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Scheduling in iron ore open-pit mining

Received: 27 November 2011 / Accepted: 9 January 2014 / Published online: 7 March 2014

copy; Springer-Verlag London 2014

Abstract During the last few years, most production-based businesses have been under enormous pressure to improve their top-line growth and bottom-line savings. As a result, many companies are turning to systems and technologies that can help optimise their supply chain activities. In this paper, we discuss a real-world application of scheduling in the mining industry. This is a highly constrained problem with some of the constraints changing over time. The objective is to generate a plan that meets the quality and tonnage targets by utilising the provided equipment and machines. The mathematical model is described in detail ,and a three-module complex algorithm based on computational intelligence is proposed. The main software functionality is also described. The developed software is currently in the production use.

Keywords Open-pit mining · Scheduling ·Metaheuristics · Real-world application

1 Introduction

Due to the high level of complexity, it becomes virtually impossible for deterministic systems or human domain experts to find an optimal solution for many real-world problems, especially in the mining industry—not to mention that the term “optimal solution” loses its meaning in multi-objective environment , as often we can talk only about trade-offs between different solutions. Moreover, the manual iteration and adjustment of scenarios (what-if scenarios and trade-off analysis), which is needed for strategic planning, becomes an expensive, if not unaffordable, exercise .

Many texts on advance planning and supply chain management (e.g. [1]) describe several commercial software applications (e.g. As pen Tech, as pen ONE; i2 Technologies, i2 Six .Two; Oracle, JDEdwards Enterprise One Supply ChainPlanning; SAP, SCM; and many others), which emerged mainly in 1990s. However, it seems that the areas of supply chain management in general, and advanced planning in particular, are ready for a new genre of applications which are based on computational intelligence methods.

Many supply chain-related projects run at large corporations worldwide failed miserably (projects that span a few years and cost many millions). In [1], the authors wrote:

In recent years since the peak of the e-business hype Supply Chain Management and especially Advanced Planning Systems were viewed more and more critically by industry firms, as many SCM projects failed or did not realise the promised business value.

The authors also identified three main reasons for such failures:

– the perception that the more you spend on IT(e.g.APS), the more value you will get from it,

– an inadequate alignment of the SCM concept with the supply chain strategy, and

– the organisational and managerial culture of industry firms.

Whilst it is difficult to argue with the above points, it seems that the fourth (and unlisted) reason is the most important: maturity of technology. Small improvements and upgrades of systems created in 1990s do not suffice any longer for solving companiesrsquo; problems of the twenty-first century. A new approach is necessary which would combine seamlessly the forecasting ,simulation and optimization components in a new architecture. Further, many existing applications are not flexible enough in the sense that they cannot cope with any exceptions, i.e. it is very difficult, if not impossible, to include some problem-specific features—and most businesses have some unique features which need to be included in the under lying model—and are not adequately captured by off-the-shelf standard applications. Thus ,the results are often not realistic, and the team of operators return to their spreadsheets and whiteboards rather than to rely on unrealistic recommendations of the software.

There are two main branches that tackle optimisation of complex problems: operations research and computational intelligence. Operations research uses techniques such as linear programming, branch and bound, dynamic programming ,etc. In this paper ,our methods ,however ,will be based on the methods of computational intelligence.

An interesting question, which is being raised from time to time, asks for guidance on the types of problems for which computational intelligence methods are more appropriate than, say, standard operation research methods. From our perspective, the best answer to this question is given in a single phrase: complexity. Let us explain. Real-world problems are usually difficult to solve for several reasons and include the following:

– The number of possible solutions is so large as to forbid an exhaustive search for the best answer.

– The evaluation function that describes the quality of any proposed solution is noisy or varies with time, thereby requiring not just a single solution but an entire series of solutions.

– The possible solutions are so heavily constrained that constructing even one feasible answer is difficult, let alone searching for an optimum solution .Note that every time we solve a problem, we must realise that we are, in reality, only finding the solution to a model of the problem. All models are a simplification of the real world—otherwise they would be as complex and unwieldy as the natural setting itself. Thus, the process of problem solving consists of two separate general steps: (i) creating a model of the problem and (ii) using that model to generate

a solution [2]:

Problem → Model → Solution.

Note that the “solution” is only a solution in terms of the model. If our model has a high degree of fidelity, we can have more confidence that our solution will be meaningful. In contrast, if the model has too many unfulfilled assu

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Open-pit iron mine mining plan