A Fuzzy Logic Based Green Performance Evaluation Model for Automotive Industry

Yıl 2018, Sayı: 13, 39 - 44, 31.08.2018

Murat Çolak , Ali İhsan Boyacı

https://doi.org/10.31590/ejosat.391828

Cited By: 3

Öz

Nowadays, increasing manufacturing activities cause to critical environmental problems such as global warming and air pollution. These environmental problems have provided increase of environmental awareness in the production process and therefore the green manufacturing (GM) concept has emerged. In general, this concept refers to a production process, which has high efficiency and minimum environmental damage in terms of resources and products. GM has recently become important in almost every sector. Automotive industry has significant importance in terms of economy and employment when considered its sub-industry and other related sectors. Therefore, it is important to evaluate the adoption level of green manufacturing concept in this sector. Fuzzy multi criteria decision making (FMCDM) methods which handle uncertainty in decision making problems can be effectively used for green performance evaluation of companies. In this study, it is aimed to assess green performance of manufacturers which operate in automotive sector by using a MCDM model based on fuzzy analytic hierarchy process (FAHP) method. This model consists of 5 main criteria which are green design, green energy, green material, green logistics and green management, and 19 sub criteria located under these main criteria. As a result of the study, green energy and low waste criteria were determined as the most important main and sub criteria with weights of 0.268 and 0.1026 respectively. The proposed model can be used as an effective tool for companies operating in the automotive industry to measure and to follow their green performance and to select their suppliers. 

Anahtar Kelimeler

Green manufacturing, Fuzzy AHP, Fuzzy MCDM, Automotive industry

Otomotiv Endüstrisi için Bulanık Tabanlı Bir Yeşil Performans Değerlendirme Modeli

Öz

Son günlerde artan üretim faaliyetleri küresel ısınma ve hava kirliliği gibi kritik çevresel problemlere neden olmaktadır. Bu çevresel problemler üretim süreçlerinde çevre bilincinin artmasını sağlamış ve böylece yeşil üretim (YÜ) konsepti ortaya çıkmıştır. Genel olarak bu konsept, kaynaklar ve ürünler açısından yüksek verimlilikli ve çevreye olabildiğince az zarar veren üretim süreçlerini ifade etmektedir. YÜ son zamanlarda neredeyse bütün sektörlerde önem kazanmıştır. Otomotiv endüstrisi, alt endüstrileri ve diğer ilişkili olduğu endüstriler düşünüldüğünde ekonomi ve istihdam açısından büyük bir öneme sahiptir. Böylelikle bu sektörde YÜ konseptinin benimsenme seviyesini değerlendirmek oldukça önemlidir. Karar verme problemlerindeki belirsizliği de göz önünde bulunduran bulanık çok kriterli karar verme (ÇKKV) metotları firmaların yeşil performansını değerlendirmek amacıyla etkili bir şekilde kullanılabilir. Bu çalışmada, bulanık analitik hiyerarşi prosesi (AHP) metoduna dayalı bir ÇKKV modeli ile otomotiv sektöründe faaliyet gösteren üreticilerin yeşil performanslarını değerlendirmek hedeflenmiştir. Bu model, yeşil tasarım, yeşil enerji, yeşil materyal, yeşil lojistik ve yeşil yönetim olmak üzere 5 ana kriter ve bu kriterlere ait 19 alt kriter içermektedir. Çalışma sonucunda sırasıyla 0,268 ve 0,1026 ağırlıkları ile yeşil enerji ve düşük atık kriterleri en önemli ana ve alt kriterler olarak belirlenmiştir. Önerilen model otomotiv endüstrisinde faaliyet gösteren firmaların yeşil performanslarını ölçmek, takip etmek ve tedarikçilerini seçmek için etkili bir araç olarak kullanılabilir.

