Evaluation of Potential Logistics Village Alternatives Using Bayesian Best-Worst Method

Authors

DOI:

https://doi.org/10.22105/opt.v1i1.35

Keywords:

Multi-Criteria Decision-Making Methods, Logistic Village, Bayesian Best-Worst Method

Abstract

Logistics centers, essential for cost-effective logistics and integral to logistics strategies, serve as central hubs for various activities involving public and private logistics organizations. Turkey's logistics sector has seen significant growth due to rising national and international trade. To meet this demand, numerous logistics villages have been proactively established, with 11 currently operational. Among these, Sakarya province, recognized for its substantial trade volume, is a focal point. This study, employing the powerful Bayesian Best-Worst Method (B-BWM) for multi-criteria decision-making, concentrates on selecting the optimal location for a logistics village in Sakarya. Criteria weights were determined, and alternatives ranked through expert opinions, surveys, and interviews. The Sapanca and Arifiye regions emerged as prime choices. The study emphasizes the importance of logistics infrastructure, with distribution networks and logistics services as vital sub-criteria. The B-BWM method's efficacy in addressing logistics village planning challenges is evident, offering valuable guidance for decision-makers.    

References

‎[1] ‎ Millar, M. (2015). Global supply chain ecosystems: strategies for competitive advantage in a complex, ‎connected world. Kogan Page Publishers.‎

‎[2] ‎ Kaynak, R., Koçoğlu, İ., & Akgün, A. E. (2014). The role of reverse logistics in the concept of logistics ‎centers. Procedia - social and behavioral sciences, 109, 438–442. DOI: 10.1016/j.sbspro.2013.12.487‎

‎[3] ‎ Alkhatib, S. F., Darlington, R., Yang, Z., & Nguyen, T. T. (2015). A novel technique for evaluating and ‎selecting logistics service providers based on the logistics resource view. Expert systems with applications, ‎‎42(20), 6976–6989. DOI: 10.1016/j.eswa.2015.05.010‎

‎[4] ‎ Özmen, M., & Aydoğan, E. K. (2020). Robust multi-criteria decision making methodology for real life ‎logistics center location problem. Artificial intelligence review, 53(1), 725–751. DOI: 10.1007/s10462-019-‎‎09763-y

‎[5] ‎ Hashemkhani Zolfani, S., Görçün, Ö. F., & Küçükönder, H. (2021). Evaluating logistics villages in turkey ‎using hybrid improved fuzzy swara (Imf swara) and fuzzy mabac techniques. Technological and economic ‎development of economy, 27(6), 1582–1612. DOI: 10.3846/tede.2021.16004‎

‎[6] ‎ Higgins, C. D., Ferguson, M., & Kanaroglou, P. S. (2012). Varieties of logistics centers developing ‎standardized typology and hierarchy. Transportation research board of the national academies, 2288, 1–20. ‎http://docs.trb.org/prp/12-3874.pdf‎

‎[7] ‎ Ballis, A., & Mavrotas, G. (2007). Freight village design using the multicriteria method PROMETHEE. ‎Operational research, 7(2), 213–231. DOI: 10.1007/bf02942388‎

‎[8] ‎ Turskis, Z., & Zavadskas, E. K. (2010). A new fuzzy additive ratio assessment method (ARAS-F). Case ‎study: The analysis of fuzzy Multiple criteria in order to select the logistic centers location. Transport, ‎‎25(4), 423–432. DOI: 10.3846/transport.2010.52‎

‎[9] ‎ Erkayman, B., Gundogar, E., Akkaya, G., & Ipek, M. (2011). A fuzzy topsis approach for logistics center ‎location selection. Journal of business case studies (JBCS), 7(3), 49–54. DOI: 10.19030/jbcs.v7i3.4263‎

‎[10] ‎ Li, Y., Liu, X., & Chen, Y. (2011). Selection of logistics center location using axiomatic fuzzy set and ‎TOPSIS methodology in logistics management. Expert systems with applications, 38(6), 7901–7908. DOI: ‎‎10.1016/j.eswa.2010.12.161‎

‎[11] ‎ Chen, K. H., Liao, C. N., & Wu, L. C. (2014). A selection model to logistic centers based on TOPSIS and ‎MCGP methods: The case of airline industry. Journal of applied mathematics, 2014(1), 470128. DOI: ‎‎10.1155/2014/470128‎

‎[12] ‎ Zak, J., & Węgliński, S. (2014). The selection of the logistics center location based on MCDM/A ‎methodology. Transportation research procedia, 3, 555–564. DOI: 10.1016/j.trpro.2014.10.034‎

‎[13] ‎ Elevli, B. (2014). Logistics freight center locations decision by using Fuzzy-PROMETHEE. Transport, ‎‎29(4), 412–418. DOI: 10.3846/16484142.2014.983966‎

‎[14] ‎ Tomić, V., Marinković, D., & Marković, D. (2014). The selection of logistic centers location using multi-‎criteria comparison: case study of the Balkan Peninsula. Acta polytechnica hungarica, 11(10), 97–113. DOI: ‎‎10.12700/aph.11.10.2014.10.6‎

