The primary aim of this study was to define the final Pit boundaries utilizing the maximum flow Pseudoflow method in an open-Pit mining context. Our methodology encompassed exploratory data analysis (EDA), establishing geomechanical and economic factors, and assessing the final Pit. The study was conducted using Python 3.11 and SGeMS 3.0. We discovered that our block model comprised 480,000 blocks of 10x10x10 m dimensions. We generated 20 Pits with revenue factors between 0.1 to 2, increasing by increments of 0.1. The Study indicated that Pit 20 was optimal, with an estimated NPV of 17855 MUSD, extracting 212 million tons of ore and 58 million tons of waste rock, achieving a stripping ratio based on block model and market conditions, and is subject to change with further block sequencing analysis. Nevertheless, Pit 20 emerged as the most advantageous when considering economic feasibility, given its high estimated NPV and favorable stripping ratio.

The large-scale open-Pit mine production planning problem is an NP-hard issue. That is, it cannot be solved in a reasonable computational time. To solve this problem, various methods, including metaheuristic methods, have been proposed to reduce the computation time. One of these methods is the genetic algorithm (GA) which can provide near-optimal solutions to the problem in a shorter time. This paper aims to evaluate the efficiency of the GA technique based on the Pit values and computational times compared with other methods of designing the ultimate Pit limit (UPL). In other words, in addition to GA evaluation in UPL design, other proposed methods for UPL design are also compared. Determining the UPL of an open-Pit mine is the first step in production planning. UPL solver selects blocks whose total economic value is maximum while meeting the slope constraints. In this regard, various methods have been proposed, which can be classified into three general categories: Operational Research (OR), heuristic, and metaheuristic. The GA, categorized as a metaheuristic method, Linear Programming (LP) model as an OR method, and Floating Cone (FC) algorithm as a heuristic method, have been employed to determine the UPL of open-Pit mines. Since the LP method provides the exact answer, consider the basics. Then the results of GA were validated based on the results of LP and compared with the results of FC. This paper used the Marvin mine block model with characteristics of 53271 blocks and eight levels as a case study. Comparing the UPL value's three ways revealed that the LP model received the highest value by comparing the value obtained from GA and the FC algorithm's lowest value. However, the GA provided the results in a shorter time than LP, which is more critical in large-scale production planning problems. By performing the sensitivity analysis in the GA on the two parameters, crossover and mutation probability, the GA's UPL value was modified to 20940. Its UPL value is only 8% less than LP's UPL value.

Open-Pit mining method has severe environmental impacts which should be prevented, monitored, controlled, and reduced by mined-land reclamation process. After mine closure, a permanent post-mining land use should be implemented as an appropriate choice for using different sections of mined land. The most appropriate alternative of post-mining land use for each section of mined land is presented as the optimum post-mining land use. Pit area among different sections of mined land has more significant effects on the environment and also on defining the optimum post-mining land use for other sections of mined land. Though there are several alternatives and criteria for defining the optimum post-mining land use, the multi-attribute decision-making methods can be efficient techniques in this regard. The nature of the effective parameters used for defining the optimum post-mining land use is the same as Fuzzy numbers including incremental changes without definite limits. Thus, application of the Fuzzy multi-attribute decision-making modeling can produce more reliable results than that of other techniques. As well, pair-wise comparisons and judgments through Fuzzy numbers have proper consistency with the nature of the effective parameters; therefore, a model is developed to attain the optimum post-mining land use for Pit area through Fuzzy analytical hierarchy processing. As a case study, the model was implemented in Sungun copper mine in the Northwest of Iran. Forestry–lumber production was defined as the optimum post-mining land use containing the greatest relative importance coefficient 3.019 for the Pit area in this mine.