A Greedy Genetic Hybrid Heuristic for Solving the Open-pit Production Scheduling Problem

Open-pit production scheduling (OPPS) is a discrete optimization problem that is classified as a non-polynomial time hard problem (NP hard). It aims to provide extraction sequences that maximize economic net present value (NPV) while satisfying different constraints. Accordingly, computational time and power are always the major concerns for OPPS optimization algorithms. This is referenced to the complexity of the problem and the substantial number of decision variables within any pit design. As a result, heuristic and meta-heuristic algorithms have been more favourable than conventional optimization algorithms. Evolutionary algorithms, i.e., genetic algorithms (GAs), have shown advantages in finding enhanced solutions. Hence, this paper proposes a novel greedy hybrid heuristic that consists of a genetic algorithm with a greedy heuristic as an initial population generation mechanism that is based on selective mining criteria. In addition, different constraints have been designed with a greedy nature. The results of eight instances that have been acquired from the MineLib datasets are presented within, and a performance comparison was held between the proposed algorithm and the previously published GA and the commercial package MiningMath®. Furthermore, statistical analysis was carried out to study the potential enhancements for the proposed algorithm compared to GA. For six instances out of eight, the results indicate that the proposed heuristic aims to produce higher solution quality than GA that ranges from 1.5% to 3.44% which is the primary objective. However, there has been no significant difference considering computational time between the proposed heuristic and GA.