Anahtar Kelimeler

Yeşil üretim, Bulanık AHP, Bulanık çok kriterli karar verme, Otomotiv endüstrisi

Kaynakça

• Anderson, T.R., Hawkins, E., Jones, P.D. 2016. CO2, the greenhouse effect and global warming: from the pioneering work of Arrhenius and Callendar to today’s Earth System Models. Endeavour 40(3), 178-187.
• Banaeian, N., Mobli, H., Nielsen, I.E., Omid, M. 2015. Criteria definition and approaches in green supplier selection – a case study for rew material and packaging of food industry. Production & Manufacturing Research 3(1), 149-168.
• Bhattacharya, A., Dey, P.K., Ho, W. 2015. Green manufacturing supply chain design and operations decision support. International Journal of Production Research 53(21), 6339- 6343.
• Buyukozkan, G. 2012. An integrated fuzzy multi-criteria group decision-making approach for green supplier evaluation. International Journal of Production Research 50(11), 2892- 2909.
• Buyukozkan, G., Cifci, G. 2012. A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers. Expert Systems with Applications 39(3), 3000-3011.
• Chithambaranathan, P., Subramanian, N., Gunasekaran, A., Palaniappan, P.L.K. 2015. Service supply chain environmental performance evaluation using grey based hybrid MCDM approach. Int. J. Production Economics 166(1), 163-176.
• Çifçi, G., Büyüközkan, G. 2011. A Fuzzy MCDM Approach to Evaluate Green Suppliers. International Journal of Computational Intelligence Systems 4(5), 894-909.
• Dweiri, F., Kumar, S., Khan, S.A., Jain, V. 2016. Designing an integrated AHP based decision support system for supplier selection in automotive industry. Expert Systems with Applications 62(1), 273-283.
• Erdem, M.B. 2016. A Fuzzy Analytical Hierarchy Process Application in Personnel Selection in IT Companies: A Case Study in a Spin-off Company. Acta Physica Polonica A 130(1), 331-334.
• Esen, H., Hatipoğlu, T., Boyacı, A.İ., 2016. A Fuzzy Approach for Performance Appraisal: The Evaluation of a Purchasing Specialist, Computational Intelligence, Studies in Computational Intelligence 620, Springer International Publishing, 237s, Switzerland.
• Ghorabaee, M.K., Zavadskas, E.K., Amiri, M., Esmaeili, A. 2016. Multi-criteria evaluation of green suppliers using an extended WASPAS method with interval type-2 fuzzy sets. Journal of Cleaner Production 137(1), 213-229.
• Govindan, K., Diabat, A., Shankar, K.M. 2015. Analyzing the drivers of green manufacturing with fuzzy approach. Journal of Cleaner Production 96(1), 182-193.
• Govindan, K., Kannan, D., Shankar, M. 2015. Evaluation of green manufacturing practices using a hybrid MCDM model combining DANP with PROMETHEE. International Journal of Production Research 53(21), 6344-6371.
• Govindan, K., Khodaverdi, R., Vafadarnikjoo, A. 2015. Intuitionistic fuzzy based DEMATEL method for developing green practices and performances in a green supply chain. Expert Systems with Applications 42(20), 7207-7220.
• Heo, E., Kim, J., Cho, S. 2012. Selecting hydrogen production methods using fuzzy analytic hierarchy process with opportunities, costs, and risks. International Journal of Hydrogen Energy 37(23), 17655–17662.
• Hussain, M., Malik, R.N., Taylor, A. 2017. Carbon footprint as an environmental sustainability indicator for the particleboard produced in Pakistan. Environmental Research 155(1), 385– 393.
• Ilgin, M.A., Gupta, S.M., Battaia, O. 2015. Use of MCDM techniques in environmentally conscious manufacturing and product recovery: State of the art. Journal of Manufacturing Systems 37(3), 746-758.
• Jayawickrama, H.M.M.M., Kulatunga, A.K., Mathavan, S. 2017. Fuzzy AHP based Plant Sustainability Evaluation Method. Procedia Manufacturing 8, 571-578.
• Kamacı, Z., Uysal, G. 2017. Pollution Determined by Using Magnetic Susceptibility Measurements: A Case Study from İzmir-Aliağa. Acta Physica Polonica A 132(3), 487-489.
• Karatas, M. 2017. Multiattribute Decision Making Using Multiperiod Probabilistic Weighted Fuzzy Axiomatic Design. Systems Engineering 20(4), 318-334.
