SELF ORGANISING MAPS FOR BIOFUEL SUPPLY CHAIN NETWORK DESIGN STUDIES

Yıl 2020, , 345 - 356, 25.06.2020

Fatih Öztürk , Ali Osman Kuşakcı , Berk Ayvaz , Melike Sırakaya Karakoç

https://doi.org/10.21923/jesd.509079

Cited By: 2

Öz

Interest in environmentally friendly, renewable energy sources has been increasing. Energy Market Regulatory Authority (EPDK) has been obliged to add biodiesel and ethanol as domestic additives to petrol. This makes it necessary to make efficient and optimal green supply chain network design studies for renewable energy sources. In this context, decision makers have focused on renewable energies to produce sustainable solutions that provide economic development, environmental sensitivity and social welfare. In this study, previous studies on biofuel supply chain network designs were analyzed in terms of their work, objectives, decision variables, constraints, optimization method used and results. the studies were clustered using a special type of artificial neural networks Self Organizing Maps method and the densities of the features were shown in the study. This article, which includes a review of the literature and a cluster of studies, is a guide for researchers working on biofuel supply chain network design.

Anahtar Kelimeler

Supply Chain Network Design, Biofuels, Renewable Energy, Decision Making

BİYOYAKIT TEDARİK ZİNCİRİ AĞ TASARIMI ÇALIŞMALARI İÇİN ÖZ DÜZENLEYİCİ HARİTALAR

Öz

Son yıllarda çevre dostu yenilenebilir enerji kaynaklarına ilgi artmaktadır. Ülkemizde Enerji Piyasası Düzenleme Kurumu (EPDK) tarafından, 2013 yılından itibaren benzine ve motorine yerli katkı olarak, oranları her yıl arttırılmak üzere biyodizel ve etanol ilave zorunluluğu getirilmiştir. Bu da yenilenebilir enerji kaynakları için etkin ve optimal yeşil tedarik zinciri ağı tasarımı çalışmalarının yapılmasını gerekli kılmaktadır. Bu bağlamda karar vericiler, ekonomik gelişme sağlayan, çevre hassasiyeti olan ve sosyal refahı sağlayan sürdürülebilir çözümler üretmek için biyoenerji, rüzgâr, güneş, dalga, gelgit vs. gibi yenilenebilir enerjilere odaklanmışlardır Bu çalışmada biyoyakıtların tedarik zinciri ağ tasarımları ile ilgili yapılmış geçmiş çalışmalara yer verilmiş ve çalışmalar; amaçları, karar değişkenleri, kısıtları, kullanılan optimizasyon metodu ve sonuçları açısından analiz edilerek değerlendirilmiştir. İncelenen çalışmalar, yapay sinir ağlarının özel bir çeşidi olan öz düzenleyici haritalar (Self Organizing Maps-SOM) yöntemi kullanılarak kümelenmiş ve literatürdeki boşluklar tartışılmıştır. Literatürün incelemesi ve çalışmaların kümelenmesini içeren bu makale, biyoyakıt tedarik zinciri ağ tasarımı ile ilgili çalışma yapacak araştırmacılar için yol gösterici niteliktedir. 

