Heuristic Strategies for Solving Complex Interacting Large-Scale Stockpile Blending Problems

TL;DR

This paper presents heuristic strategies, including repair operators and a multi-component fitness function, to effectively solve large-scale stockpile blending problems in mining, improving solution quality over real-world instances.

Contribution

The paper introduces two repair operators and a multi-component fitness function to enhance differential evolution for large-scale stockpile blending optimization.

Findings

The proposed methods outperform existing solutions on real-world instances.

The repair operators effectively handle infeasible solutions under tight constraints.

The combined approach improves metal volume maximization and stockpile balance.

Abstract

The Stockpile blending problem is an important component of mine production scheduling, where stockpiles are used to store and blend raw material. The goal of blending material from stockpiles is to create parcels of concentrate which contain optimal metal grades based on the material available. The volume of material that each stockpile provides to a given parcel is dependent on a set of mine schedule conditions and customer demands. Therefore, the problem can be formulated as a continuous optimization problem. In the real-world application, there are several constraints required to guarantee parcels that meet the demand of downstream customers. It is a challenge in solving the stockpile blending problems since its scale can be very large. We introduce two repaired operators for the problems to convert the infeasible solutions into the solutions without violating the two tight constraints. Besides, we introduce a multi-component fitness function for solving the large-scale stockpile blending problem which can maximize the volume of metal over the plan and maintain the balance between stockpiles according to the usage of metal. Furthermore, we investigate the well-known approach in this paper, which is used to solve optimization problems over continuous space, namely the differential evolution (DE) algorithm. The experimental results show that the DE algorithm combined with two proposed duration repair methods is significantly better in terms of the values of results than the results on real-world instances for both one-month problems and large-scale problems.