Reliability based multi-objective strategic and operational mine planning through parallel meta-heuristics

采矿业是一个周期性的行业，经历过价格低迷的时期。在加拿大，煤矿也开采得更深，这增加了运营成本。这些特点危及一些采矿作业的未来。暂停或终止运营导致失业率上升、投资损失和对当地社区经济的负面影响。因此，为了继续经营下去，采矿作业应该以一种挑战负面气氛的方式得到改善。计算机科学和运筹学领域的进步对提高采矿作业的生产率、效率和盈利能力具有重要的潜力。与此同时，矿山规划软件行业也是竞争激烈的行业，寻求在其各种产品中添加新功能，以改善其产品组合。在这一范围内，拟议的研究旨在开发新的优化框架，以增加地下采矿作业的效用。研究将从顺序优化推进到同步优化，以便同时解决截止品位、采场布局和排序问题，以生成战略和操作计划。问题的规模将不可避免地扩大。因此，本研究将研究各种集群、并行和分布式计算技术来处理大小问题。为了解决这一问题，将开发一种结合模拟退火法、遗传算法和禁忌搜索各种性质的混合元启发式算法。由于战术或地雷的特殊原因，除了使净现值最大化外，还可能有不同的目标，如采收率最大化、稀释最小化或产能效用最大化。将问题描述扩展到考虑多目标特性，并产生非支配解。因此，决策者可以选择适合她/他的偏好的解决方案。用于地下矿山规划的品位数据稀少，资金投入取决于一系列未知的未来事件。因此，地下矿山规划包含很大程度的不确定性。因此，研究还将涉及基于可靠性的优化和风险量化。

Mining is a cyclical business and experiences with periods of low prices. In Canada, mines also go deeper, which increases operation costs. These characteristics jeopardize the future of some mining operations. The suspension or termination of operations leads to increasing unemployment rate, loss of the investment and negative impact on the economies of local communities. Therefore, to stay in business, mining operations should be improved in such a way as to challenge the negative atmosphere. The advancements in the fields of computer science and operation research have an important potential to increase productivity, efficiency and profitability of mining operations. At the same time, mine planning software industry is also competitive and seeks to add new features to their variety of products to improve their product portfolio. In this scope, the proposed research aims to develop new optimization frameworks to increase the utility obtained from underground mining operations. The research will move forward from sequential to simultaneous optimization such that cut-off grade, stope layout and sequencing problems are concurrently solved to generate strategic and operational plans. The size of problem will inevitably grow. Therefore, the research will investigate various clustering, parallel and distributed computing techniques to deal with size issue. To solve the problem, a hybrid meta-heuristics using various properties of simulated annealing, genetic algorithms and tabu search will be developed. In addition to maximization of net present value, due to tactical or mine specific reasons, there could be different objectives such as recovery maximization, dilution minimization or capacity utility maximization. The problem formulation will be extended to consider multi-objective characteristics and non-dominated solutions will be generated. Hence, the decision-maker can pick up a solution suiting her/his preferences. Grade data used in underground mine planning are sparse and financial inputs depend on a series of unknown future events. Consequently, underground mine planning comprises significant degree of uncertainty. Therefore, the research will also address reliability-based optimization and risk quantification.