A mine waste dump progression plan can provide a clear outlook of the mining operation. To produce such an output requires detailed placement schedule of the mined material, including the volume (or tonnage) and the allocated dumping location. However, current practise mainly focuses on the ore production, over-simplifying the waste material scheduling. As a result, a rock dump is often treated as a single point in long-term planning, making it difficult to predict the waste dump progression pattern over the life-of-mine. Without such a guidance, it is almost impossible to carry out progressive rehabilitation on the waste rock dumps. The lack of dumping schedule could cause delay in development construction, ie tailing storage facility and run-of-mine pad. Other downstream effect due to the over-simplification is inaccurate estimation of required truck hours, which could have huge financial impact on the operation.In this paper, mixed integer programming (MIP) models of different objective functions, ie maximise truck productivity by minimising the overall haulage distance, minimise required truck deviation between adjacent years, and a hybrid between the two objectives, are utilised to generate the long-term optimum rock placement schedules under the criteria of satisfying site specific conditions. All three MIP models are implemented in a large scale open pit mine. The numerical solutions from the models forms three different rock placement schedules, based on which, the yearly truck requirements are easily calculated and compared. The graphical results show the three corresponding waste dump progression patterns over the life-of-mine, providing the optimised long-term forecast of the operation.CITATION:Li, Y, Topal, E and Ramazan, S, 2014. Optimising the long-term mine waste dump progression and truck hour schedule in a large-scale open pit mine using mixed integer programming, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 335-344 (The Australasian Institute of Mining and Metallurgy: Melbourne).