Heuristic Approximations for Closed Networks: A Case Study in Open-pit Mining

TL;DR

This paper introduces a simple heuristic algorithm for estimating the capacity of a cyclic system in open-pit mining, outperforming more complex general-purpose algorithms in realistic scenarios.

Contribution

A novel, highly simplified algorithm tailored for open-pit mining systems that achieves better accuracy than complex general algorithms under realistic conditions.

Findings

The simple algorithm outperforms general-purpose algorithms in realistic mining scenarios.

Extensive simulations validate the effectiveness of the proposed heuristic.

The approach reduces computational complexity while maintaining high accuracy.

Abstract

We investigate a fundamental model from open-pit mining, which is a cyclic system consisting of a shovel, traveling loaded, unloading facility, and traveling back empty. The interaction of these subsystem determines the capacity of the shovel, which is the fundamental quantity of interest. To determine this capacity one needs the stationary probability that the shovel is idle. Because an exact analysis of the performance of the system is out of reach, besides of simulations there are various approximation algorithms proposed in the literature which stem from computer science and can be characterized as general purpose algorithms. We propose for solving the special problem under mining conditions an extremely simple algorithm. Comparison with several general purpose algorithms shows that for realistic situations the special algorithm outperforms the precision of the general purpose algorithms. This holds even if these general purpose candidates incorporate more details of the underlying models than our simple algorithm, which works on a strongly reduced model. The comparison and assessment is done with extensive simulations on a level of detail which the general purpose algorithms are able to cover.