Long-Term Room and Pillar Mine Production Planning Based on Fuzzy 0-1 Linear Programing and Multicriteria Clustering Algorithm with Uncertainty

Production planning in an underground mine plays a key activity in the mining company business. It is supported by the fact that mineral industry is unique and volatile environment. There are two uncertain parameters that cannot be managed by planners, metal price, and operating costs. Having ability to quantify and incorporate them in the process of planning can help companies to do their business in much easier way. We quantify these uncertainties by the simulation of mean reverting process and Ito-Doob stochastic differential equation, respectively. Mineral deposit is represented as a set of mineable blocks and room and pillar mining method is selected as a way of mining. Multicriteria clustering algorithm is used to create areas inside of mineral deposit that have technological characteristics required by the planners. We also developed a way to forecast the volatility of economic values of these areas through the planning period. Fuzzy 0-1 linear programming model is used to define the sequence of mining of these areas by maximization of the expected value of the fuzzy future cash flow. Model was tested on small hypothetical lead-zinc mineral deposit and results showed that our approach was able to solve such complex problem.

[1]  Jim Z. C. Lai,et al.  A Fuzzy K-means Clustering Algorithm Using Cluster Center Displacement , 2009, J. Inf. Sci. Eng..

[2]  Wei Tian,et al.  Implementation of the Fuzzy C-Means Clustering Algorithm in Meteorological Data , 2013 .

[3]  Erkan Topal,et al.  Integrated short and medium term underground mine production scheduling , 2012 .

[4]  Chen-Tung Chen,et al.  Extensions of the TOPSIS for group decision-making under fuzzy environment , 2000, Fuzzy Sets Syst..

[5]  George Karypis,et al.  Multilevel k-way Partitioning Scheme for Irregular Graphs , 1998, J. Parallel Distributed Comput..

[6]  Yadong Wang,et al.  Improving fuzzy c-means clustering based on feature-weight learning , 2004, Pattern Recognit. Lett..

[7]  Xiaoyu Bai,et al.  Underground stope optimization with network flow method , 2013, Comput. Geosci..

[8]  Andreas Bley,et al.  An improved formulation of the underground mine scheduling optimisation problem when considering selective mining SE Terblanche , 2012 .

[9]  Milan Zeleny,et al.  Multiple Criteria Decision Making (MCDM) , 2004 .

[10]  Erkan Topal,et al.  Implementing a Production Schedule at LKAB's Kiruna Mine , 2004, Interfaces.

[11]  Amit Kumar,et al.  A new method for solving fully fuzzy linear programming problems , 2011 .

[12]  Taho Yang,et al.  Multiple-attribute decision making methods for plant layout design problem , 2007 .

[13]  W. Matthew Carlyle,et al.  Underground Planning at Stillwater Mining Company , 2001 .

[14]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[15]  R. Dimitrakopoulos,et al.  Managing grade risk in stope design optimisation: probabilistic mathematical programming model and application in sublevel stoping , 2007 .

[16]  Ezzeddin Bakhtavar,et al.  Optimization of the transition from open-pit to underground operation in combined mining using (0-1) integer programming , 2012 .

[17]  Erkan Topal,et al.  A new mathematical programming model for production schedule optimization in underground mining operations , 2010 .

[18]  Ying-Ming Wang,et al.  Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment , 2006, Expert Syst. Appl..

[19]  A. Kaufmann,et al.  Introduction to fuzzy arithmetic : theory and applications , 1986 .

[20]  Leen-Kiat Soh,et al.  Polygonal spatial clustering , 2011 .

[21]  Sasa Jovanovic,et al.  Hybrid model of evaluation of underground lead—zinc mine capacity expansion project using Monte Carlo simulation and fuzzy numbers , 2011, Simul..

[22]  Ronald R. Yager,et al.  A procedure for ordering fuzzy subsets of the unit interval , 1981, Inf. Sci..

[23]  Rasim M. Alguliyev,et al.  Multicriteria Personnel Selection by the Modified Fuzzy VIKOR Method , 2015, TheScientificWorldJournal.

[24]  G. S. Ladde,et al.  Stochastic versus Deterministic Systems of Differential Equations , 2003 .

[25]  Alexandra M. Newman,et al.  Is openpit production scheduling “easier” than its underground counterpart? , 2015 .

[26]  Erkan Topal,et al.  Early start and late start algorithms to improve the solution time for long-term underground mine production scheduling , 2008 .

[27]  Ching-Lai Hwang,et al.  Multiple Attribute Decision Making: Methods and Applications - A State-of-the-Art Survey , 1981, Lecture Notes in Economics and Mathematical Systems.

[28]  Milos Gligoric,et al.  Model of room and pillar production planning in small scale underground mines with metal price and operating cost uncertainty , 2020 .

[29]  Gregor von Laszewski,et al.  Intelligent Structural Operators for the k-way Graph Partitioning Problem , 1991, ICGA.

[30]  T. Chu Selecting Plant Location via a Fuzzy TOPSIS Approach , 2002 .

[31]  J. Bezdek,et al.  FCM: The fuzzy c-means clustering algorithm , 1984 .

[32]  Joseph Christian Hirschi,et al.  A Dynamic Programming Approach to Identifying Optimal Mining Sequences for Continuous Miner Coal Production Systems , 2012 .

[33]  Angelina Konadu Anani,et al.  Applications of simulation and optimization techniques in optimizing room and pillar mining systems , 2016 .

[34]  Felipe Caro,et al.  Optimizing Long-Term Production Plans in Underground and Open-Pit Copper Mines , 2010, Oper. Res..

[35]  Leen-Kiat Soh,et al.  Redistricting Using Heuristic-Based Polygonal Clustering , 2009, 2009 Ninth IEEE International Conference on Data Mining.

[36]  Leen-Kiat Soh,et al.  Redistricting Using Constrained Polygonal Clustering , 2012, IEEE Transactions on Knowledge and Data Engineering.

[37]  Eduardo S. Schwartz The stochastic behavior of commodity prices: Implications for valuation and hedging , 1997 .

[38]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .