Introduction to Multi-Criteria Decision Making and the Evidential Reasoning Approach (pp. 1-21). Manchester: Manchester School of Management.
32. Xu, R. ve Wunsch, D. (2008). Clustering (Vol. 10). John Wiley & Sons.
33. Yang, J., ve Lee, H. (1997). An AHP decision model for facility location selection. Facilities,15(9/10), 241-254.
34. Yavuz, S. ve Deveci, M. (2014). Bulanık TOPSIS ve bulanık VIKOR yöntemleriyle alışveriş merkezi kuruluş yeri seçimi ve bir uygulama. Ege Akademik Bakış, 14 (3), 463-479.
35. Yoon, K. P. ve Hwang, C, (1995), Multible Attribute Decision Making:An Introduction, Sage University Paper Series on Quantitative Applications in the Social Science, 07-104. Thousand Oaks. CA: Sage.