Site Selection for Limestone Paper Plant Using AHP-Monte Carlo Approach

Document Type : Regular Article


1 Assistant Professor, Department of Mining, Faculty of Engineering, University of Kurdistan (UOK), Sanandaj, Iran

2 M.Sc. Student, Department of Mining, Faculty of Engineering, University of Kurdistan (UOK), Sanandaj, Iran

3 Assistant Professor, Department of Mining and Metallurgical Engineering, Urmia University of Technology, Urmia, Iran


Paper played a crucial role in the history of the development of human society. Even in current times in the modern world, with Tablet, eBook readers and smart phones, the use of paper is still unavoidable. The wood needed for the production of the paper is provided by cutting down trees; hence, paper production has a cost to the environment. Recently, new technology has been developed which uses limestone instead of wood as the main material for paper production. This technology is environmentally friendly compared to the traditional paper-making technology. Choosing a suitable location for construction of such paper production plant based on different factors affecting paper quality is of great importance. To choose the desired location of such a plant, it is proposed to use a combination of Monte Carlo, and Analytical Hierarchic Process approaches. In this way, in the search area, there is a distribution of rates for each pixel instead of a single rate which allows determining the appropriate location for different confidence levels. The proposed method has been applied on Bijar, one of the cites of Kurdistan province in Iran, and a suitable location of the paper production plant is highlighted for various levels of confidence.