‎[15] ‎ Yildirim, B. F., & Önder, E. (2014). Evaluating potential freight villages in istanbul using multi criteria ‎decision making techniques. Journal of logistics management, 2014(1), 1–10. DOI: ‎‎10.5923/j.logistics.20140301.01‎

‎[16] ‎ Özceylan, E., Erbaş, M., Tolon, M., Kabak, M., & Durʇut, T. (2016). Evaluation of freight villages: A GIS-‎based multi-criteria decision analysis. Computers in industry, 76, 38–52. DOI: ‎‎10.1016/j.compind.2015.12.003‎

‎[17] ‎ Pham, T. Y., Ma, H. M., & Yeo, G. T. (2017). Application of fuzzy Delphi TOPSIS to locate logistics centers ‎in vietnam: The logisticians’ perspective. Asian journal of shipping and logistics, 33(4), 211–219. DOI: ‎‎10.1016/j.ajsl.2017.12.004‎

‎[18] ‎ Uyanik, C., Tuzkaya, G., Kalender, Z. T., & Oguztimur, S. (2020). An integrated dematel–if-topsis ‎methodology for logistics centers’ location selection problem: An application for istanbul metropolitan ‎area. Transport, 35(6), 548–556. DOI: 10.3846/transport.2020.12210‎

‎[19] ‎ Komchornrit, K. (2021). Location selection of logistics center: a case study of greater mekong subregion ‎economic corridors in northeastern thailand. ABAC journal, 41(2), 137–155.‎

‎[20] ‎ Mohammadi, M., & Rezaei, J. (2020). Bayesian best-worst method: A probabilistic group decision ‎making model. Omega (united kingdom), 96, 102075. DOI: 10.1016/j.omega.2019.06.001‎

‎[21] ‎ Yanilmaz, S., Baskak, D., Yucesan, M., & Gul, M. (2021). Extension of FEMA and SMUG models with ‎Bayesian best-worst method for disaster risk reduction. International journal of disaster risk reduction, 66, ‎‎102631. DOI: 10.1016/j.ijdrr.2021.102631‎

‎[22] ‎ Ak, M. F., Yucesan, M., & Gul, M. (2022). Occupational health, safety and environmental risk assessment ‎in textile production industry through a Bayesian BWM-VIKOR approach. Stochastic environmental ‎research and risk assessment, 36(2), 629–642. DOI: 10.1007/s00477-021-02069-y

‎[23] ‎ Gul, M., & Yucesan, M. (2022). Performance evaluation of Turkish universities by an integrated Bayesian ‎BWM-TOPSIS model. Socio-economic planning sciences, 80, 101173. DOI: 10.1016/j.seps.2021.101173‎

‎[24] ‎ Gul, M., Yucesan, M., & Ak, M. F. (2022). Control measure prioritization in Fine − Kinney-based risk ‎assessment: a Bayesian BWM-fuzzy VIKOR combined approach in an oil station. Environmental science ‎and pollution research, 29(39), 59385–59402. DOI: 10.1007/s11356-022-19454-x

‎[25] ‎ Munim, Z. H., Balasubramaniyan, S., Kouhizadeh, M., & Ullah Ibne Hossain, N. (2022). Assessing ‎blockchain technology adoption in the Norwegian oil and gas industry using Bayesian best worst ‎method. Journal of industrial information integration, 28, 100346. DOI: 10.1016/j.jii.2022.100346‎

‎[26] ‎ Tsang, Y. P., Fan, Y., & Feng, Z. P. (2023). Bridging the gap: Building environmental, social and ‎governance capabilities in small and medium logistics companies. Journal of environmental management, ‎‎338, 117758. DOI: 10.1016/j.jenvman.2023.117758‎

‎[27] ‎ Gupta, H., Shreshth, K., Kharub, M., & Kumar, A. (2024). Strategies to overcome challenges to smart ‎sustainable logistics: a Bayesian-based group decision-making approach. Environment, development and ‎sustainability, 26(5), 11743–11770. DOI: 10.1007/s10668-023-03477-6‎

‎[28] ‎ Rimienė, K., & Grundey, D. (2007). Logistics centre concept through evolution and definition. ‎Engineering economics, 4(4), 87–95. https://www.ceeol.com/search/article-detail?id=120728‎

‎[29] ‎ Karadeniz, V., & Akpınar, E. (2011). Logistics village applications in Turkey and a new logistics village ‎proposal. Marmara geography magazine, 23(1303–2429), 49–71. ‎https://dergipark.org.tr/en/download/article-file/3253‎

‎[30] ‎ Sheffi, Y. (2013). Logistics-intensive clusters: Global competitiveness and regional growth. In ‎International series in operations research and management science (pp. 463–500). Springer. DOI: 10.1007/978-‎‎1-4419-6132-7_19‎

‎[31] ‎ Regmi, M. B., & Hanaoka, S. (2013). Location analysis of logistics centres in Laos. International journal of ‎logistics research and applications, 16(3), 227–242. DOI: 10.1080/13675567.2013.812194‎

