A carbon-aware planning framework for production scheduling in mining

• Main Authors: AZHAR, Nurual Asyikeen, GUNAWAN, Aldy, CHENG, Shih-Fen, LEONARDI, Erwin
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Operations research and management; Resource capacity planning; Lagrangian relaxation; Sustainability; Artificial Intelligence and Robotics; Operations Research, Systems Engineering and Industrial Engineering; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/7566; https://ink.library.smu.edu.sg/context/sis_research/article/8569/viewcontent/978_3_031_16579_5_30_pv.pdf
• Institution: Singapore Management University

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.