This thesis presents advanced algorithms for realtime detection of dragline duty, the quantification of its causes and the combined optimisation of dragline motion to minimise cycle time and duty. Draglines are large, powerful, rotating, multibody systems that operate in a similar manner to cranes and certain pick and place robots. Duty is an estimate of fatigue damage on the dragline boom caused by cyclic stresses that are associated with the repetitive dig and dump operation. Neither realtime detection of duty nor the quantification of its causes were previously available. In addition, no previous researchers have optimised the dynamic motion of mining equipment to achieve the combined maximisation of productivity and minimisation of maintenance measures. The advanced duty detection system was developed to improve feedback to dragline operators. The algorithms that were developed are based on the mechanics of dragline motion and fatigue. In particular, fatigue cycles in measured stress are identified at the earliest possible time, based on a novel proof and modification to the rainflow cycle counting algorithm. The contributions of specific causes to each individual stress range are quantified based on the mechanics of operator dependent control and dragline dynamics. In this manner, specific causes of duty are measured. The algorithms confirmed the significant contribution from operator dependent factors and identified the major causes, attributing 28% of the total duty to out-of-plane bucket motion and 15% to dynamic vibration. Further improvements to dragline performance required the development of a dragline dynamic model for offline testing and optimisation. A complete, condensed set of equations for a four-degree-of-freedom nonlinear coupled model of a dragline was derived using Lagrange’s method, allowing direct insight into dragline behaviour not available from previous research. The model was used to investigate the relationship between motor power, operator behaviour, bucket trajectory, productivity and duty during the swing and return phases of operation. Significant potential for increasing productivity and reducing duty was demonstrated. The advanced duty detection system and the dragline model were validated with field measured data, video footage, alternative modelling and expert review. Realtime and end-of-cycle feedback was simulated over many cycles of measured data. Experts from industry and research were consulted to verify the causes of duty based on detailed measured data analysis. The forces, stresses and out-of-plane angle predicted by the dragline model were closely compared with measured data over various indicative cycles. The dragline model was also validated against an alternative model constructed in ADAMS. The development of the dragline model enabled model-based numerical optimisation. Significant nonlinearities in the model and the constraints necessitated the use of the Lagrange multiplier method. The bucket trajectory during the swing and return phase was directly optimised. In order to minimise cycle time and duty, a penalty for duty incurred was added to the cycle time, effectively maximising long-term productivity. For a slew torque optimisation scenario using measured rope lengths, the numerical optimisation performance was shown to be 10-30% better than manual optimisation and 50-60% better than the operator performance. This thesis outlines several significant contributions to improving dragline performance. Underpinning the advanced duty detection system are three significant contributions to fatigue cycle counting algorithms: a proof of the equivalence of two pre-existing algorithms; a new algorithm that enables realtime detection of duty; and an algorithm that can attribute duty to specific causes. These novel feedback tools can provide realtime operator feedback and identify the causes of excess duty and when it was incurred. A complete and condensed set of equations for the four-degree-of-freedom model enabled, for the first time, the optimisation of dragline operation to concurrently reduce duty and increase productivity. The models and feedback algorithms were validated with field measured data. Future work could include installation and extension of the advanced duty detection system. In addition, further modelling and optimisation research could focus on improving the heuristics used for bucket trajectory control, realtime determination of optimum bucket trajectory and testing proposed dragline modifications.