‎[32] ‎ Kayikci, Y. (2010). A conceptual model for intermodal freight logistics centre location decisions. Procedia ‎‎- social and behavioral sciences, 2(3), 6297–6311. DOI: 10.1016/j.sbspro.2010.04.039‎

‎[33] ‎ Arikan, F. (2012). Freight villages and an application [Thesis]. .‎

‎[34] ‎ Can, A. M. (2012). Selection the location of freight village in samsun with multi-criteria decision ‎making. Yükseklisans tezi. kayseri: erciyes university.‎

‎[35] ‎ ELGÜN, M. N., & Cemal, E. (2011). A model proposal for the selection of logistics village centers in ‎terms of local, national and international transportation and trade. Manisa celal bayar university journal of ‎social sciences, 9(2), 630–645.‎

‎[36] ‎ Eryürük, S. H., Kalaoǧlu, F., & Baskak, M. (2012). A site selection model for establishing a clothing ‎logistics center. Tekstil ve konfeksiyon, 22(1), 40–47.‎

‎ [37] ATEŞ, Ç., & ESEN, S. (2022). Evaluation of Sakarya province in terms of its potential as a logistics base. ‎Sakarya university business institute journal, 4(2), 35–41. DOI: 10.47542/sauied.1175207‎

‎[38] ‎ Rowshan, M., Shojaei, P., Askarifar, K., & Rahimi, H. (2020). Identifying and prioritizing effective ‎factors on outsourcing in public hospitals using fuzzy BWM. Hospital topics, 98(1), 16–25. DOI: ‎‎10.1080/00185868.2019.1711482‎

‎[39] ‎ Amiri, M., Hashemi-Tabatabaei, M., Ghahremanloo, M., Keshavarz-Ghorabaee, M., Zavadskas, E. K., & ‎Antucheviciene, J. (2020). A new fuzzy approach based on BWM and fuzzy preference programming for ‎hospital performance evaluation: A case study. Applied soft computing journal, 92, 106279. DOI: ‎‎10.1016/j.asoc.2020.106279‎

‎[40] ‎ Nabeeh, N. A., Abdel-Monem, A., & Abdelmouty, A. (2019). A novel methodology for assessment of ‎hospital service according to BWM, MABAC, PROMETHEE II. Neutrosophic sets and systems, 31(1), 63–79.‎

‎[41] ‎ Torkayesh, A. E., Pamucar, D., Ecer, F., & Chatterjee, P. (2021). An integrated BWM-LBWA-CoCoSo ‎framework for evaluation of healthcare sectors in Eastern Europe. Socio-economic planning sciences, 78, ‎‎101052. DOI: 10.1016/j.seps.2021.101052‎

‎[42] ‎ Aghaloo, K., Ali, T., Chiu, Y. R., & Sharifi, A. (2023). Optimal site selection for the solar-wind hybrid ‎renewable energy systems in Bangladesh using an integrated GIS-based BWM-fuzzy logic method. ‎Energy conversion and management, 283, 116899. DOI: 10.1016/j.enconman.2023.116899‎

‎[43] ‎ Alshamrani, A., Majumder, P., Das, A., Hezam, I. M., & Božanić, D. (2023). An integrated BWM-TOPSIS-I ‎Approach to Determine the Ranking of Alternatives and Application of Sustainability Analysis of ‎renewable energy. Axioms, 12(2), 1–19. DOI: 10.3390/axioms12020159‎

‎[44] ‎ Foroozesh, F., Monavari, S. M., Salmanmahiny, A., Robati, M., & Rahimi, R. (2022). Assessment of ‎sustainable urban development based on a hybrid decision-making approach: Group fuzzy BWM, AHP, ‎and TOPSIS–GIS. Sustainable cities and society, 76, 103402. DOI: 10.1016/j.scs.2021.103402‎

‎[45] ‎ Li, Q., Rezaei, J., Tavasszy, L., Wiegmans, B., Guo, J., Tang, Y., & Peng, Q. (2020). Customers’ preferences ‎for freight service attributes of China railway express. Transportation research part a: policy and practice, ‎‎142, 225–236. DOI: 10.1016/j.tra.2020.10.019‎

‎[46] ‎ Beysenbaev, R., & Dus, Y. (2020). Proposals for improving the logistics performance index. Asian journal ‎of shipping and logistics, 36(1), 34–42. DOI: 10.1016/j.ajsl.2019.10.001‎

‎[47] ‎ Rezaei, J., van Roekel, W. S., & Tavasszy, L. (2018). Measuring the relative importance of the logistics ‎performance index indicators using best worst method. Transport policy, 68, 158–169. DOI: ‎‎10.1016/j.tranpol.2018.05.007‎

‎[48] ‎ Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57.‎

‎[49] ‎ Forbes, C., Evans, M., Hastings, N., & Peacock, B. (2011). Statistical distributions. John Wiley & Sons.‎

Published

2024-08-20

How to Cite

Koç, S., Erden, C., Ateş, Çağdaş, & Ceviz, E. (2024). Evaluation of Potential Logistics Village Alternatives Using Bayesian Best-Worst Method. Optimality, 1(1), 100-120. https://doi.org/10.22105/opt.v1i1.35