Ideal location selection for new stone crusher machine and landfill using FAHP and TOPSIS method: a case study in a copper mine

Yıl 2021, Cilt: 9 Sayı: 5, 1592 - 1609, 31.10.2021

Nıma Mırzaeı , Özlem Müge Testik

https://doi.org/10.29130/dubited.821490

Cited By: 2

Öz

This study presents a practical approach used to find the best location for installing a new stone crusher machine and the landfill (waste) in the Sarcheshmeh copper in-pit mine located in the southeast of Iran. Fuzzy analytical hierarchal process (FAHP) and technique for order preference by similarity to ideal solution (TOPSIS) are the two methods, which applied in the study as a part of multi-criteria decision making (MCDM) analysis. In the first part FAHP method was utilized to find the ideal location for the stone crusher machine, and in the next part TOPSIS (in combination with Shannon entropy weighting) was used in landfill selection. The analysis was performed using the data collected from experts (engineers, mine specialists, and managers). The North, South, East, and West sides of the mine were considered as potential alternatives, and 21 factors were considered as criteria for computational analysis. The obtained results from FAHP suggested that the best alternative was alternative 3 (East side of the mine) to place the new stone crusher machine. Considering solid waste management, the TOPSIS method demonstrated that alternative 1 (North side of the mine) was the best location to be considered for landfills. Finally, a sensitivity analysis was carried out to examine the effects of changes in weights of criteria on the obtained results.

Anahtar Kelimeler

Multi-criteria decision-making, Shannon entropy, fuzzy analytical hierarchy process (FAHP), location decision technique for order preference by similarity to ideal solution (TOPSIS)

Teşekkür

I would like to extend my sincere thanks to industrial and mine engineers in Sarcheshmeh copper mine for valuable information that help us in this research.

FAHP ve TOPSIS kullanılarak yeni taş kırma makinesi ve katı atık sahası için en iyi yerin seçilmesi: bir bakır madeni örneği

Öz

Bu çalışma, İran'ın güneydoğusundaki Sarcheshmeh bakır çukur madeni için yeni bir taş kırma makinesinin ve katı atık sahasının (atık) yeri hakkında karar vermek için pratik bir yaklaşım sunmaktadır. Bu çalışmada, Bulanık analitik hiyerarşik Prosesi (FAHP) ve technique for order preference by similarity to ideal solution (TOPSIS), çok kriterli karar verme (MCDM) analizinin bir parçası olarak kullanılan iki yöntemdir. Birinci bölümde taş kırma makinesi için en uygun yeri bulmak için FAHP yöntemi kullanılmış, daha sonra depolama alanı seçimi için TOPSIS (Shannon entropi ağırlıklandırması ile birlikte) kullanılmıştır. Analiz, uzmanlar (mühendisler, maden mütehassıslar ve yöneticiler) toplanan verilere dayanılarak yapılmıştır. Madenin kuzey, güney, doğu ve batı tarafı potansiyel alternatifler olarak kabul edilmiş ve hesaplamalar için 21 faktör kriter olarak kabul edilmiştir. FAHP'den elde edilen sonuçlar, yeni taş kırma makinesinin yerleştirilmesi için en iyi alternatifin alternatif 3 (madenin doğu tarafı) olmasını önermiştir. Katı atık yönetimi dikkate alındığında ise TOPSIS yöntemi, alternatif 1'in (madenin kuzey tarafı) düzenli depolama alanları için en iyi yer olduğunu göstermiştir. Son olarak, kriterlerin ağırlıkları değiştirildiğinde sonuçların nasıl değişeceğini incelemek için bir duyarlılık analizi yapılmıştır.

