Erkan Topal

A new MIP model for mine equipment scheduling by minimizing maintenance cost

Mining investment has been recognized as capital intensive due mainly to the cost of large equipment. Equipment capital costs for a given operation are usually within the order of hundreds of million dollars but may reach to billion dollars for large companies operating multiple mines. Such large investments require the optimum usage of equipment in a manner that the operating costs are minimized and the utilization of equipment is maximized through optimal scheduling. This optimum usage is required to ensure that the business remains sustainable and financially stable. Most mining operations utilize trucks to haul the mined material. Maintenance is one of the major operating cost items for these fleets as it can reach approximately one hundred million dollars yearly. There is no method or application in the literature that optimizes the utilization for truck fleet over the life of mine. A new approach based on mixed integer programming (MIP) techniques is used for annually scheduling a fixed fleet of mining trucks in a given operation, over a multi-year time horizon to minimize maintenance cost. The model uses the truck age (total hours of usage), maintenance cost and required operating hours to achieve annual production targets to produce an optimum truck schedule. While this paper focuses on scheduling trucks for mining operation, concept can be used in most businesses using equipment with significant maintenance costs. A case study for a large scale gold mine showed an annual discounted (10% rate) maintenance cost saving of over

Implementing a Production Schedule at LKAB's Kiruna Mine

2M and more than 16% (

Review article: A review of soft computing technology applications in several mining problems

21M) of overall maintenance cost reduction over 10 years of mine life, compared with the spreadsheet based approach used currently at the operation.

Flexible open-pit mine design under uncertainty

LKABs Kiruna Mine, located in northern Sweden, produces about 24 million tons of iron ore yearly using an underground mining method known as sublevel caving. To efficiently run the mills that process the iron ore, the mine must deliver planned quantities of three ore types. We used mixed-integer programming to schedule Kirunas operations, specifically, which production blocks to mine and when to mine them to minimize deviations from monthly planned production quantities while adhering to operational restrictions. These production schedules save costs compared to schedules produced manually by meeting desired production quantities more closely and reducing employee time spent on preparing schedules.

Evaluation of a mining project using Discounted Cash Flow analysis, Decision Tree analysis, Monte Carlo Simulation and Real Options using an example

Soft computing (SC) is a field of computer science that resembles the processes of the human brain. While conventional hard computing is run based on crisp values and binary numbers, SC uses soft values and fuzzy sets. In fact, SC technology is capable of address imprecision and uncertainty. The application of SC techniques in the mining industry is fairly extensive and covers a considerable number of applications. This paper provides a comprehensive overview of the published work on SC applications in different mining areas. A brief introduction to mining and the general field of SC applications are presented in the first section of the paper. The second section comprises four review chapters. Mining method selection, equipment selection problems and their applications in SC technologies are presented in chapters one and two. Chapter three discusses rock mechanics-related subjects and some of representative SC applications in this field. The last chapter presents rock blasting related SC applications that include blast design and hazards. The final section of the paper comments on the use of SC applications in several mining problems and possible future applications of advanced SC technologies.

Dynamic Short term production scheduling and machine allocation in underground mining using mathematical programming

The risk associated with a mining project comes from the uncertainties involved in the industry. Mining companies endeavouring to maximize their return for shareholders make important strategic decisions which take years or even decades to “play out”. However, many mining companies feel comfortable with point estimates of all project parameters but realize that no parameter value is known with certainty. A model that incorporates uncertainties and is able to adapt will help deliver a design with a better riskreturn profile. In this paper, a new methodology is developed in order to have a design that is flexible and able to adapt with change. Following recent research on decision making methods in mine planning, this paper develops a mixed integer programming model that determines the optimal design for simulated stochastic parameters. The paper shows how to incorporate optionality (flexibility) in relation to mine, stockpile, plant and capacity constraint options. Obtained results are promising and are helping decision makers to think in terms of value, risk and frequency of execution.

Strategic mine planning model using network flow model and real case application

Investments in the mining and minerals industry are considered to be risky. The major challenge of project evaluation is how to deal with the uncertainty involved in capital investment. Discounted Cash Flow (DCF) methods, Decision Trees (DT), Monte Carlo Simulation (MCS) and Real Options (RO) are commonly used for evaluating mining projects. This paper briefly reviews the previous studies, outlines and summarises above four methods. Subsequently it employs these methods to evaluate a mining project where the decision whether or not to open the mine is considered. Pros and cons of investigated methods are discussed in the final section.

Strategies to assist in obtaining an optimal solution for an underground mine planning problem using Mixed Integer Programming

Abstract Maximising value is the main objective when developing long term mine production schedules. These results provide input for the development of a short term schedule that aims to meet process plant feed requirements so as to produce a quality saleable product. This paper reviews previous work on optimised short- and long term production scheduling and real time fleet management systems. A new dynamic mathematical model using mixed integer programming is proposed to optimise short term production scheduling and machine allocation for application in sublevel stoping operations. The objective of the model is to minimise deviation from targeted metal production. The dynamic nature of the model not only optimises the shift based schedule but also allows rapid equipment reassignment to take place as underground operating conditions change. Optimal results are generated in less than 1 min when trialled on a conceptual sublevel stoping dataset.

Waste rock dumping optimisation using mixed integer programming (MIP)

Strategic mine planning is one of the key factors in the successful survival of large scale mining companies in the long term, and deals with billions of dollars in magnitude. It provides answers in regards to how much capital should be invested, in what resources and when, and finally what type of production strategies should be followed in terms of capacity and product specifications. Existing tools fail to provide optimum solutions to these problems, mainly due to their large scale nature and inherent technical complexities. This article presents a network linear programming (LP) model to efficiently optimise strategic planning and production scheduling by maximising net present value (NPV). The model is applied to optimise the strategic schedule over a 50-year life span for a large mining district in Western Australia which contains many mines with more than 100 pits and 13 plants.

New approach for estimating total mining costs in surface coal mines

Mixed Integer Programming (MIP) models are recognised as possessing the ability to optimise underground mine planning. However, MIPs use for optimising underground mine planning has often been restricted to problems of certain sizes and/or simplicity. This is because the number of variables and complex constraints in MIP formulations influences the models ability to generate optimal results. This paper reviews optimisation studies, focusing on model reduction approaches, which employ MIP techniques for simultaneous optimisation of stope layouts and underground production scheduling. Four theories are presented to reduce the number of variables and complex constraints without comprising its mathematical integrity.