Anahtar Kelimeler

Tedarik Zinciri Ağ Tasarımı, Biyoyakıt, Yenilenebilir Enerji, Karar Verme

Kaynakça

• Andersen, F., Iturmendi, F., Espinosa, S., Diaz, M.S., 2012. Optimal Design and Planning of Biodiesel Supply Chain with Land Competition. Computers & Chemical Engineering, 47, 170-182. DOI: 10.1016/j.compchemeng.2012.06.044.
• Avami, A., 2012. A Model for Biodiesel Supply Chain: A Case Study in Iran. Renewable and Sustainable Energy Reviews, 16(6), 4196-4203. DOI: 10.1016/j.rser.2012.03.023.
• Ayvaz, B., Kuşakçı, A. O., Öztürk, F. Sırakaya, M., (2018a). Biyodizel Tedarik Zinciri Ağ Tasarımı İçin Çok Amaçlı Karma Tam Sayılı Doğrusal Programlama Modeli Önerisi. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 23(4), 55-70.
• Ayvaz, B., Kuşakçı, A. O., Öztürk, F., Karakoç, E., (2018b). Biyodizel Yakıtlar İçin Çok Dönemli Tedarik Zinciri Ağ Tasarımı: Türkiye Örneği. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 6 (4), 862-876. DOI: 10.29109/gujsc.411873
• Azadeh, A., Arani, H. V., 2016. Biodiesel Supply Chain Optimization Via a Hybrid System Dynamics-Mathematical Programming Approach. Renewable Energy, 93, 383-403. DOI: 10.1016/j.renene.2016.02.070
• Azadeh, A., Arani, H. V., Dashti, H., 2014. A Stochastic Programming Approach Towards Optimization of Biofuel Supply Chain. Energy, 76, 513-525. DOI: 10.1016/j.energy.2014.08.048.
• Azar, A. T., El-Said, S. A., Hassanien, A. E., 2013. Fuzzy and Hard Clustering Analysis for Thyroid Disease. Computer Methods and Programs in Biomedicine. 111(1), 1–16, DOI: 10.1016/j.cmpb.2013.01.002.
• Babazadeh, R., 2017. Optimal Design and Planning of Biodiesel Supply Chain Considering Nonedible Feedstock. Renewable and Sustainable Energy Reviews, 75, 1089-1100. DOI: 10.1016/j.rser.2016.11.088.
• Babazadeh, R., Razmi, J., Pishvaee, M.S., Rabbani, M., 2017. A Sustainable Second Generation Biodiesel Supply Chain Network Design Problem Under Risk. Omega, 66(B), 258-277, DOI: 10.1016/j.omega.2015.12.010.
• Babazadeh, R., Razmi, J., Rabbani, M., Pishvaee, M. S., 2015. An Integrated Data Envelopment Analysis Mathematical Programming Approach to Strategic Biodiesel Supply Chain Network Design Problem. Journal of Cleaner Production, 147, 694-707. DOI: 10.1016/j.jclepro.2015.09.038.
• Bai, Y., Ouyang, Y., ShiPang, J., 2016. Enhanced Models and Improved Solution for Competitive Biofuel Supply Chain Design Under Land Use Constraints. European Journal of Operational Research, 249(1), 281-297. DOI: 10.1016/j.ejor.2015.08.027.
• Cáceres, R. G. G., Avella, M. E.M., Gómez, F. P., 2015. Tactical Optimization of the Oil Palm Agribusiness Supply Chain. Applied Mathematical Modelling, 39(20), 6375-6395. DOI:10.1016/j.apm.2015.01.031.
• Cavazos, T., 2000. Using Self-Organizing Maps to Investigate Extreme Climate Events: An Application to Wintertime Precipitation in the Balkans. Journal of Climate, 13(10), 1718-1732. DOI: 10.1175/1520-0442(2000)013<1718:USOMTI>2.0.CO;2.
• Duarte, A., Sarache, W., Costa, Y., 2016. Biofuel Supply Chain Design From Coffee Cut Stem Under Environmental Analysis. Energy, 100(C), 321-331. DOI: 10.1016/j.energy.2016.01.076.
• Ennis, D., Medaille, A., Lambert, T., Kelley, R., Harris, F. C., 2013. A Comparison of Academic Libraries: An Analysis Using a Self‐Organizing Map, Performance Measurement and Metrics, Vol. 14(2), 118-131. DOI: 10.1108/PMM-07-2012-0026.