Google Scholar


Main Subjects

[2]     Kim N, Park J, Choi J-J. Perceptual differences in core competencies between tourism industry practitioners and students using Analytic Hierarchy Process (AHP). J Hosp Leis Sport Tour Educ 2017;20:76–86. doi:10.1016/j.jhlste.2017.04.003.
[3]     Saaty TL. A scaling method for priorities in hierarchical structures. J Math Psychol 1977;15:234–81. doi:10.1016/0022-2496(77)90033-5.
[4]     Saaty TL. Modeling unstructured decision problems — the theory of analytical hierarchies. Math Comput Simul 1978;20:147–58. doi:10.1016/0378-4754(78)90064-2.
[5]     Saaty TL. Applications of analytical hierarchies. Math Comput Simul 1979;21:1–20. doi:10.1016/0378-4754(79)90101-0.
[6]     Sindhu S, Nehra V, Luthra S. Investigation of feasibility study of solar farms deployment using hybrid AHP-TOPSIS analysis: Case study of India. Renew Sustain Energy Rev 2017;73:496–511. doi:10.1016/j.rser.2017.01.135.
[7]     Arsić S, Nikolić D, Živković Ž. Hybrid SWOT - ANP - FANP model for prioritization strategies of sustainable development of ecotourism in National Park Djerdap, Serbia. For Policy Econ 2017;80:11–26. doi:10.1016/j.forpol.2017.02.003.
[8]     Kokangül A, Polat U, Dağsuyu C. A new approximation for risk assessment using the AHP and Fine Kinney methodologies. Saf Sci 2017;91:24–32. doi:10.1016/j.ssci.2016.07.015.
[9]     Choudhary D, Shankar R. An STEEP-fuzzy AHP-TOPSIS framework for evaluation and selection of thermal power plant location: A case study from India. Energy 2012;42:510–21. doi:10.1016/
[10]    Büyüközkan G, Çifçi G. A combined fuzzy AHP and fuzzy TOPSIS based strategic analysis of electronic service quality in healthcare industry. Expert Syst Appl 2012;39:2341–54. doi:10.1016/j.eswa.2011.08.061.
[11]    Kaya T, Kahraman C. Multicriteria renewable energy planning using an integrated fuzzy VIKOR & AHP methodology: The case of Istanbul. Energy 2010;35:2517–27. doi:10.1016/
[12]    Naghadehi MZ, Mikaeil R, Ataei M. The application of fuzzy analytic hierarchy process (FAHP) approach to selection of optimum underground mining method for Jajarm Bauxite Mine, Iran. Expert Syst Appl 2009;36:8218–26. doi:10.1016/j.eswa.2008.10.006.
[13]    Garg M, Kumar M. Identifying influential segments from word co-occurrence networks using AHP. Cogn Syst Res 2018;47:28–41. doi:10.1016/j.cogsys.2017.07.003.
[14]    Awasthi A, Govindan K, Gold S. Multi-tier sustainable global supplier selection using a fuzzy AHP-VIKOR based approach. Int J Prod Econ 2018;195:106–17. doi:10.1016/j.ijpe.2017.10.013.
[15]    Dağdeviren M, Yavuz S, Kılınç N. Weapon selection using the AHP and TOPSIS methods under fuzzy environment. Expert Syst Appl 2009;36:8143–51. doi:10.1016/j.eswa.2008.10.016.
[16]    Naess A, Gaidai O, Teigen PS. Extreme response prediction for nonlinear floating offshore structures by Monte Carlo simulation. Appl Ocean Res 2007;29:221–30. doi:10.1016/j.apor.2007.12.001.
[17]    Talebi S, Gharehbash K, Jalali HR. Study on random walk and its application to solution of heat conduction equation by Monte Carlo method. Prog Nucl Energy 2017;96:18–35. doi:10.1016/j.pnucene.2016.12.004.
[18]    Iwabuchi H, Okamura R. Multispectral Monte Carlo radiative transfer simulation by the maximum cross-section method. J Quant Spectrosc Radiat Transf 2017;193:40–6. doi:10.1016/j.jqsrt.2017.01.025.
[19]    Sun H-F, Sun F-X, Xia X-L. A line-by-line hybrid unstructured finite volume/Monte Carlo method for radiation transfer in 3D non-gray medium. J Quant Spectrosc Radiat Transf 2018;205:135–46. doi:10.1016/j.jqsrt.2017.09.030.
[20]    Lang A, Petersson A. Monte Carlo versus multilevel Monte Carlo in weak error simulations of SPDE approximations. Math Comput Simul 2018;143:99–113. doi:10.1016/j.matcom.2017.05.002.
[21]    Ataei M, Shahsavany H, Mikaeil R. Monte Carlo Analytic Hierarchy Process (MAHP) approach to selection of optimum mining method. Int J Min Sci Technol 2013;23:573–8. doi:10.1016/j.ijmst.2013.07.017.
[22]    Li S, Li JZ. Hybridising human judgment, AHP, simulation and a fuzzy expert system for strategy formulation under uncertainty. Expert Syst Appl 2009;36:5557–64. doi:10.1016/j.eswa.2008.06.095.
[23]    Momani AM, Ahmed AA. Material handling equipment selection using hybrid Monte Carlo simulation and analytic hierarchy process. World Acad Sci Eng Technol 2011;59:953–8.
[24]    Hsu T-H, Pan FFC. Application of Monte Carlo AHP in ranking dental quality attributes. Expert Syst Appl 2009;36:2310–6. doi:10.1016/j.eswa.2007.12.023.
[25]    Dahri N, Abida H. Monte Carlo simulation-aided analytical hierarchy process (AHP) for flood susceptibility mapping in Gabes Basin (southeastern Tunisia). Environ Earth Sci 2017;76:302. doi:10.1007/s12665-017-6619-4.
[26]    Jing L, Chen B, Zhang B, Li P, Zheng J. Monte Carlo Simulation–Aided Analytic Hierarchy Process Approach: Case Study of Assessing Preferred Non-Point-Source Pollution Control Best Management Practices. J Environ Eng 2013;139:618–26. doi:10.1061/(ASCE)EE.1943-7870.0000673.
[27]    LIAO G, NIE C, LIU X. The Risk Analysis of the Cost of Construction Project Based on CIM-AHP Model and Combination Weighting Method. DEStech Trans Comput Sci Eng 2017. doi:10.12783/dtcse/mcsse2016/10982.
[28]    Kerzner H. Project management: a systems approach to planning, scheduling, and controlling. John Wiley & Sons; 2003.
[29]    Uyan M. GIS-based solar farms site selection using analytic hierarchy process (AHP) in Karapinar region, Konya/Turkey. Renew Sustain Energy Rev 2013;28:11–7. doi:10.1016/j.rser.2013.07.042.
[30]    Talinli  lhan, Topuz E, Egemen, Kabakc S. A Holistic Approach for Wind Farm Site Selection by Using FAHP. Wind Farm - Tech. Regul. Potential Estim. Siting Assess., InTech; 2011. doi:10.5772/17311.
[31]    Aragonés-Beltrán P, Chaparro-González F, Pastor-Ferrando J-P, Pla-Rubio A. An AHP (Analytic Hierarchy Process)/ANP (Analytic Network Process)-based multi-criteria decision approach for the selection of solar-thermal power plant investment projects. Energy 2014;66:222–38. doi:10.1016/