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Review of scheduling algorithms in Open Pit Mining
The document reviews various scheduling algorithms used in open pit mining, highlighting the lack of universally accepted methodologies and the ongoing development of new algorithms. It discusses the pit scheduling problem, including different formulations like bench-phase, dynamic programming (DP), mixed-integer linear programming (MILP), and their respective advantages. The document concludes with a note on the paradigm shift in mine scheduling, indicating that phases are now emerging from scheduling algorithms rather than being pre-designed.
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Review of scheduling algorithms in Open Pit Mining
- 1. Review of Scheduling Algorithms inOpen Pit Mining ING.JOSE GONZALES BORJA FACULTAD DE INGENIERIA GEOLOGICA, MINERA Y METALURGICA LIMA, AUGUST 2015
- 2. Summary - Lack of“universally accepted” methodology: ◦ Ad-hoc solutions for specific cases are useless for other cases ◦ Partially explained by commercial interests of software and consulting companies - Heuristic techniques are poorly understood by mine planning practitioners: ◦ A fair comparison can’t be done without understanding the low-level details of the algorithm ◦ Thus, the NPV criterion is incorrectly used as a proxy of superiority of a given algorithm - Every year a new algorithm is developed by academics and/or industry researchers across the world. Is Peru ready to compete?
- 3. Pit scheduling problem– Bench Phase formulation - In a bench-phase model, the decision variables are the tonnages mined in each available bench-phase at every period, subject to the precedencies established between successive phases and the “bench above” rule. - The objective function is maximize the NPV of cash flows obtained through the life of mine, taking into consideration mining costs, processing costs, capital costs and revenues by period. - Constraints may include vertical advance in benches per phase per period, truck hours, loader hours, tonnages from specific regions of the deposit, or period constraints such as “don’t start Phase X until Period T”. - This model is the most flexible and user-friendly for mining practitioners.
- 4. Pit scheduling problem- DP formulation 𝑉 𝑅, 𝑇 = 𝑀𝑎𝑥 𝑎𝑙𝑙 𝜔 𝑐 𝑡, 𝑟, 𝜔 + 𝑉(𝑅 − 𝑟, 𝑇 + 𝑡) 1 + 𝛿 𝑡 , 0 ≤ 𝑟 ≤ 𝑅 Which means: “The maximum NPV (V) of the entire reserve (R), at a time (T), can be calculated by considering all feasible strategies () and picking the maximum sum of the cash flow (c) of a portion of the reserve (r) and the maximum value of the remaining reserve. A discount rate () is used to adjust the remaining value by the time (t) taken to mine an increment of the reserve”.
- 5. Pit scheduling problem- MILP formulation 𝑀𝑎𝑥 𝑝 𝑏𝑑𝑡 𝑦 𝑏𝑑𝑡 𝑡 ∈𝑇𝑑 ∈𝐷𝑏 ∈𝐵 Subject to 𝑥 𝑏𝜏 ≤ 𝑥 𝑏 𝜏 𝜏 ≤𝑡 ∀𝑏 ∈ 𝐵, 𝑏 ∈ 𝐵, 𝑡 ∈ 𝑇 𝜏 ≤𝑡 𝑥 𝑏𝑡 = 𝑦 𝑏𝑑𝑡 ∀𝑏 ∈ 𝐵, 𝑡 ∈ 𝑇 𝑑∈𝐷 𝑥 𝑏𝑡 ≤ 1 ∀𝑏 ∈ 𝐵 𝑡∈𝑇 𝑅 𝑟𝑡 ≤ 𝑞 𝑏𝑟𝑑 𝑦 𝑏𝑑𝑡 ≤ 𝑅 𝑟𝑡 𝑑∈𝐷𝑏∈𝐵 𝑟 ∈ 𝑅, ∀𝑡 ∈ 𝑇 𝑦 𝑏𝑑𝑡 ∈ 0,1 ; 𝑥 𝑏𝑡 ∈ 0,1 ∀𝑏 ∈ 𝐵, 𝑑 ∈ 𝐷, 𝑡 ∈ 𝑇 where
- 6. Pit scheduling problem- MILP formulation (cont.) 𝑡 ∈ 𝑇: set of time periods in the horizon 𝑏 ∈ 𝐵: set of blocks 𝑏′ ∈ 𝐵: set of predecessor blocks for block b 𝑟 ∈ 𝑅: set of operational resources 𝑑 ∈ 𝐷: set of destinations 𝑝 𝑏𝑑𝑡 = 𝑝 𝑏𝑑 (1+ 𝛼) 𝑡 : profit obtained from processing block b when sending it to destination d at time period t; 𝛼 is discount rate 𝑞 𝑏𝑟𝑑: amount of resource r used to process block b when sent to destination d
- 7. Pit scheduling problem- MILP formulation (cont.) 𝑅 𝑟𝑡: minimum availability of operational resource r in time period t 𝑅 𝑟𝑡: maximum availability of operational resource r in time period t 𝑥 𝑏𝑡: binary variable equal to 1 if block b is extracted in time period t 𝑦 𝑏𝑑𝑡: the amount of block b sent to destination d in time period t
