A Developed Fuzzy Goal Programming Approach to Determine a Processing Plant Site

Document Type : Research Article

Authors

Dept. of Mining and Materials Engineering, Urmia University of Technology, Iran

10.29252/anm.8.15.1

Abstract

Summary
In this paper, a fuzzy-based goal programming approach was developed in order to solve the problem of processing plant site determination. In order to determine the efficiency of the developed approach, Sungun copper area was studied. In this case, five goals were considered as proximity to crusher, tailing dam, power source, distance from blasting sources, and topography of the available land. Furthermore, six feasible alternatives were initially specified by studying Sungun area map, and then were prioritized using the approach. After investigating the Sungun area, it was concluded that the approach is capable in determining the most ideal alternative in this case, considering the levels of the goals and their fuzzy weights. A fuzzy-based goal programming approach was developed in order to solve the processing plant site determination problem, with application in Sungun copper mine.
 
Introduction
Since a processing plant is usually used during the mine-life, finding the most ideal site helps in reducing operation costs. The researches related to solving this problem are very limited to a few cases, by developing multi-attribute decision-making methods such as fuzzy TOPSIS. In this study, a different approach has been developed based on fuzzy goal programming approach.
 
Methodology and Approaches
First, the most important goals are considered together with the feasible alternatives. Then, a pairwise comparison matrix of the considered goals is created based on triangular fuzzy numbers, in order to find the weight of each goal. In next step, a membership function is defined for each goal. After that, a maximization objective function is mathematically defined based on the model introduced by Tiwari et al. (1987), by considering both weights and membership functions of the goals. Finally, the mathematical model is solved using the Solver tool in Excel; as a result, the alternatives are prioritized.
 
Results and Conclusions
After in-field investigation of the considered alternatives, it was concluded that the alternatives were appropriately prioritized and the ideal site was determined using the developed approach.

Keywords

Main Subjects

References

[1] Ataei, M. (2005). Multicriteria selection for an alumina-cement plant location in East Azerbaijan province of Iran. Journal of the South African Institute of Mining and Metallurgy, 105(7), 507-514.
[2] Yong, D. (2006). Plant location selection based on fuzzy TOPSIS. The International Journal of Advanced Manufacturing Technology, 28(7-8), 839-844.
[3] Devi, K., & Yadav, S. P. (2013). A multicriteria intuitionistic fuzzy group decision making for plant location selection with ELECTRE method. The International Journal of Advanced Manufacturing Technology, 66(9-12), 1219-1229.
[4] Choudhary, D., & Shankar, R. (2012). An STEEP-fuzzy AHP-TOPSIS framework for evaluation and selection of thermal power plant location: A case study from India. Energy, 42(1), 510-521.
[5] Mousavi, S. M., Tavakkoli-Moghaddam, R., Heydar, M., & Ebrahimnejad, S. (2013). Multi-criteria decision making for plant location selection: an integrated Delphi–AHP–PROMETHEE methodology. Arabian Journal for Science and Engineering, 38(5), 1255-1268.
[6] Safari, M., Ataei, M., Khalokakaie, R., & Karamozian, M. (2010). Mineral processing plant location using the analytic hierarchy process—a case study: the Sangan iron ore mine (phase 1). Mining Science and Technology (China), 20(5), 691-695.
[7] Safari, M., Kakaei, R., Ataei, M., & Karamoozian, M. (2012). Using fuzzy TOPSIS method for mineral processing plant site selection. Arabian Journal of Geosciences, 5(5), 1011-1019.
[8] Kwak, N. K., & Schniederjans, M. J. (1985). A Goal Programming model for selecting a facility location site. Revue française d'automatique, d'informatique et de recherche opérationnelle. Recherche opérationnelle, 19(1), 1-14.
[9] Bhattacharya, U., Rao, J. R., & Tiwari, R. N. (1993). Bi-criteria multi facility location problem in fuzzy environment. Fuzzy Sets and Systems, 56(2), 145-153.
[10] Chang, N. B., & Wang, S. F. (1997). A fuzzy goal programming approach for the optimal planning of metropolitan solid waste management systems. European journal of operational research, 99(2), 303-321.
[11] Zarandi, M. H. F., Davari, S., Hamidifar, M., & Türksen, I. B. (2011). Locating Post Offices Using Fuzzy Goal Programming and Geographical Information System (GIS). In AMCIS.
[12] Aouni, B., Kettani, O., & Martel, J. M. (1997). Estimation through the imprecise goal programming model (pp. 120-128). Springer Berlin Heidelberg.
[13] Charnes, A., & Cooper, W.W., (1961), Management Models and Industrial Applications of Linear Programming, Wiley, New York.
[14] Jones, D. F., & Tamiz, M. (2002). Goal programming in the period 1990–2000. In Multiple Criteria Optimization: State of the art annotated bibliographic surveys (pp. 129-170). Springer US.
[15] Tamiz, M., Jones, D. F., & El-Darzi, E. (1995). A review of goal programming and its applications. Annals of Operations Research, 58(1), 39-53.
[16] Tamiz, M., Jones, D., & Romero, C. (1998). Goal programming for decision making: An overview of the current state-of-the-art. European Journal of operational research, 111(3), 569-581.
[17] Yaghoobi, M. A., & Tamiz, M. (2007). A method for solving fuzzy goal programming problems based on MINMAX approach. European Journal of Operational Research, 177(3), 1580-1590.
[18] Bellman, R. E., & Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Management science, 17(4), B-141.
[19] Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
[20] Narasimhan, R. (1980). Goal programming in a fuzzy environment. Decision sciences, 11(2), 325-336.
[21] Hannan, E. L. (1981). ON FUZZY GOAL PROGRAMMING*. Decision Sciences, 12(3), 522-531.
[22] Yang, T., Ignizio, J. P., & Kim, H. J. (1991). Fuzzy programming with nonlinear membership functions: piecewise linear approximation. Fuzzy sets and systems, 41(1), 39-53.
[23] Tiwari, R. N., Dharmar, S., & Rao, J. R. (1987). Fuzzy goal programming—an additive model. Fuzzy sets and systems, 24(1), 27-34.
[24] Zimmermann, H. J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy sets and systems, 1(1), 45-55.
[25] Zimmerman, H. J. (1983). Using fuzzy sets in operational research. European Journal of Operational Research, 13(3), 201-216.
Journal of Analytical and Numerical Methods in Mining Engineering
Volume 8, Issue 15
May 2018
Pages 1-9

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History

  • Receive Date: 09 January 2016
  • Revise Date: 22 July 2018
  • Accept Date: 17 December 2016

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