University of TehranInternational Journal of Mining and Geo-Engineering2345-693051120170601A genetic algorithm approach for open-pit mine production scheduling47526215210.22059/ijmge.2017.62152ENArefAlipourSchool of Mining Engineering, College of Engineering, University of Tehran, Tehran, IranAli AsgharKhodaiariSchool of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran.AhmadJafariSchool of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran.RezaTavakkoli-MoghaddamSchool of Industrial Engineering & Engineering Optimization Research Group, College of Engineering, University of Tehran, Tehran, Iran.0000-0002-6757-926XJournal Article20160723In an Open-Pit Production Scheduling (OPPS) problem, the goal is to determine the mining sequence of an orebody as a block model. In this article, linear programing formulation is used to aim this goal. OPPS problem is known as an NP-hard problem, so an exact mathematical model cannot be applied to solve in the real state. Genetic Algorithm (GA) is a well-known member of evolutionary algorithms that widely are utilized to solve NP-hard problems. Herein, GA is implemented in a hypothetical Two-Dimensional (2D) copper orebody model. The orebody is featured as two-dimensional (2D) array of blocks. Likewise, counterpart 2D GA array was used to represent the OPPS problemâ€™s solution space. Thereupon, the fitness function is defined according to the OPPS problemâ€™s objective function to assess the solution domain. Also, new normalization method was used for the handling of block sequencing constraint. A numerical study is performed to compare the solutions of the exact and GA-based methods. It is shown that the gap between GA and the optimal solution by the exact method is less than % 5; hereupon GA is found to be efficiently in solving OPPS problem.https://ijmge.ut.ac.ir/article_62152_63f524d3a495f1be5547d320fdf13636.pdf