Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

9-2022

Abstract

Managing the flow of excavated materials from a mine pit and the subsequent processing steps is the logistical challenge in mining. Mine planning needs to consider various geometric and resource constraints while maximizing the net present value (NPV) of profits over a long horizon. This mine planning problem has been modelled and solved as a precedence constrained production scheduling problem (PCPSP) using heuristics, due to its NP-hardness. However, the recent push for sustainable and carbon-aware mining practices calls for new planning approaches. In this paper, we propose an efficient temporally decomposed greedy Lagrangian relaxation (TDGLR) approach to maximize profits while observing the stipulated carbon emission limit per year. With a collection of real-world-inspired mining datasets, we demonstrate how we generate approximated Pareto fronts for planners. Using this approach, they can choose mine plans that maximize profits while observing the given carbon emission target. The TDGLR was compared against a Mixed Integer Programming (MIP) model to solve a real mine dataset with the gaps not exceeding 0.3178%0.3178% and averaging 0.015%0.015%. For larger instances, MIP cannot even generate feasible solutions.

Keywords

Operations research and management, Resource capacity planning, Lagrangian relaxation, Sustainability

Discipline

Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering | Theory and Algorithms

Research Areas

Intelligent Systems and Optimization

Publication

ICCL 2022: Proceedings of the 13th International Conference, Barcelona, September 21–23

Volume

13557

First Page

441

Last Page

456

ISBN

9783031165788

Identifier

10.1007/978-3-031-16579-5_30

Publisher

Springer

City or Country

Cham

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1007/978-3-031-16579-5_30

Download

Share

COinS