We study the capacity planning and allocation decisions for multiple heterogeneous resources, considering potential demand scenarios, where each demand requests a subset of the available resource types simultaneously ...
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We study the capacity planning and allocation decisions for multiple heterogeneous resources, considering potential demand scenarios, where each demand requests a subset of the available resource types simultaneously at a specified time, location, and duration (smRmD). We model this problem as a two-stage stochasticinteger program and consider two variants for the objective function: (a) maximize the expected reward of demands met over all scenarios, subject to a budget B for resources, and (b) maximize the expected reward of demands met over all scenarios minus the cost of resources. Contributions of this work include (i) a thorough complexity analysis of smRmD and its variants, (ii) analysis of structural properties, (iii) development of various approximation algorithms using the unique structural properties of smRmD and its variants, and (iv) an extensive computational study to explore the ease with which exact and approximate solutions may be found.
In the commonly used underground mine planning framework, mine design is first established and is the main input for the subsequent long-term mine production scheduling optimization. This sequential optimization appro...
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In the commonly used underground mine planning framework, mine design is first established and is the main input for the subsequent long-term mine production scheduling optimization. This sequential optimization approach cannot, therefore, capture the synergies between the involved planning steps, generating solutions that depart substantially from a global optimum. In addition, traditional underground mine planning methods for stope design and life-of-mine production scheduling are deterministic and are based on a single estimated ore -body model. As a result, the uncertainty and variability in grades and material types are not incorporated into the optimization process, resulting in designs that misrepresent all high-, medium-and low-grade stoping volumes and production schedules with misleading forecasts. A two-stage stochasticinteger program (SIP) for integrated optimization of stope and development network designs and an underground mine production scheduling are proposed for the sublevel open stoping mining method under grade uncertainty and variability, quantified by a set of geostatistical simulations of the mineral deposit considered. Assuming a mine is accessed through a shaft, the model defines a schedule of levels and stopes, which aims to maximize the discounted revenues, minimize development costs, and manage the risk of not meeting production targets, while satisfying geotechnical con-straints. The practical aspects of the proposed method are presented through an application at an underground gold mine. A comparison with the stepwise framework, where the stope design is input to a subsequent opti-mization of the production schedule, shows that the proposed approach provides a physically different design and production schedule with an 11% higher net present value (NPV) and a life-of-mine that is two years shorter, affirming the advantages of the integrated optimization process.
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