Anahtar Kelimeler

Çok kriterli karar verme, Shannon entropy, bulanık analitik hiyerarşi Prosesi (FAHP), konum kararı, ideal çözüme benzerliğe göre sipariş tercihi tekniği (TOPSIS)

Kaynakça

• [1] I. Alavi, “Fuzzy AHP method for plant species selection in mine reclamation plans: Case study sungun copper mine,” Iran. J. Fuzzy Syst., vol. 11, no. 5, pp. 23–38, 2014.
• [2] M. Salles, “Decision making in SMEs and information requirements for competitive intelligence,” Prod. Plan. Control, vol. 17, no. 3, pp. 229–237, 2006.
• [3] İ. Ertuğrul and N. Karakaşoğlu, “Comparison of fuzzy AHP and fuzzy TOPSIS methods for facility location selection,” Int. J. Adv. Manuf. Technol., vol. 39, no. 7, pp. 783–795, 2008.
• [4] A. Budak and A. Ustundag, “Fuzzy decision making model for selection of real time location systems,” Appl. Soft Comput., vol. 36, pp. 177–184, 2015.
• [5] M. Erdogan and I. Kaya, “A combined fuzzy approach to determine the best region for a nuclear power plant in Turkey,” Appl. Soft Comput. J., vol. 39, pp. 84–93, 2016.
• [6] J. M. Sánchez-Lozano, M. S. García-Cascales, and M. T. Lamata, “Evaluation of suitable locations for the installation of solar thermoelectric power plants,” Comput. Ind. Eng., vol. 87, pp. 343–355, 2015.
• [7] R. Chakraborty, A. Ray, and P. K. Dan, “Multi criteria decision making methods for location selection of distribution centers,” Int. J. Ind. Eng. Comput., vol. 4, no. 4, pp. 491–504, 2013.
• [8] A. H. Bangian, M. Ataei, A. Sayadi, and A. Gholinejad, “Optimizing post-mining land use for pit area in open-pit mining using fuzzy decision making method,” Int. J. Environ. Sci. Technol., vol. 9, no. 4, pp. 613–628, 2012.
• [9] M. Erdoĝan and I. Kaya, “An integrated multi-criteria decision-making methodology based on type-2 fuzzy sets for selection among energy alternatives in Turkey,” Iran. J. Fuzzy Syst., vol. 12, no. 1, pp. 1–25, 2015.
• [10] A. Mardani, A. Jusoh, K. M. D. Nor, Z. Khalifah, N. Zakwan, and A. Valipour, “Multiple criteria decision-making techniques and their applications - A review of the literature from 2000 to 2014,” Econ. Res. Istraz. , vol. 28, no. 1, pp. 516–571, 2015.
• [11] D. Golmohammadi and M. Mellat-Parast, “Developing a grey-based decision-making model for supplier selection,” Int. J. Prod. Econ., vol. 137, no. 2, pp. 191–200, 2012.
• [12] C. Ram, G. Montibeller, and A. Morton, “Extending the use of scenario planning and MCDA for the evaluation of strategic options,” J. Oper. Res. Soc., vol. 62, no. 5, pp. 817–829, 2011.
• [13] G. T. Temur, “A novel multi attribute decision making approach for location decision under high uncertainty,” Appl. Soft Comput. J., vol. 40, pp. 674–682, 2016.
• [14] E. K. Zavadskas, Z. Turskis, and S. Kildienė, “State of art surveys of overviews on MCDM/MADM methods,” Technol. Econ. Dev. Econ., vol. 20, no. 1, pp. 165–179, 2014.
• [15] R. Z. Farahani, M. SteadieSeifi, and N. Asgari, “Multiple criteria facility location problems: A survey,” Appl. Math. Model., vol. 34, no. 7, pp. 1689–1709, 2010.
• [16] C. T. Lin and M. C. Tsai, “Location choice for direct foreign investment in new hospitals in China by using ANP and TOPSIS,” Qual. Quant., vol. 44, no. 2, pp. 375–390, 2010.
• [17] K. Devi and S. P. Yadav, “A multicriteria intuitionistic fuzzy group decision making for plant location selection with ELECTRE method,” Int. J. Adv. Manuf. Technol., vol. 66, no. 9–12, pp. 1219–1229, 2013.