• Karatas, M., Sulukan, E., Karacan, I. 2018. Assessment of Turkey’s energy management performance via a hybrid multi-criteria decision-making methodology. Energy 153, 890-912.
• Kumar, A., Jain, V., Kumar, S., Chandra, C. 2016. Green supplier selection: a new genetic/immune strategy with industrial application. Enterprise Information Systems 10(8), 911-943.
• Kusumawardani, R.P., Agintiara, M. 2015. Application of Fuzzy AHP-TOPSIS Method for Decision Making in Human Resource Manager Selection Process. Procedia Computer Science 72(1), 638–646.
• Li, W., Yu, S., Pei, H., Zhao, C., Tian, B. 2017. A hybrid approach based on fuzzy AHP and 2-tuple fuzzy linguistic method for evaluation in-flight service quality. Journal of Air Transport Management 60, 49-64.
• Liao, C.N., Fu, Y.K., Wu, L.C. 2016. Integrated FAHP, ARAS-F and MSGP Methods for Green Supplier Evaluation and Selection. Technological and Economic Development of Economy 22(5), 651-669.
• Luthra, S., Govindan, K., Kannan, D., Mangla, S.K., Garg, C.P. 2017. An integrated framework for sustainable supplier selection and evaluation in supply chains. Journal of Cleaner Production 140(3), 1686-1698.
• Mittal, V.K., Sangwan, K.S. 2014. Prioritizing Drivers for Green Manufacturing: Environmental, Social and Economic Perspectives. Procedia CIRP 15(1), 135-140.
• Otay, I., Oztaysi, B., Cevik Onar, S., Kahraman, C. 2017. Multiexpert performance evaluation of healthcare institutions using an integrated intuitionistic fuzzy AHP&DEA methodology. Knowledge-Based Systems 133, 90-106.
• Rostemzadeh, R., Govindan, K. 2015. A. Esmaeili, M. Sabaghi, Application of fuzzy VIKOR for evaluation of green supply chain management practices. Ecological Indicators 49(1), 188-203.
• Saaty, T.L. 1977. A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology 15(3), 234- 281.
• Saaty, T.L. 2001. Decision Making with Dependence and Feedback: The Analytic Network Process: The Organization and Prioritization of Complexity. Rws Publications, 370 pages.
• Saaty, T.L. 2008. Decision making with the analytic hierarchy process. International Journal of Services Sciences 1(1), 83- 98.
• Sahin, B., Yip, T.L. 2017. Shipping technology selection for dynamic capability based on improved Gaussian fuzzy AHP model. Ocean Engineering 136, 233-242.
• Salem, A.H., Deif, A.M. 2017. Developing a Greenometer for green manufacturing assessment. Journal of Cleaner Production 154(1), 413-423.
• Toklu, M.C. 2017. Determination of Customer Loyalty Levels by Using Fuzzy MCDM Approaches. Acta Physica Polonica A 132(3), 650-654.
• Tseng, M.L. 2011. Green supply chain management with linguistic preferences and incomplete information. Applied Soft Computing 11(8), 4894-4903.
• Tseng, M.L., Chiu, A.S.F. 2013. Evaluating firm’s green supply chain management in linguistic preferences. Journal of Cleaner Production 40(1), 22-31.
• Ugur, L.O., Baykan, U. 2017. A Model Proposal for Wall Material Selection Decisions by Using Analytic Hierarchy Process (AHP). Acta Physica Polonica A 132(3), 577-579.
• Uygun, O., Dede, A. 2016. Performance evaluation of green supply chain management using integrated fuzzy multicriteria decision-making techniques. Computers & Industrial Engineering 102(1), 502-511.
• Wang, Y.M., Chin, K.S. 2011. Fuzzy analytic hierarchy process: A logarithmic fuzzy preference programming methodology. International Journal of Approximate Reasoning 52(4), 541– 553.
• Yazdani, M., Zolfani, S.H., Zavadskas, E.K. 2016., E.K. New Integration of MCDM methods and QFD in the selection of green suppliers. Journal of Business Economics and Management 17(6), 1097-1113.
• Yuen, KKF. 2014. The Least Penalty Optimization Prioritization Operators for the Analytic Hierarchy Process: A Revised Case of Medical Decision Problem of Organ Transplantation. Systems Engineering 17, 442-461.
• Zhang, N., Zhou, K., Du, X. 2017. Application of fuzzy logic and fuzzy AHP to mineral prospectivity mapping of porphyry and hydrothermal vein copper deposits in the DananhuTousuquan island arc, Xinjiang, NW China. Journal of African Earth Sciences 128, 84-96.