Les métaheuristiques sont très utilisées dans le domaine de l'optimisation discrète. Elles permettent d’obtenir une solution de bonne qualité en un temps raisonnable, pour des problèmes qui sont de grande taille, complexes, et difficiles à résoudre. Souvent, les métaheuristiques ont beaucoup de paramètres que l’utilisateur doit ajuster manuellement pour un problème donné. L'objectif d'une métaheuristique adaptative est de permettre l'ajustement automatique de certains paramètres par la méthode, en se basant sur l’instance à résoudre. La métaheuristique adaptative, en utilisant les connaissances préalables dans la compréhension du problème, des notions de l'apprentissage machine et des domaines associés, crée une méthode plus générale et automatique pour résoudre des problèmes. L’optimisation globale des complexes miniers vise à établir les mouvements des matériaux dans les mines et les flux de traitement afin de maximiser la valeur économique du système. Souvent, en raison du grand nombre de variables entières dans le modèle, de la présence de contraintes complexes et de contraintes non-linéaires, il devient prohibitif de résoudre ces modèles en utilisant les optimiseurs disponibles dans l’industrie. Par conséquent, les métaheuristiques sont souvent utilisées pour l’optimisation de complexes miniers. Ce mémoire améliore un procédé de recuit simulé développé par Goodfellow & Dimitrakopoulos (2016) pour l’optimisation stochastique des complexes miniers stochastiques. La méthode développée par les auteurs nécessite beaucoup de paramètres pour fonctionner. Un de ceux-ci est de savoir comment la méthode de recuit simulé cherche dans le voisinage local de solutions. Ce mémoire implémente une méthode adaptative de recherche dans le voisinage pour améliorer la qualité d'une solution. Les résultats numériques montrent une augmentation jusqu'à 10% de la valeur de la fonction économique. / Metaheuristics are a useful tool within the field of discrete optimization that allow for large, complex, and difficult optimization problems to achieve a solution with a good quality in a reasonable amount of time. Often metaheuristics have many parameters that require a user to manually define and tune for a given problem. An adaptive metaheuristic aims to remove some parameters from being tuned or defined by the end user by allowing the method to specify and/or adapt a parameter or set of parameters based on the problem. The adaptive metaheuristic, using advancements in understanding of the problem being solved, machine learning, and related fields, aims to provide this more generalized and automatic toolkit for solving problems. Global optimization of mining complexes aims to schedule material movement in mines and processing streams to maximize the economic value of the system. Often due to the large number of integer variables within the model, complicated constraints, and non-linear constraints, it becomes prohibitive to solve these models using commercially available optimizers. Therefore, metaheuristics are often employed in solving mining complexes. This thesis builds upon a simulated annealing method developed by Goodfellow & Dimitrakopoulos (2016) to optimize the stochastic global mining complex. The method outlined by the authors requires many parameters to be defined to operate. One of these is how the simulated annealing algorithm searches the local neighborhood of solutions. This thesis illustrates and implements an adaptive way of searching the neighborhood for increasing the quality of a solution. Numerical results show up to a 10% increase in objective function value.

Mining operations exploit mineral deposits, processing a portion of the extracted material to produce salable products. The concentration of valuable commodities within these deposits, or the grade, is heterogeneous. Not all material has sufficiently high grades to economically justify processing. Cut-off grade is the lowest grade at which material is considered ore and is processed to create a concentrated commodity product. The choice of cut-off grade at a mining project can be varied over time and dramatically impacts both the operation of the mine and the economics of the project. The majority of literature and the accepted industry practices focus on optimizing cut-off grade under known commodity prices. However, most mining operations sell their products into highly competitive global markets, which exhibit volatile commodity prices. Making planning decisions assuming that a given commodity price prediction is accurate can lead to sub-optimal cut-off grade strategies and inaccurate valuations. Some academic investigations have been conducted to optimize cut-off grade under stochastic or uncertain price conditions. These works made large simplifications in order to facilitate the computation of a solution. These simplifications mean that detailed mine planning data cannot be used and the complexities involved in many real world projects cannot be considered. A new method for optimizing cut-off grade under stochastic or uncertain prices is outlined and demonstrated. The model presented makes use of theory from the field of Real Options and is designed to incorporate real mine planning data. The model introduces two key innovations. The first is the method in which it handles the cut-off grade determination. The second innovation is the use of a stochastic price model of the entire futures curve and not simply a stocastic spot price model. The model is applied to two cases. The first uses public data from a National Instrument 43-101 report. The second case uses highly detailed, confidential data, provided by a mining company from one of their operating mines. / Thesis (Master, Mining Engineering) -- Queen's University, 2012-04-30 22:36:51.257