• [18] P. Wang, Z. Zhu, and Y. Wang, “A novel hybrid MCDM model combining the SAW, TOPSIS and GRA methods based on experimental design,” Inf. Sci. (Ny)., vol. 345, pp. 27–45, 2016.
• [19] M. Shaverdi, I. Ramezani, R. Tahmasebi, and A. A. A. Rostamy, “Combining Fuzzy AHP and Fuzzy TOPSIS with Financial Ratios to Design a Novel Performance Evaluation Model,” Int. J. Fuzzy Syst., vol. 18, no. 2, pp. 248–262, 2016.
• [20] Z. Hussain and M. S. Yang, “Entropy for Hesitant Fuzzy Sets Based on Hausdorff Metric with Construction of Hesitant Fuzzy TOPSIS,” Int. J. Fuzzy Syst., vol. 20, no. 8, pp. 2517–2533, 2018.
• [21] F. Samanlioglu, Y. E. Taskaya, U. C. Gulen, and O. Cokcan, “A Fuzzy AHP–TOPSIS-Based Group Decision-Making Approach to IT Personnel Selection,” Int. J. Fuzzy Syst., vol. 20, no. 5, pp. 1576–1591, 2018.
• [22] Saaty T.L., The Analytic Hierarchy Process. New York: McGraw-Hill, 1980.
• [23] T. L. Saaty, “Decision making — the Analytic Hierarchy and Network Processes (AHP/ANP),” J. Syst. Sci. Syst. Eng., vol. 13, no. 1, pp. 1–35, 2004.
• [24] J. Korpela and M. Tuominen, “A decision aid in warehouse site selection,” Int. J. Prod. Econ., vol. 45, no. 1–3, pp. 169–180, 1996.
• [25] J. Yang and H. Lee, “An AHP decision model for facility location selection,” Facilities, vol. 15, no. 9/10, pp. 241–254, 1997.
• [26] M. A. Badri, “Combining the analytic hierarchy process and goal programming for global facility location-allocation problem,” Int. J. Prod. Econ., vol. 62, no. 3, pp. 237–248, 1999.
• [27] P. Alberto, “The Logistics of Industrial Location Decisions: An Application of the Analytic Hierarchy Process Methodology,” Int. J. Logist. Res. Appl. A Lead. J. Supply Chain Manag., vol. 3, no. 3, pp. 273–289, 2000.
• [28] S. Y. Roh, H. M. Jang, and C. H. Han, “Warehouse location decision factors in humanitarian relief logistics,” Asian J. Shipp. Logist., vol. 29, no. 1, pp. 103–120, 2013.
• [29] O. S. Vaidya and S. Kumar, “Analytic hierarchy process: An overview of applications,” European Journal of Operational Research, vol. 169, no. 1. pp. 1–29, 2006.
• [30] S. Önüt and S. Soner, “Transshipment site selection using the AHP and TOPSIS approaches under fuzzy environment,” Waste Manag., vol. 28, no. 9, pp. 1552–1559, 2008.
• [31] G. Wang, L. Qin, G. Li, and L. Chen, “Landfill site selection using spatial information technologies and AHP: A case study in Beijing, China,” J. Environ. Manage., vol. 90, no. 8, pp. 2414–2421, 2009.
• [32] P. V. Gorsevski, K. R. Donevska, C. D. Mitrovski, and J. P. Frizado, “Integrating multi-criteria evaluation techniques with geographic information systems for landfill site selection: A case study using ordered weighted average,” Waste Manag., vol. 32, no. 2, pp. 287–296, 2012.
• [33] K. Hwang, Ching-Lai, Yoon, Multiple Attribute Decision Making - Methods and Applications. A State-of-the-Art Survey. Springer Berlin Heidelberg, 1981.
• [34] C.-L. Hwang, Y.-J. Lai, and T.-Y. Liu, “A new approach for multiple objective decision making,” Comput. Oper. Res., vol. 20, no. 8, pp. 889–899, Oct. 1993.
• [35] S. Beheshtifar and A. Alimoahmmadi, “A multiobjective optimization approach for location-allocation of clinics,” Int. Trans. Oper. Res., vol. 22, no. 2, pp. 313–328, 2015.
• [36] L. Anojkumar, M. Ilangkumaran, and V. Sasirekha, “Comparative analysis of MCDM methods for pipe material selection in sugar industry,” Expert Syst. Appl., vol. 41, no. 6, pp. 2964–2980, 2014.
