New Long-term Production Scheduling Methodologies for Open-pit Mines

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Copyright: Samavati, Mehran
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Abstract
The open pit mine production scheduling problem (OPMPSP) is a well-known combinatorial optimization problem that plays a vital role in surface mining, as the economic viability of a mining project is highly dependent on careful long-term planning. This problem consists of scheduling the extraction of a mineral deposit that is broken into a number of smaller segments, or blocks, such that the profit of the operation is maximized. The basic variant of this problem belongs to a class of the precedence constrained knapsack problem, in which a decision maker faces a horizon of several periods. In each period, the capacity limit imposes a hard constraint. The main difficulty in such problems is the size of the problem and constraints due to operational complexities. The size of the problem is linked to the number of blocks in the mineral deposit, as well as the number of years included in the planning horizon of the project. Mineral deposits can involve thousands of blocks, implying that the problem is significantly larger than most other scheduling problems. Despite a wide literature on the basic variant of this problem, the development of more powerful algorithms that can lead to smaller gaps from optimality is an ongoing area of research. A more practical problem includes minimum mining limits and ore processing requirements. Those are important operational considerations that are principally associated with consistent and acceptable equipment utilisation between scheduling periods. This variant of the problem has got a very little attention in the literature. The basic OPMPSP defined in the 1960s was based on a traditional extraction system known as truck-and-shovel (T&S). Since then, however, some equipment and facilities have been updated in open pit mining. The In-pit Crusher Conveyor (IPCC) systems offer an alternative to traditional T&S systems by replacing trucks with conveyors. This replacement in an open pit operation introduces a number of additional constraints that are not required in T&S systems. These constraints mainly relate to the geometric need for a fixed pit access location to accommodate a conveyor network, which thus restricts possible production sequencing in the deposit. These additional constraints add further complexity to an alreadycomplex large scale OPMPSP. While OPMPSP has been extensively studied, the focus however has mainly been on T&S systems. In contrast, when IPCC systems are used, OPMPSP is very much unresolved; in most cases, industry still relies on the judgement or best estimate of experienced personnel. This thesis first proposes a new scheduling methodology to efficiently solve the basic OPMPSP. As part of this methodology, a new theorem is developed and analysed. This methodology is tested on a set of benchmark instances that includes real-world mine deposit scenarios, and shows better performance than the best known heuristics in the literature. Interestingly, the proposed methodology improves the best known solutions for the majority of instances. This thesis also proposes an approach to solve OPMPSP with minimum resource requirements, by using a novel metaheuristic technique known as local branching. To accelerate the search process, it is combined with a new adaptive branching scheme. A new heuristic is also developed to quickly generate an initial feasible solution. Despite consideration of minimum requirements being seldom taken into account in the literature, this method yields near-optimal solutions for a series of data sets that this thesis conceptually generates. To judge the performance of the methodology, the results are compared to those of techniques from the literature, as well as to those obtained by commercial constraint programming solvers. The results indicate that the developed algorithm is superior. Finally, this thesis addresses OPMPSP under IPCC systems. A mathematical model is first developed to comprehensively represent all aspects in the problem, as the existing mathematical models are not useful for the new system. An efficient solution algorithm is proposed to solve the problem. This is the first algorithm that considers all aspects in the IPCC mining system, and thus can be applicable for mining companies that desire to use this system.
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Author(s)
Samavati, Mehran
Supervisor(s)
Essam, Daryl
Sarker, Ruhul
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Publication Year
2017
Resource Type
Thesis
Degree Type
PhD Doctorate
UNSW Faculty
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