Ambalaj Atığı Toplama-Ayırma Tesisi Etkinliklerinin Hibrid bir Model Önerisi ile İncelenmesi

Öz Ambalaj atığının çevre üzerindeki olumsuz etkisi ve bakir kaynakların kullanımından kaynaklı ekonomik kayıplar, geri dönüşüm sürecini zorunlu hale getirmiştir. Geri dönüşüm sürecindeki en önemli aşama olan ambalaj atığının toplanması ve kategorilerine göre ayrıştırılması işlemlerinin başarısı, bu işlemlerin etkin bir şekilde yönetilmesine bağlıdır. Türkiye’de ambalaj atıklarının toplanması ve kategorilerine göre ayrıştırılması işlemini Toplama-Ayırma Tesisleri (TAT) üstlenmektedir. Bu çalışmada, Çok Amaçlı Karar Verme (ÇAKV) yöntemlerinden Çok Amaçlı Doğrusal Programlama (ÇADP) ile modellenen Çok Kriterli Veri Zarflama Analizi (ÇKVZA) Global Kriter Yöntem kullanılarak çözülmüştür. Önerilen yeni yöntem Global-Çok Kriterli Veri Zarflama Analizi (G-ÇKVZA) olarak isimlendirilmiştir. Çalışma sonucunda İç Anadolu Bölgesinde faaliyetlerini sürdüren 67 tesisten 6’sı etkin tesisler olarak bulunmuştur.

___

  • Adler, N., Yazhemsky, E. (2010). Improving discrimination in data envelopment analysis: PCA–DEA or variable reduction. European Journal of Operational Research, 202(1), 273- 284. (https://doi.org/10.1016/j.ejor.2009.03.050)
  • Alizadeh, M. H., Rasouli, S. M., Tavakkoli-Moghaddam, R. (2011). The use of multi-criteria data envelopment analysis (MCDEA) for location–allocation problems in a fuzzy environment. Expert Systems with Applications, 38(5), 5687-5695. (https://doi.org/10.1016/j.eswa.2010.10.065)
  • Arora, J. S. (2012). Introduction to optimum design. California : Elsevier Academic.
  • Bal, H., Örkcü, H. H., Çelebioğlu, S. (2010). Improving the discrimination power and weights dispersion in the data envelopment analysis. Computers & Operations Research, 37(1), 99- 107. (https://doi.org/10.1016/j.cor.2009.03.028)
  • Bosch, N., Pedraja, F., Suárez‐Pandiello, J. (2000). Measuring the efficiency of Spanish municipal refuse collection services. Local Government Studies, 26(3), 71-90. (https://doi.org/10.1080/03003930008434000)
  • Chang, D. S., Liu, W., Yeh, L. T. (2013). Incorporating the learning effect into data envelopment analysis to measure MSW recycling performance. European Journal of Operational Research, 229(2), 496-504. (https://doi.org/10.1016/j.ejor.2013.01.026)
  • Charnes, A., Cooper, W. W. Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(1978), 429-444. (https://doi.org/10.1016/0377-2217(78)90138-8)
  • Chen, C. C. (2010). A performance evaluation of MSW management practice in Taiwan. Resources, Conservation and Recycling, 54(12), 1353-1361. (https://doi.org/10.1016/j.resconrec.2010.05.003)
  • Chiou, H. K., Chu, Y. F., Tzeng, G. H. (2005). Comparing AHP/GRA with DEA to evaluate the performance of municipal waste recycling in Taiwan. International Symposium on the Analytic Hierarchy Process, Honolulu, Havai, USA, 1-9. (URL: http://isahp.org/2005Proceedings/Papers/ChiouHK_Chu_Tzeng_ComparingAHPGRA_ to_DEA_MunicipalWaste.pdf)
  • Christopoulos, A. G., Dokas, I. G., Katsimardou, S., Vlachogiannatos, K. (2016). Investigation of the relative efficiency for the Greek listed firms of the construction sector based on two DEA approaches for the period 2006–2012. Operational Research, 16(3), 423-444. (https://doi.org/10.1007/s12351-015-0207-8)
  • Cooper, W. W., Seiford, L. M. Tone, K. (2002). Data envelopment analysis: A comprehensive text with models, applications, references and DEA-solver software. Dordrecht: Kluwer Academic Publishers.
