Whittle Training
Module Learning Guide
Production Scheduling and Optimisation
Pit optimisation using Whittle 4X
Jeff Whittle brought pit optimisation of age with the first fully implemented LerchsGrossmann algorithm. The graph-maximisation approach has a number of advantages over the LP formulation illustrated above. The primary advantage is that the pit optimisation problem can be coded in any language, such as Fortran in the case of early Whittle products. As such, it is not dependent on LP solution algorithms which can vary dramatically in solution speed given their implementation and application. No doubt, Whittle’s early implementations were far faster than solving the LP equivalent using the available Simplex algorithms. Currently, 4X is one of several competing pit optimisation products on the market but is the most widely used and recognised. The following sections are not designed as a Whittle tutorial. Rather, we introduce 4X as an excellent example of the methodology of pushback generation as a means of illustrating the optimisation process for a very large example. While we could expand upon the LP-based example of the previous section, doing so would require a full version of AMPL/Cplex and far more depth in programming than is required to understand the topic. 4X follows the same basic approach as with the LP implementation: pushback generation using a series of price increments. This is supplemented by comprehensive reporting and graphing of results coupled with scheduling algorithms aimed at providing guidance in the selection of pushbacks from among the set of pits generated by applying the price factors. It should be noted that a certain amount of educated trial-and-error is required in using 4X and that the scheduling algorithms are not provably optimal but are heuristic which in some cases provide very good, if not optimal, solutions. Still, as we will see, there is no direct approach to selecting a set of optimised pit limits as pushbacks and the
Production Scheduling and Optimisation
Pit optimisation using Whittle 4X
Jeff Whittle brought pit optimisation of age with the first fully implemented LerchsGrossmann algorithm. The graph-maximisation approach has a number of advantages over the LP formulation illustrated above. The primary advantage is that the pit optimisation problem can be coded in any language, such as Fortran in the case of early Whittle products. As such, it is not dependent on LP solution algorithms which can vary dramatically in solution speed given their implementation and application. No doubt, Whittle’s early implementations were far faster than solving the LP equivalent using the available Simplex algorithms. Currently, 4X is one of several competing pit optimisation products on the market but is the most widely used and recognised. The following sections are not designed as a Whittle tutorial. Rather, we introduce 4X as an excellent example of the methodology of pushback generation as a means of illustrating the optimisation process for a very large example. While we could expand upon the LP-based example of the previous section, doing so would require a full version of AMPL/Cplex and far more depth in programming than is required to understand the topic. 4X follows the same basic approach as with the LP implementation: pushback generation using a series of price increments. This is supplemented by comprehensive reporting and graphing of results coupled with scheduling algorithms aimed at providing guidance in the selection of pushbacks from among the set of pits generated by applying the price factors. It should be noted that a certain amount of educated trial-and-error is required in using 4X and that the scheduling algorithms are not provably optimal but are heuristic which in some cases provide very good, if not optimal, solutions. Still, as we will see, there is no direct approach to selecting a set of optimised pit limits as pushbacks and the