366
in Table 1 record the percentage of total target that has been fulfilled by the end of the shift by
the LP and Flow Planner, respectively (note that the total target given to both algorithms is high
enough that is not completely achievable by the end of the shift using the given capacity). From
the table, only 61.8% of the target has been met by the LP in the first instance, whereas Flow
Planner achieves 93.4%. The average achievement over all instances is only 62.70% by the LP
while the average of achievement by Flow Planner is 95.58%. Note that both algorithms are very
fast, solving realistic scenarios in seconds, and therefore we did not represent the solution times.
5 CONCLUDING REMARKS
We presented the Flow Planner system to generate haulage allocation plans from production
targets. This is the first component of plan-driven dispatching systems in open pit mines.
In contrast to existing techniques, the proposed Flow Planner optimises production for the
entire shift (or similar period) rather than only instantaneous production, and takes into
account upcoming events, such as maintenance and blasts, as well as changes in equipment
availability or performance throughout the shift. This allows for better decision making
across the full period, reducing the need for an operator to closely monitor and update the
plan every time the state of the mine changes. Our Flow Planner additionally considers the
flow of empty trucks and enables feedback control on material quantity targets.
The Flow Planner output was described after running it on a conceptually generated, yet
realistic, mining scenario. The Flow Planner was compared to an existing plan-driven technique, where five case studies were considered. Our results indicated that an operator would
have had to perform the updating and re-running process 7–16 times to be able to cover the
entire shift, whereas Flow Planner generated the plan by a single run. The results also showed
that the performance of the plan—measured as production target completion—generated
by Flow Planner is significantly higher than that of LP; 95.58% of targets were achieved by
Flow Planner, whereas the achievement was only 62.70% by the LP.
Future work will consider unexpected disruptions using stochastic and/or robust techniques so that Flow Planner can better prioritise material movements, rather than relying on
manual tuning.
REFERENCES
Alarie, S. and M. Gamache (2002). “Overview of solution strategies used in truck dispatching systems for
open pit mines.” International Journal of Surface Mining, Reclamation and Environment 16(1): 59–76.
Bonates, E. and Y. Lizotte (1988). “A combined approach to solve truck dispatching problems.” Computer
Application in the Mineral Industry, Fystas, Collins & Singhal (eds), Balkema, Rotterdam: 403–412.
Burt, C.N. and L. Caccetta (2007). “Match factor for heterogeneous truck and loader fleets.” International journal of mining, reclamation and environment 21(4): 262–270.
Munirathinam, M. and J.C. Yingling (1994). “A review of computer-based truck dispatching strategies
for surface mining operations.” International Journal of Surface Mining and Reclamation 8(1): 1–15.
Soumis, F., J. Ethier and J. Elbrond (1989). “Evaluation of the new truck dispatching in the Mount
Wright Mine.” Application of Computers and Operations Research in the Mineral Industry: 674–682.
Subtil, R.F., D.M. Silva and J.C. Alves (2011). A practical approach to truck dispatch for open pit mines.
35Th APCOM symposium.
Ta, C.H., J.V. Kresta, J.F. Forbes and H.J. Marquez (2005). “A stochastic optimization approach to mine
truck allocation.” International journal of surface mining, reclamation and environment 19(3): 162–175.
Upadhyay, S. and H. Askari-Nasab (2016). “Truck-shovel allocation optimisation: a goal programming
approach.” Mining Technology 125(2): 82–92.
White, J. and J. Olson (1986). “Computer-based dispatching in mines with concurrent operating objectives.” Min. Eng. (Littleton, Colo.); (United States) 38(11).
White, J.W., J. Olson and S. Vohnout (1993). “On improving truck/shovel productivity in open pit mines.”
CIM bulletin 86(973): 43–49.
in Table 1 record the percentage of total target that has been fulfilled by the end of the shift by
the LP and Flow Planner, respectively (note that the total target given to both algorithms is high
enough that is not completely achievable by the end of the shift using the given capacity). From
the table, only 61.8% of the target has been met by the LP in the first instance, whereas Flow
Planner achieves 93.4%. The average achievement over all instances is only 62.70% by the LP
while the average of achievement by Flow Planner is 95.58%. Note that both algorithms are very
fast, solving realistic scenarios in seconds, and therefore we did not represent the solution times.
5 CONCLUDING REMARKS
We presented the Flow Planner system to generate haulage allocation plans from production
targets. This is the first component of plan-driven dispatching systems in open pit mines.
In contrast to existing techniques, the proposed Flow Planner optimises production for the
entire shift (or similar period) rather than only instantaneous production, and takes into
account upcoming events, such as maintenance and blasts, as well as changes in equipment
availability or performance throughout the shift. This allows for better decision making
across the full period, reducing the need for an operator to closely monitor and update the
plan every time the state of the mine changes. Our Flow Planner additionally considers the
flow of empty trucks and enables feedback control on material quantity targets.
The Flow Planner output was described after running it on a conceptually generated, yet
realistic, mining scenario. The Flow Planner was compared to an existing plan-driven technique, where five case studies were considered. Our results indicated that an operator would
have had to perform the updating and re-running process 7–16 times to be able to cover the
entire shift, whereas Flow Planner generated the plan by a single run. The results also showed
that the performance of the plan—measured as production target completion—generated
by Flow Planner is significantly higher than that of LP; 95.58% of targets were achieved by
Flow Planner, whereas the achievement was only 62.70% by the LP.
Future work will consider unexpected disruptions using stochastic and/or robust techniques so that Flow Planner can better prioritise material movements, rather than relying on
manual tuning.
REFERENCES
Alarie, S. and M. Gamache (2002). “Overview of solution strategies used in truck dispatching systems for
open pit mines.” International Journal of Surface Mining, Reclamation and Environment 16(1): 59–76.
Bonates, E. and Y. Lizotte (1988). “A combined approach to solve truck dispatching problems.” Computer
Application in the Mineral Industry, Fystas, Collins & Singhal (eds), Balkema, Rotterdam: 403–412.
Burt, C.N. and L. Caccetta (2007). “Match factor for heterogeneous truck and loader fleets.” International journal of mining, reclamation and environment 21(4): 262–270.
Munirathinam, M. and J.C. Yingling (1994). “A review of computer-based truck dispatching strategies
for surface mining operations.” International Journal of Surface Mining and Reclamation 8(1): 1–15.
Soumis, F., J. Ethier and J. Elbrond (1989). “Evaluation of the new truck dispatching in the Mount
Wright Mine.” Application of Computers and Operations Research in the Mineral Industry: 674–682.
Subtil, R.F., D.M. Silva and J.C. Alves (2011). A practical approach to truck dispatch for open pit mines.
35Th APCOM symposium.
Ta, C.H., J.V. Kresta, J.F. Forbes and H.J. Marquez (2005). “A stochastic optimization approach to mine
truck allocation.” International journal of surface mining, reclamation and environment 19(3): 162–175.
Upadhyay, S. and H. Askari-Nasab (2016). “Truck-shovel allocation optimisation: a goal programming
approach.” Mining Technology 125(2): 82–92.
White, J. and J. Olson (1986). “Computer-based dispatching in mines with concurrent operating objectives.” Min. Eng. (Littleton, Colo.); (United States) 38(11).
White, J.W., J. Olson and S. Vohnout (1993). “On improving truck/shovel productivity in open pit mines.”
CIM bulletin 86(973): 43–49.