  • De Jaeger, S., Rogge, N. (2014). Cost-efficiency in packaging waste management: The case of Belgium. Resources, Conservation and Recycling, 85(2014), 106-115. (https://doi.org/10.1016/j.resconrec.2013.08.006)
  • Expósito, A., Velasco, F. (2018). Municipal solid-waste recycling market and the European 2020 Horizon Strategy: A regional efficiency analysis in Spain. Journal of Cleaner Production, 172(2018), 938-948. (https://doi.org/10.1016/j.jclepro.2017.10.221)
  • French, M. (2018). Fundamentals of Optimization: Methods, Minimum Principles, and Applications for Making Things Better. Switzerland: Springer.
  • Friedman, L., Sinuany-Stern, Z. (1998). Combining ranking scales and selecting variables in the DEA context: The case of industrial branches. Computers & Operations Research, 25(9), 781-791. (https://doi.org/10.1016/S0305-0548(97)00102-0)
  • Garcia-Sánchez, I. M. (2008). The performance of Spanish solid waste collection. Waste Management & Research, 26(4), 327-336. (https://doi.org/10.1177/0734242X07081486)
  • Ghasemi, M. R., Ignatius, J., Emrouznejad, A. (2014). A bi-objective weighted model for improving the discrimination power in MCDEA. European Journal of Operational Research, 233(3), 640-650. (https://doi.org/10.1016/j.ejor.2013.08.041)
  • Ginter, P. M., Starling, J. M. (1978). Reverse distribution channels for recycling. California Management Review, 20(3), 72-82. (https://doi.org/10.2307/41165284)
  • Golany, B., Roll, Y. (1989). An application procedure for DEA. Omega, 17(3), 237-250. (https://doi.org/10.1016/0305-0483(89)90029-7)
  • Guiltinan, J. P., Nwokoye, N. G. (1975). Developing distribution channels and systems in the emerging recycling industries. International Journal of Physical Distribution, 6(1), 28-38. (https://doi.org/10.1108/eb014359)
  • Huang, Y. T., Pan, T. C., Kao, J. J. (2011). Performance assessment for municipal solid waste collection in Taiwan. Journal of environmental management, 92(4), 1277-1283. (https://doi.org/10.1016/j.jenvman.2010.12.002)
  • Hwang, C. L. Masud, A. S. M. (1979). Multiple objective decision making - methods and applications: a state-of-the-art survey. New York: Springer-Verlag.
  • Ichinose, D., Yamamoto, M., Yoshida, Y. (2013). Productive efficiency of public and private solid waste logistics and its implications for waste management policy. IATSS Research, 36(2), 98-105. (https://doi.org/10.1016/j.iatssr.2013.01.002)
  • Jenkins, L., Anderson, M. (2003). A multivariate statistical approach to reducing the number of variables in data envelopment analysis. European Journal of Operational Research, 147(1), 51-61. (https://doi.org/10.1016/S0377-2217(02)00243-6)
  • Khadivi, M. R., Ghomi, S. F. (2012). Solid waste facilities location using of analytical network process and data envelopment analysis approaches. Waste management, 32(6), 1258- 1265. (https://doi.org/10.1016/j.wasman.2012.02.002)
  • Kim, K., Song, I., Kim, J., Jeong, B. (2006). Supply planning model for remanufacturing system in reverse logistics environment. Computers & Industrial Engineering, 51(2), 279-287. (https://doi.org/10.1016/j.cie.2006.02.008)
  • Kontodimopoulos, N., Bellali, T., Labiris, G., Niakas, D. (2006). Investigating sources of inefficiency in residential mental health facilities. Journal of Medical Systems, 30(3), 169- 176. (https://doi.org/10.1007/s10916-005-7981-4)
  • Lewin, A. Y., Morey, R. C., Cook, T. J. (1982). Evaluating the administrative efficiency of courts. Omega, 10(4), 401-411. (https://doi.org/10.1016/0305-0483(82)90019-6)
  • Li, X. B., Reeves, G. R. (1999). A multiple criteria approach to data envelopment analysis. European Journal of Operational Research, 115(3), 507-517. (https://doi.org/10.1016/S0377-2217(98)00130-1)
  • Lozano, S., Villa, G., Adenso-Diaz, B. (2004). Centralised target setting for regional recycling operations using DEA. Omega, 32(2), 101-110. (https://doi.org/10.1016/j.omega.2003.09.012)
  • Marbini, A. H., Toloo, M. (2017). An extended multiple criteria data envelopment analysis model. Expert Systems with Applications, 73, 201-219. (https://doi.org/10.1016/j.eswa.2016.12.030)
  • Marques, R. C., Simões, P. (2009). Incentive regulation and performance measurement of the Portuguese solid waste management services. Waste Management & Research, 27(2), 188- 196. (https://doi.org/10.1177/0734242X08095025)
  • Miettinen, K. (1998). Non1inear multiobjective optimization. New York: Springer Science+Business.
