MULSOPS: Multivariate Optimized Pit Shells Simulator for Tactical Mine Planning

Production planning, scheduling and allocation of resources in large-scale surface mining operations present a great challenge to mine planning engineers. Ore and waste extraction plans must be executed to achieve tactical objectives using appropriate tools. Many production planning and scheduling and resource allocation methods are based on trial and error, crisis management or subjective judgements with no detailed economic basis or mathematical rigour. In addition, these methods do not consider the random processes governing critical development and production variables. In this study, the authors develop a multivariate pit shell simulator, MULSOPS, which addresses these problems. Rigorous geometric formulations of the ellipsoidal approximations of the pit shells geometry, their expansions and sequential interactions are modeled to mimic material displacement dynamics in an open pit operation. Stochastic and numerical modeling techniques are used to provide solutions to the time-dependent geometric models in random multivariate states. Under different production and economic paradigms, the geometric models are simulated to yield the source and characteristics of appropriate cuts. Combined production from successive exposed cuts provides periodic targets for tactical planning. Variance simulation is also used to provide analysts with sensitive stochastic variables for input data definition and tight production target tolerance. A numerical example is used to illustrate the use of MULSOPS for tactical planning in a typical open pit operation.Production planning, scheduling and allocation of resources in large-scale surface mining operations present a great challenge to mine planning engineers. Ore and waste extraction plans must be executed to achieve tactical objectives using appropriate tools. Many production planning and scheduling and resource allocation methods are based on trial and error, crisis management or subjective judgements with no detailed economic basis or mathematical rigour. In addition, these methods do not consider the random processes governing critical development and production variables. In this study, the authors develop a multivariate pit shell simulator, MULSOPS, which addresses these problems. Rigorous geometric formulations of the ellipsoidal approximations of the pit shells geometry, their expansions and sequential interactions are modeled to mimic material displacement dynamics in an open pit operation. Stochastic and numerical modeling techniques are used to provide solutions to the time-dependent geometric models in random multivariate states. Under different production and economic paradigms, the geometric models are simulated to yield the source and characteristics of appropriate cuts. Combined production from successive exposed cuts provides periodic targets for tactical planning. Variance simulation is also used to provide analysts with sensitive stochastic variables for input data definition and tight production target tolerance. A numerical example is used to illustrate the use of MULSOPS for tactical planning in a typical open pit operation