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Simulation optimization of mine operations for uncertainty based short term and operational planning in open pit mines Open Access


Other title
Simulation optimization
Discrete event simulation
Mine operational planning
short term mine planning
Type of item
Degree grantor
University of Alberta
Author or creator
Upadhyay, Shiv P
Supervisor and department
Askari-Nasab, Hooman (Civil and Environmental Engineering)
Examining committee member and department
Doucette, John (Mechanical Engineering)
Mohamed, Yaser (Civil and Environmental)
Askari-Nasab, Hooman (Civil and Environmental)
Rostami, Jamal (Colorado School of Mines)
Pourrahimian, Yashar (Civil and Environmental)
Department of Civil and Environmental Engineering
Mining Engineering
Date accepted
Graduation date
2017-06:Spring 2017
Doctor of Philosophy
Degree level
The objective of this PhD thesis is to develop, implement and verify a theoretical framework to generate practical, achievable and robust uncertainty based short term plans meeting operational objectives and short term planning targets. This thesis intends to develop a mathematical optimization model as mine operational optimization tool (MOOT), a discrete event simulation model of mine operations and in turn an integrated simulation optimization model through an interaction mechanism that links the simulation model with MOOT as a multi-stage dispatching system for dynamic shovel and truck allocation optimization. The goal is to develop a simulation optimization framework/tool which integrates simulation with MOOT for dynamic operational decision making based on a feedback loop. This framework/tool must capture the operational uncertainty, achieve operational objectives of production and grade blend requirements, and project practical uncertainty based short term plans with a higher confidence on the deliverability of operational targets and key performance indicators (KPIs) of the system. The MOOT is developed as a Mixed Integer Linear Goal Programming (MILGP) model in this thesis. The MILGP model is a multi-period optimization tool to optimally allocate shovels to available faces from strategic schedule and determine production targets and number of truck trips from each shovel so that operational objectives of maximum production and plant requirement of target tonnage and grade blend can be achieved. We showed the applicability of MOOT as dynamic decision making tool in real mine operations, and in parallel with a simulation model for dynamic shovel and truck allocation decision making. The MOOT is analogues to a planner in real mine operations who provides shovel and truck allocation decisions based on operational objectives and schedule; and updates short term plans based on current system state. This thesis also presents the development of a discrete event simulation model of mine operations, including loading, spotting, dumping, queuing, hauling, plant crushers and equipment failures. Since truck haulage is a critical and limiting component in mine operation systems, a microscopic modeling approach of truck haulage is presented in this thesis. This approach captures the truck interactions and variable speeds along the haul road network of the mine based on gradients and rolling resistances of roads, and rimpull curve characteristics of the trucks, so that practical deliverables can be estimated. This thesis finally presents the integration, implementation and verification of the simulation optimization model with an iron ore mine case study. The 11th year strategic schedule and the designed haul road network of the mine, along with operational process distribution times are used as inputs to the simulation optimization model. The detailed verification and implementation through scenario analysis showed the strength of the model and the approach in capturing the realistic operational behavior, along with practical operational decision making leading to the development of confident, achievable and robust short term plans. The implementation also shows the strength of the model for proactive decision making by analyzing several desired scenarios for haulage planning and grade blending strategies. The proactive decision making is made easy by this approach due to the increased confidence in the derived plans through operational executions in simulation. The main contributions of this thesis to the research community in mining applications are: i) a novel simulation optimization approach for uncertainty based short term planning that captures the deliverability of production and grade blend targets during practical operational executions, and accounts for the mine operational details, ii) The MOOT developed as a mixed integer linear goal programming model for dynamic operational decision making with its applicability in real mine operations and simulation for shovel and truck allocation decisions, iii) a discrete event simulation model of mine operations that accounts for realistic truck travel behavior and interactions on haul road network of the mine, and iv) a proactive decision making capability because of the higher confidence in the achievability and practicality of the short term plans developed.
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
Citation for previous publication
Upadhyay, S. P., & Askari-Nasab, H. (2016). Truck-shovel allocation optimization: a goal programming approach. Mining Technology, 125 (2), 82-92

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