  • Nataraja, N. R., Johnson, A. L. (2011). Guidelines for using variable selection techniques in data envelopment analysis. European Journal of Operational Research, 215(3), 662-669. (https://doi.org/10.1016/j.ejor.2011.06.045)
  • Ramanathan, R. (2003). An Introduction to Data Envelopment Analysis: A Tool for Performance Measurement. California: Sage.
  • Rogers, D. S., Tibben‐Lembke, R. (2001). An examination of reverse logistics practices. Journal of business logistics, 22(2), 129-148. (https://doi.org/10.1002/j.2158-1592.2001.tb00007.x)
  • Rogge, N., De Jaeger, S. (2012). Evaluating the efficiency of municipalities in collecting and processing municipal solid waste: A shared input DEA-model. Waste management, 32(10), 1968-1978. (https://doi.org/10.1016/j.wasman.2012.05.021)
  • Rubem, A. P. S., Brandão, L. C. (2015). Multiple criteria data envelopment analysis–an application to UEFA EURO 2012. Procedia Computer Science, 55(2015), 186-195. (https://doi.org/10.1016/j.procs.2015.07.031)
  • San Cristóbal, J. R. (2011). A multi criteria data envelopment analysis model to evaluate the efficiency of the Renewable Energy Technologies. Renewable Energy, 36(10), 2742-2746. (https://doi.org/10.1016/j.renene.2011.03.008)
  • Shih, C. J., Chang, C. J. (1995). Pareto optimization of alternative global criterion method for fuzzy structural design. Computers & structures, 54(3), 455-460. (https://doi.org/10.1016/0045-7949(94)00341-Y)
  • Thanassoulis, E. (2001). Introduction to the theory and application of data envelopment analysis: A foundation text with integrated software. New York: Springer Science+Business Media.
  • Umarusman, N., Türkmen, A. (2013). Building optimum production settings using De Novo programming with global criterion method. International Journal of Computer Applications, 82(18), 12-15. (URL: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.402.761&rep=rep1&type=pdf
  • Vassiloglou, M., Giokas, D. (1990). A study of the relative efficiency of bank branches: an application of data envelopment analysis. Journal of the Operational Research Society, 41(7), 591-597. (https://doi.org/10.1057/jors.1990.83)
  • Verma, M. K., Mukherjee, V., Yadav, V. K. (2016). Greenfield distribution network expansion strategy with hierarchical GA and MCDEA under uncertainty. International Journal of Electrical Power & Energy Systems, 79(2016), 245-252. (https://doi.org/10.1016/j.ijepes.2016.01.004)
  • Wadhwa, S., Madaan, J., Chan, F. T. S. (2009). Flexible decision modeling of reverse logistics system: A value adding MCDM approach for alternative selection. Robotics and Computer-Integrated Manufacturing, 25(2), 460-469. (https://doi.org/10.1016/j.rcim.2008.01.006)
  • Wagner, J. M., Shimshak, D. G. (2007). Stepwise selection of variables in data envelopment analysis: Procedures and managerial perspectives. European journal of operational research, 180(1), 57-67. (https://doi.org/10.1016/j.ejor.2006.02.048)
  • Wiel, A. V. D., Bossink, B., Masurel, E. (2012). Reverse logistics for waste reduction in cradleto- cradle-oriented firms: waste management strategies in the Dutch metal industry. International Journal of Technology Management, 60(1-2), 96-113. (https://doi.org/10.1504/IJTM.2012.049108)
  • Worthington, A. C., Dollery, B. E. (2001). Measuring efficiency in local government: an analysis of New South Wales municipalities' domestic waste management function. Policy Studies Journal, 29(2), 232-249. (https://doi.org/10.1111/j.1541-0072.2001.tb02088.x)
  • Yadav, V. K., Jha, D. K., Chauhan, Y. K. (2012). A Multi Criteria DEA approach to performance evaluation of Indian thermal power plants. Power System Technology (POWERCON), 2012 IEEE International Conference on, Auckland, New Zealand, 1-5. (https://doi.org/10.1109/PowerCon.2012.6401451)
  • Zhao, M. Y., Cheng, C. T., Chau, K. W., Li, G. (2006). Multiple criteria data envelopment analysis for full ranking units associated to environment impact assessment. International Journal of Environment and Pollution, 28(3-4), 448-464. (https://doi.org/10.1504/IJEP.2006.011222)
  • Zikmund, W. G., Stanton, W. J. (1971). Recycling solid wastes: a channels-of-distribution problem. The Journal of Marketing, 35(3), 34-39. (https://doi.org/10.1177/002224297103500306)