- 8. Pit scheduling problem– SIP formulation 𝑀𝑎𝑥 𝑉𝑖 𝑡 𝑏𝑖 𝑡 − 𝑐 𝑢 𝑡 𝑑 𝑠𝑢 𝑡 + 𝑐𝑙 𝑡 𝑑 𝑠𝑙 𝑡 𝑀 𝑠=1 𝑁 𝑖=1 𝑃 𝑡=1 Subject to ◦ 𝐺𝑠𝑖 − 𝐺 𝑚𝑖𝑛 𝑂𝑠𝑖 𝑏𝑖 𝑡 + 𝑑 𝑠𝑙 𝑡 − 𝑚 𝑠𝑙 𝑡 = 0 𝑁 𝑖=1 ◦ 𝐺𝑠𝑖 − 𝐺 𝑚𝑎𝑥 𝑂𝑠𝑖 𝑏𝑖 𝑡 + 𝑑 𝑠𝑢 𝑡 − 𝑚 𝑠𝑢 𝑡 = 0 𝑁 𝑖=1 where
- 9. Pit scheduling problem– SIP formulation (cont.) 𝑏𝑖 𝑡 = percentage of block 𝑖 mined in period 𝑡; there are 𝑁 blocks and 𝑃 periods 𝑑 𝑠𝑢 𝑡 = excess of ore tonnage above the upper limit, in period 𝑡 for block model 𝑠, there are 𝑀 equiprobable block models 𝑑 𝑠𝑙 𝑡 = deficit of ore tonnage below the lower limit, in period 𝑡 for block model 𝑠 𝑚 𝑠𝑢 𝑡 = dummy variable to balance the second equality constraint 𝑚 𝑠𝑙 𝑡 = dummy variable to balance the first equality constraint 𝑉𝑖 𝑡 = expected discounted value of block 𝑖 when mined in period 𝑡, averaged among all block models
- 10. Pit scheduling problem– SIP formulation (cont.) 𝐺𝑠𝑖 = grade of block 𝑖 in orebody model 𝑠 𝐺 𝑚𝑖𝑛, 𝐺 𝑚𝑎𝑥 = minimum and maximum target grades of the ore 𝑂𝑠𝑖 = ore tonnage of block 𝑖 in orebody model 𝑠
- 11. Route map: modelsand algorithms Algorithms ↓ Models BP MILP DP SIP MSSP ® Milawa ® Tolwinski COMET® Branch and cut Lagrangian relaxation Fundamental trees Ant colony optimization Genetic algorithms Critical Multipliers DeepMine®
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- 13. MSSP® - Cai andBanfield, 1979 – United States - Used in Minesight Strategic Planner (MSSP®), now with support discontinued - The bottom benches are mined in fractions via linear programming with the status of direct mill feed stockpiles considered automatically - Also in Step 6, “ the materials mined from all pushbacks can be allocated to available material destinations by linear programming on a pushback to destination basis” (excerpt from Minesight for Engineers, Mintec®).
- 14. Milawa® algorithm - Unknownauthor, 1999 - Used in Whittle ®, now a product of Dassault Systèmes - France - Variables are benches in each pushback and regions of high value are identified with a heuristic approach - considers two constraints per period: ◦ Minimum and maximum separation between pushbacks ◦ Maximum vertical advance - all mining in a phase is assumed to occur at the same rate
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- 16. Tolwinski algorithm - Tolwinski,1992 - United States - Used in NPV Scheduler®, now a product of CAE Inc. - Canada - It combines ideas from dynamic programming with stochastic search heuristics to produce feasible solutions to the problem. - Dynamic programming states grows exponentially with number of blocks, making the problem intractable for large open pits.
- 17. COMET® algorithm - King,2000 - United Kingdom - Used in COMET®, a product of Comet Strategy - Australia - Works as an add-in to Microsoft Excel® - Simultaneous optimization of cutoff grades, dilution and comminution - Requires a “seed” schedule from which the program iterates - Requires pre-defined pushbacks
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- 19. Lagrangian relaxation - Dagdelen,1985 - United States - Used in Colorado School of Mines – Not available commercially. - In his PhD thesis, Dagdelen solves the MILP problem with Lagrange multipliers, but failed in guaranteeing convergence for the general case. - Akaike (1999) and then Kawahata (2006) expands this procedure to solve the convergence issue by using more multipliers and changing the iteration scheme for determining the value of the multipliers.
- 20. Branch and cutalgorithm - Caccetta & Hill, 2003 – Australia - It was used in MineMap software (Australia), but now is out of business. - Caccetta demonstrated rigorously that the ultimate pit obtained with Lerchs & Grossmann is an upper bound of the MILP solution. - Instead of the branch and bound method used in Minemax®, Caccetta uses auxiliary heuristics to select which branch is analyzed in depth and which one is cut, using 17000 lines of code in C++. - A model of 200,000 blocks and 23,000 constraints produced a solution guaranteed to be within 2.5% of the optimum in 4 hours.
