Kemal Yasin GÖKA, Görkem GÜLHAN, Olcay POLAT

SOĞUK HAVA DEPOLARININ TALEP BAZLI YER SEÇİMİ YAKLAŞIMINDA KÜTLE DENGELİ ERİŞEBİLİRLİK MODELİ: İZMİR ÖRNEĞİ

Isıya duyarlı ürünlerin üretimi ve tüketimi arasındaki sürenin uzaması halinde duraklı bir zincir lojistik yapısına ihtiyaç duyulur. Bu yapı genel olarak üretim-taşıma-depolama-taşıma-tüketim şeklinde gerçekleşen zincirde soğuk hava depolarının ara durak düğümlerini üstlendiği sistemlerdir. İki taşıma süreci arasında kalan bir tesisin bulunduğu konum dolayısı ile taşıma maliyetlerini, ürün tazeliğini, ulaşım süresini, erişebilirliği doğrudan etkilemektedir. Soğuk hava depolarının mekansal planlamaları genellikle mikro ölçekte üretim ve tüketim bölgelerinin arasında bulunan ve çeşitli çevresel dinamikler nedeniyle seçilen bir konumdan oluşmaktadır. Ancak bu dinamikler arasında trafik bazlı erişilebilirlik ve talep dengesini sağlayan bütünsel bir çerçevenin eksikliği problemi seyahat süresi ve talep dalgalanmalarının gerçekçiliğinden uzaklaştırabilmektedir. Bu çalışmada gıda taşımacılığında soğuk zincir verimliliğini artırmak amacıyla İzmir Sürdürülebilir Kentsel Lojistik Planı kapsamında gerçekleştirilen trafik ataması verilerinden yararlanılmıştır. Soğuk hava depoları yer seçim kriterlerinde trafik bazlı erişilebilirlik ve talep ölçütleri kullanılarak stratejik yer seçimi yaklaşımı önerilmiştir. Bu yaklaşım ürün ve taşıma türlerinden oluşturulan koridorlarda tampon bölgelerin belirlenmesi ile elde edilmiştir.

Anahtar Kelimeler:

Yer Seçimi, Erişilebilirlik, Soğuk Hava Deposu, Lojistik, İzmir, Kentsel Lojistik Plan

MASS BALANCED ACCESSIBILITY MODEL IN DEMAND-BASED SITE SELECTION APPROACH OF COLD STORAGES: THE EXAMPLE OF IZMIR

If the time between the production and consumption of heat-sensitive products is prolonged, a chain logistics structure with stops is needed. This structure is the system in which the cold storages undertake the intermediate stops in the chain, which generally takes place in the form of production-transport-storage-transport-consumption. The location of a facility between two transportation processes directly affects transportation costs, product freshness, transportation time, and accessibility. Spatial planning of cold storage usually consists of a location between micro-scale production and consumption zones and is chosen due to various environmental dynamics. However, the lack of a holistic framework that balances traffic-based accessibility and demand among these dynamics can distract the problem from the realism of travel time and demand fluctuations. In this study, in order to increase the cold chain efficiency in food transportation, the traffic assignment data carried out within the scope of the Izmir Sustainable Urban Logistics Plan was used. A strategic location selection approach has been proposed by using traffic-based accessibility and demand criteria in cold storage location selection criteria. This approach has been achieved by determining buffer zones in corridors formed from product and transport types.

Keywords:

Site Selection, Accessibility, Cold Storage, Urban Logistic Plan, Izmir.,

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Arkoc, O. (2014). Municipal solid waste landfill site selection using geographical information systems: a case study from Çorlu, Turkey. Arabian Journal of Geosciences, 7(11), 4975-4985.
Cao, W., Yan, M., & Zhang, L. (2017). Cold Chain Logistics Enterprise Performance Evaluation Based on DEA-AHP and Its Improved Method.
Chen, J., Liao, W., & Yu, C. (2021). Route optimization for cold chain logistics of front warehouses based on traffic congestion and carbon emission. Computers & Industrial Engineering, 161, 107663.
Dong, W. (2020). Research on Supply Chain Resilience of Agricultural Products Based on AHP-FCE Model.
Han, Y. (2020). Research on Selection of Fresh Cold Chain Logistics Service Providers Based on AHP-TOPSIS. Int. J. Sci, 7, 39-46.
Hassan, M., Chakma, M., & Hasan, Z. (2020, July). An AHP Approach for Cold Storage Warehouse Site Selection: A Case Study in Bangladesh. In 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1-6). IEEE.
Hiremath, D. B., Patil, N. R., & Dasgupta, A. (2013). Geospatial technique for potato cold storage allocation. Journal of Geomatics, 7(1), 13-17.
Jia, Z. Y., & Yang, X. X. (2012). Application of entropy weight method and TOPSIS model in the cold-chain logistics and distribution center location. In Advanced Materials Research (Vol. 569, pp. 693-696). Trans Tech Publications Ltd.
Jian, S., & Jing, Z. (2015). Evaluation of core competence of cold-chain logistics enterprises based on FCE model. The Open Cybernetics & Systemics Journal, 9(1).
Jiao, X., Xu, W., & Duan, L. (2021). Study on Cold Chain Transportation Model of Fruit and Vegetable Fresh-Keeping in Low-Temperature Cold Storage Environment. Discrete Dynamics in Nature and Society, 2021.
Joshi, R., Banwet, D. K., & Shankar, R. (2011). A Delphi-AHP-TOPSIS based benchmarking framework for performance improvement of a cold chain. Expert Systems with Applications, 38(8), 10170-10182.
Hiremath, D. B., Patil, N. R., & Dasgupta, A. (2013). Geospatial technique for potato cold storage allocation. Journal of Geomatics, 7(1), 13-17.
İzmir Büyükşehir Belediyesi, 2019. İzmir Sürdürülebilir Kentsel Lojistik Ana Planı. İzmir.
Kamali, M., Alesheikh, A. A., Khodaparast, Z., Hosseinniakani, S. M., & Borazjani, S. A. (2015). Application of delphi-AHP and fuzzy-GIS approaches for site selection of large extractive industrial units in Iran. Journal of settlements and Spatial planning, 6(1), 9-17.
Liu, H., & Fan, L. (2018, September). Location Selection of Cold Chain Logistics Park Based on “HF-A” Model. In IOP Conference Series: Earth and Environmental Science (Vol. 186, No. 6, p. 012047). IOP Publishing.
Mingfei, L., & Ting, Y. (2011, July). The cold chain logistics performance evaluation on sideline products based on data envelopment analysis. In 2011 International Conference on Product Innovation Management (ICPIM 2011) (pp. 371-374). IEEE.
Singh, R. K., Chaudhary, N., & Saxena, N. (2018). Selection of warehouse location for a global supply chain: A case study. IIMB management review, 30(4), 343-356.
Şener, B., Süzen, M. L., & Doyuran, V. (2006). Landfill site selection by using geographic information systems. Environmental geology, 49(3), 376-388.
Upadhyay, G., & Bhattacharya, B. K. (2019). Site Suitability for Developing New Cold Chain Using Multi-Criteria Decision Analysis and Geospatial Techniques. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 325-331.
Uyan, M. (2013). GIS-based solar farms site selection using analytic hierarchy process (AHP) in Karapinar region, Konya/Turkey. Renewable and Sustainable Energy Reviews, 28, 11-17.
Viswanadham, N. (2006). Can India be the food basket for the world. Achieving rural and global supply chain excellence: the Indian way. Hyderabad: GLAMS, 1, 1-16.
Wang, W., Dong, W., Wang, H., Wu, H., & Zhang, W. (2015, November). Research on Assessment of Farm Product Cold Chain Quality and Safety Based on Intuitionistic Triangular Fuzzy TOPSIS. In 3rd International Conference on Management Science, Education Technology, Arts, Social Science and Economics (pp. 1247-1251). Atlantis Press.
Yuanguo, Y., & Shengyu, H. (2019). Research on optimization of distribution route for cold chain logistics of agricultural products based on traffic big data. Management Review, 31(4), 240.
Yubin Liu and Zongwei Ren , "Study on site selection of cold chain logistics in northwest territories", AIP Conference Proceedings 1864, 020157 (2017) https://doi.org/10.1063/1.4992974
Young, M. (1997). The cold storage chain. In: Dellino, C.V.J. (eds) Cold and Chilled Storage Technology. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1127-0_1
Zhang, S., Chen, N., She, N., & Li, K. (2021). Location optimization of a competitive distribution center for urban cold chain logistics in terms of low-carbon emissions. Computers & Industrial Engineering, 154, 107120.
Zhao, Z., Li, X., & Zhou, X. (2020). Distribution route optimization for electric vehicles in urban cold chain logistics for fresh products under time-varying traffic conditions. Mathematical Problems in Engineering, 2020.