A Genetic algorithm scheme for large scale open-pit mine production scheduling

ABSTRACT

Due to the large size of open-pit mines’ long-term production scheduling (OPMPS) problem in large-scale deposits, it is challenging to solve that problem as the mixed integer linear programming (MILP) model. This study used an approach of the genetic algorithm (GA) to tackle this challenge. So, in a small hypothetical deposit, based on the blocks in the ultimate pit limit and scenarios with 2–6 phases, net present values (NPV) and computational times obtained from the GA and MILP model were compared to evaluate the GA. Also, the GA was applied to a large-scale deposit to determine the efficiency of the GA in real deposits. The maximum NPV was obtained for the four-phase scenario in the hypothetical deposit and the six-phase scenario in the large-scale deposit. Although the GA’s NPV decreased slightly compared to the global optimum solution from the MILP model, the computational time was significantly reduced.

KEYWORDS:

• Open-Pit mine
• ultimate pit limit
• net present value
• operational constraints
• NP-hard problem
• long-term production scheduling
• genetic algorithm
• mixed-integer linear programming model

Disclosure statement

No potential conflict of interest was reported by the author(s).