- 21. Fundamental tree algorithm -Ramazan, 2007 – Australia - Not available commercially - Reduces the number of binary variables required in the MILP model by solving a LP model to find the fundamental trees, by minimizing the arc connections in the network weighted by the assigned ranks. - After generating the fundamental trees for a given orebody model, the MILP model uses each tree as a block having certain attributes. - A case study showed a reduction from 38,457 variables in the raw MILP model to 5,512 with the use of the FT algorithm. - Requires pre-defined pushbacks
- 22. Ant colony optimization -Sattarvand & Niemann-Delius, 2011 - Iran/Germany - Not available commercially - When one ant finds a good path from the colony to a food source, other ants are more likely to follow that path, and eventually all the ants will follow a single path = emerging behavior. - By repeated iterations, the pheromone values of those blocks that define the shape of the optimum solution are increased, whereas those of the others have been significantly evaporated. - However, “a trial and error process might be necessary at the beginning to set the relevant combination of parameters for each individual case”, i.e., number of ants, amount of pheromone, and evaporation rates.
- 23. Genetic algorithm - BitanshuDas, 2012- India - Not available commercially - Mimics natural selection where a population of candidate solutions are mutated to increase the fitness of the solution. - In his thesis, the author starts from a random solution performing several crossovers, mutations and eliminations to reach the optimized solution and shows an example for an iron ore mine.
- 24. Critical Multiplier algorithm -Chicoisne et al., 2012 - Chile - Not available commercially - It solves an LP version of the MILP and applies a rounding heuristic based on topological sorting. Then a second heuristic is applied based on local search. - The critical multipliers are break-point values from the ultimate pit parameterization that define a piecewise linear profit function. - It solves the Marvin deposit example in 12 seconds, but blocks mined in a given period may be scattered over the pit.
- 25. DeepMine® algorithm - Echeverríaet al., 2013 - Chile - Used in DeepMine®, a product of Boamine, Chile - It creates multiple possible states in which the mine might be at a particular period. Then for each of these possible states, the algorithm develops new states, and selects the path that leads to the highest NPV. - In order to guide the solution, the algorithm considers operational constraints for generating extraction zones, and follows the LG ultimate pit. - Phases are not predefined, rather emerge from the tree of states generated based on slope angles and minimum mining width required.
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- 27. Simulated annealing -Dimitrakopoulos andConsuegra, 2009 – Canada - Not available commercially - Finds a global optimum in a large discrete search space, by changing the rate of decrease in the probability of accepting worse solutions as it explores the solution space. - It takes several mine production schedules corresponding to each one of the simulated orebody models, and focus the attention to those blocks that have less than 100% probability of being mined in a particular period. These blocks will be accepted to the extent that they exceed a predetermined annealing temperature. - However, the method is computational and labor intensive, even with the current computing power.
- 28. Final Note We areliving a change of paradigm: phase design was considered previous to mine scheduling. Now, it has been shown that phases emerge from the scheduling algorithm, leaving the phase design as a post-process after the mine schedule is completed.
- 29. References - Dagdelen,K. andJohnson, T. 1986: Optimum Open Pit Mine Production Scheduling by Lagrangian Parameterization. 19th APCOM Symposium, pp. 127-142 - Tolwinski, B. and Underwood, R. 1992: An Algorithm to Estimate the Optimal Evolution of an Open Pit mine. 23rd APCOM Symposium, pp. 399-409 - Wharton, C. 2000: Add Value to Your Mine Through Improved Long Term Scheduling. Whittle North American Strategic Mine Planning Conference, Colorado - Caccetta, L. and Hill, S. 2003: An Application of Branch and Cut to Open Pit Mine Scheduling. Journal of Global Optimization 27: 349-365 - Ramazan, S. 2007: Large-Scale Production Scheduling with the Fundamental Tree Algorithm – Model, Case Study and Comparisons. Orebody Modelling and Strategic Mine Planning, pp. 121-127 - Wooller, R. 2007: Optimising multiple operating policies for exploiting complex resources – An overview of the COMET Scheduler. Orebody Modelling and Strategic Mine Planning, pp. 309-316
- 30. References (cont.) - Dimitrakopoulos,R. and Consuegra, A. 2009: Stochastic mine design optimisation based on simulated annealing: pit limits, production schedules, multiple orebody scenarios and sensitivity analysis. Mining Technology Vol 118 #2 p. 79-90 - Newman et al. 2010: A Review of Operations Research in Mine Planning Interfaces 40(3), pp. 222– 245, ©2010 INFORMS - Sattarvand, J. and Niemann-Delius 2011: A New Metaheuristic Algorithm for Long-Term Open Pit Production Planning. 35th APCOM Symposium, pp. 319-328 - Chicoisne et al. 2012: A New Algorithm for the Open-Pit Mine Production Scheduling Problem. Operations Research 60(3), pp. 517-528 - Bitanshu 2012: Open Pit Production Scheduling Applying Meta Heuristic Approach. Thesis, National Institute of Technology – India - Juarez, G. et al 2014: Open Pit Strategic Mine Planning with Automatic Phase Generation. Orebody Modelling and Strategic Mine Planning. pp. 147-154
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