• Ercan, S., Kayakutlu, G. 2015. Scheduling in Energy Systems. Sigma Journal of Engineering and Natural Sciences-Sigma Mühendislik ve Fen Bilimleri Dergisi, 33, 679-690.
• Gonela, V., Zhang, J., Osman, A., Onyeaghala, R., 2015. Stochastic Optimization of Sustainable Hybrid Generation Bioethanol Supply Chains. Transportation Research Part E: Logistics and Transportation Review, 77, 1 -28. DOI: 10.1016/j.tre.2015.02.008.
• Haykin, S. S. 2009. Neural networks and learning machines. Upper Saddle River, NJ: Pearson Education, New Jersey.
• Hombach, L. E., Cambero, C., Sowlati, T., Walther, G., 2016. Optimal Design of Supply Chains for Second Generation Biofuels Incorporating European Biofuel Regulations. Journal of Cleaner Production, 133(1), 565-575. DOI: 10.1016/j.jclepro.2016.05.107.
• Höppner, F., 2002. Speeding Up Fuzzy C-Means: Using A Hierarchical Data Organisation to Control the Precision of Membership Calculation. Fuzzy Sets and Systems, 128(3), 365–376. DOI: 10.1016/S0165-0114(01)00204-4.
• Ivanov, B., Stoyanov, S., 2016. A mathematical model formulation for the design of an integrated biodiesel-petroleum diesel blends system. Energy, 99, 221-236. DOI: 10.1016/j.energy.2016.01.038.
• Jiang, Y., Zhang, Y., 2016. Supply Chain Optimization of Biodiesel Produced from Waste Cooking Oil. Transportation Research Procedia, 12, 938-949. DOI: 10.1016/j.trpro.2016.02.045.
• Kalteh, A.M., Hjorth, P., Berndtsson, R., 2008. Review of The Self-Organizing Map (SOM) Approach in Water Resources: Analysis, Modelling and Application. Environmental Modelling & Software, 23(7), 835-845. DOI: 10.1016/j.envsoft.2007.10.001.
• Kaufman, L., Rousseeuw, P. J., 1990. Finding Groups in Data: An Introduction to Cluster Analysis. Wiley Publication. New York.
• Kohonen, T., 1982. Self-Organized Formation of Topologically Correct Feature Maps. Biological Cybernetics, 43(1), 59–69. DOI: doi.org/10.1007/BF00337288.
• Kuşakçı, A., Ayvaz, B., Öztürk, F., Sofu, F. (2019) Bulanık MULTIMOORA ile Personel Seçimi: Havacılık Sektöründe Bir Uygulama. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 8 (1), 96-110. DOI: 10.28948/ngumuh.516835
• Länsiluoto, A., Eklund, T., Back, B., Vanharanta, H., Visa, A., 2004. Industry‐Specific Cycles and Companies' Financial Performance Comparison Using Self‐Organizing Maps, Benchmarking: An International Journal, 11(3), 267-286. DOI: 10.1007/BF00337288.
• Leão, R.R.C.C., Hamacher, S., Oliveira, F., 2011. Optimization of Biodiesel Supply Chains Based On Small Farmers: A Case Study in Brazil. Bioresour Technol, 102(19), 8958‐8963. DOI:10.1016/j.biortech.2011.07.002.
• Li, Q., Hu, G., 2014. Supply Chain Design Under Uncertainty for Advanced Biofuel Production Based On Bio-Oil Gasification. Energy, 74(1), 576-584. DOI: 10.1016/j.energy.2014.07.023.
• Liu, Z., Qiu, T., Chen, B., 2014. A study of the LCA based biofuel supply chain multi-objective optimization model with multi-conversion paths in China. Applied Energy, 126(C), 221-234 DOI:10.1016/j.apenergy.2014.04.001.
• MacQueen, J., 1967. Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281-297.
• Marufuzzaman, M., Eksioglu, S. D., Huang, Y. E., 2014. Two-Stage Stochastic Programming Supply Chain Model for Biodiesel Production Via Wastewater Treatment. Computers & Operations Research, 49, 1-17. DOI: 10.1016/j.cor.2014.03.010.
