A carbon-aware planning framework for production scheduling in mining
Speaker:
Nurul Asyikeen Binte AZHAR
EngD Candidate
School of Computing and Information Systems
Singapore Management University
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Date:
Time:
Venue:
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6 September 2022, Tuesday
2:00pm - 2:30pm
This is a virtual seminar. Please register by 30 August. The zoom link will be sent out on the following day to registrants.
We look forward to seeing you at this research seminar.
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About the Talk
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% and averaging 0.015%. For larger instances, MIP cannot even generate feasible solutions.
This is a pre-conference talk for the ICCL2022: International Conference on Computational Logistics.
ABOUT THE SPEAKER
Nurul Asyikeen Binte AZHAR is an EngD candidate at the School of Computing and Information Systems, Singapore Management University. She is supervised by Assistant Professor Aldy GUNAWAN and Associate Professor CHENG Shih-Fen. Concurrently, she is a Data Scientist at Rio Tinto. She received her Bachelor's Degree in Accountancy with second major in Corporate Communication and her Master's degree, Master of IT in Business (Analytics) from the Singapore Management University. Her research aims to enable sustainable mining via AI-based approaches.