• [37] U. Di Matteo, P. M. Pezzimenti, and A. A. D, “Methodological proposal for optimal location of emergency operation centers through multi-criteria approach,” Sustain., vol. 8, no. 1, pp. 1–12, 2016.
• [38] P. P. Kalbar, S. Karmakar, and S. R. Asolekar, “Selection of an appropriate wastewater treatment technology: A scenario-based multiple-attribute decision-making approach,” J. Environ. Manage., vol. 113, pp. 158–169, 2012.
• [39] C. Rao, M. Goh, Y. Zhao, and J. Zheng, “Location selection of city logistics centers under sustainability,” Transp. Res. Part D Transp. Environ., vol. 36, pp. 29–44, 2015.
• [40] A. Awasthi, S. S. Chauhan, and S. K. Goyal, “A multi-criteria decision making approach for location planning for urban distribution centers under uncertainty,” Math. Comput. Model., vol. 53, no. 1–2, pp. 98–109, 2011.
• [41] M. Boomeri, K. Nakashima, and D. R. Lentz, “The Sarcheshmeh porphyry copper deposit, Kerman, Iran: A mineralogical analysis of the igneous rocks and alteration zones including halogen element systematics related to Cu mineralization processes,” Ore Geol. Rev., vol. 38, no. 4, pp. 367–381, 2010.
• [42] P. J. M. van Laarhoven and W. Pedrycz, “A fuzzy extension of Saaty’s priority theory,” Fuzzy Sets Syst., vol. 11, no. 1–3, pp. 229–241, 1983.
• [43] A. Mardani, A. Jusoh, and E. K. Zavadskas, “Fuzzy multiple criteria decision-making techniques and applications - Two decades review from 1994 to 2014,” Expert Syst. Appl., vol. 42, no. 8, pp. 4126–4148, 2015.
• [44] J. Yang and H. Lee, “Facilities An AHP decision model for facility location selection,” Facil. Int. J. Oper. &amp Prod. Manag. Iss, vol. 15, no. 22, pp. 241–254, 1997.
• [45] R. J. Kuo, S. C. Chi, and S. S. Kao, “A decision support system for selecting convenience store location through integration of fuzzy AHP and artificial neural network,” Comput. Ind., vol. 47, no. 2, pp. 199–214, 2002.
• [46] B. Ka, “Application of Fuzzy AHP and ELECTRE to China Dry Port Location Selection,” Asian J. Shipp. Logist., vol. 27, no. 2, pp. 331–353, 2011.
• [47] D. Choudhary and R. Shankar, “An STEEP-fuzzy AHP-TOPSIS framework for evaluation and selection of thermal power plant location: A case study from India,” Energy, vol. 42, no. 1, pp. 510–521, 2012.
• [48] D. Ozgen and B. Gulsun, “Combining possibilistic linear programming and fuzzy AHP for solving the multi-objective capacitated multi-facility location problem,” Inf. Sci. (Ny)., vol. 268, pp. 185–201, 2014.
• [49] X. Du and Z. Wang, “Optimizing monitoring locations using a combination of GIS and fuzzy multi criteria decision analysis, a case study from the Tomur World Natural Heritage site,” J. Nat. Conserv., vol. 43, pp. 67–74, 2018.
• [50] D.-Y. Chang, “Applications of the extent analysis method on fuzzy AHP,” Eur. J. Oper. Res., vol. 95, no. 3, pp. 649–655, 1996.
• [51] C. E. Shannon, “A Mathematical Theory of Communication,” Bell Syst. Tech. J., vol. 27, no. 4, pp. 623–656, 1948.
• [52] C.-L. Hwang and K. Yoon, Multiple Attribute Decision Making, vol. 186. Berlin, Heidelberg: Springer Berlin Heidelberg, 1981.
• [53] A. Amini and A. Alinezhad, “Sensitivity Analysis of TOPSIS Technique: The Results of Change in the Weight of One Attribute on the Final Ranking of Alternatives,” J. Optim. Ind. Eng., vol. 7, no. 2011, pp. 23–28, 2011.
• [54] P. Li, H. Qian, J. Wu, and J. Chen, “Sensitivity analysis of TOPSIS method in water quality assessment: I. Sensitivity to the parameter weights,” Environ. Monit. Assess., vol. 185, no. 3, pp. 2453–2461, 2013.