• Meyer, A., Cattrysse, D., Orshoven, J.V., 2015. A Generic Mathematical Model to Optimise Strategic and Tactical Decisions in Biomass-Based Supply Chains(OPTIMASS). European Journal of Operational Research, 245(1), 247-264. DOI: 10.1016/j.ejor.2015.02.045.
• Mohseni, S., Pishvaee, M. S., 2016. A Robust Programming Approach Towards Design and Optimization of Microalgae-Based Biofuel Supply Chain. Computers & Industrial Engineering, 100, 58-71. DOI: 10.1016/j.cie.2016.08.003.
• Mohseni, S., Pishvaee, M.S., Sahebi, H., 2016. Robust Design and Planning of Microalgae Biomass-To-Biodiesel Supply Chain: A Case Study in Iran. Energy, 111(C), 736-755. DOI: 10.1016/j.energy.2016.06.025.
• Moncada, J.A., Lukszo, Z., Junginger, M., Faaij, A., Weijnen, M., 2017. A Conceptual Framework for The Analysis of The Effect Of Institutions On Biofuel Supply Chains. Applied Energy, 185(1), 895-915. DOI: 10.1016/j.apenergy.2016.10.070.
• Mostafa, M. M., 2009. Shades of green: A Psychographic Segmentation of the Green Consumer in Kuwait Using Self-Organizing Maps. Expert Systems with Applications, 36(8), 11030-11038. DOI: 10.1016/j.eswa.2009.02.088.
• Najmi, A., Shakouri, G. H., Nazari, S., 2016. An Integrated Supply Chain: A Large Scale Complementarity Model for the Biofuel Markets. Biomass and Bioenergy, 86, 88-104. DOI: 10.1016/j.biombioe.2016.01.010.
• Oğuzlar A., 2005. Kümeleme Analizinde Yeni Bir Yaklaşım: Kendini Düzenleyen Haritalar (Kohonen Ağları). Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 19 (2), 93-107.
• Özçalıcı, M., 2016. Clustering Stocks with Self-Organizing Maps: An Application On Stocks Listed in BIST50 Index. Istanbul University Journal of the School of Business, 45(1), 22–33.
• Ozturk, F., Kaya, G. K. (2020). Personnel selection with fuzzy VIKOR: an application in automotive supply industry. Gazi University Science Journal: Part C Design and Technology, 8(1), ss. 94–108. DOI: 10.29109/gujsc.595288
• Pasandideh, S.H. R., Niaki, S. T. A., Asadi, K., 2015. Bi-Objective Optimization of a Multi-Product Multi-Period Three-Echelon Supply Chain Problem Under Uncertain Environments: NSGA-II and NRGA. Information Sciences, 292, 57-74. DOI: 10.1016/j.ins.2014.08.068.
• Ren, J., Dong, L., Sun, L., Goodsite, M. E., Tan, S., Dong, L., 2015. Life Cycle Cost Optimization of Biofuel Supply Chains Under Uncertainties Based on Interval Linear Programming. Bioresource Technology, 187, 6-13. DOI: 10.1016/j.biortech.2015.03.083.
• Rincón, L. E., Valencia, M. J., Hernández, V., Matallana, L. G, Cardona, C. A., 2015. Optimization of The Colombian Biodiesel Supply Chain from Oil Palm Crop Based On Techno-Economical and Environmental Criteria. Energy Economics, 47, 154-167. DOI: 10.1016/j.eneco.2014.10.018.
• Schwardt, M., Dethloff, J., 2005. Solving A Continuous Location-Routing Problem by Use of a Self-Organizing Map. Int J. Physical Distribution & Logistics Management 35(6), 390–408. DOI: 10.1108/09600030510611639.
• Yılmaz Balaman Ş., Selim, H., (2016). Sustainable Design of Renewable Energy Supply Chains Integrated with District Heating Systems: A Fuzzy Optimization Approach. Journal of Cleaner Production, 133, 863-885. DOI: 10.1016/j.jclepro.2016.06.001
• Zhang, F., Johnson, D., Johnson, M., Watkins, D., Froese, R., Wang J., 2016. Decision support system integrating GIS with simulation and optimisation for a biofuel supply chain. Renewable Energy, 85, 740-748. DOI: 10.1016/j.renene.2015.07.041.