Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

9-2024

Abstract

The NP-hard precedence-constrained production scheduling problem (PCPSP) for mine planning chooses the ordered removal of materials from the mine pit and the next processing steps based on resource, geological, and geometrical constraints. Traditionally, it prioritizes the net present value (NPV) of profits across the lifespan of the mine. Yet, the growing shift in environmental concerns also requires shifts to more carbon-aware practices. In this paper, we use the enhanced multi-objective version of the generic PCPSP formulation by adding the NPV of carbon costs as another objective. We then compare how the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Pareto Envelope-based Selection Algorithm II (PESA-II) solve several real-world inspired datasets, after experimenting with the selection pressure parameter of PESA-II. The outcome reveals that PESA-II runs faster for 75% of the datasets and gives sets of solutions that are more distributed. Meanwhile, NSGA-II consistently produces non-dominated solutions even when the apportionment of a decision variable is varied. Moreover, we assess how the uncertainty of ore tonnage at the mine site modifies the Pareto front via sensitivity analysis. We show that deviations above 15% induce a larger gap from the original.

Keywords

Genetic algorithms, Pareto optimization, production planning, environmental economics

Discipline

Operations Research, Systems Engineering and Industrial Engineering | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

2024 IEEE 20th International Conference on Automation Science and Engineering (CASE): Bari, Italy, August 28 - September 1: Proceedings

First Page

962

Last Page

969

ISBN

9798350358513

Identifier

10.1109/CASE59546.2024.10711825

Publisher

IEEE

City or Country

Piscataway, NJ

Additional URL

https://doi.org/10.1109/CASE59546.2024.10711825

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