An OPEN-PIT Production Scheduling (OPPS) problem focuses on specifying block production scheduling to achieve the highest possible Net Present Value (NPV). This paper presents a new mathematical model for OPPS under uncertainty. To this end, a robust box and ellipsoidal counterpart approach was used. The proposed method was implemented in a hypothetical model. A Genetic Algorithm (GA) and an exact mathematical modeling approach were used to solve the model. It was shown that the scheduling of deterministic and robust models in various conditions is different. Considering the type of robust counterparts, different production plans under various conditions were scheduled. Furthermore, the price of robustness was determined for various levels of conservation.

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.

In OPEN-PIT mine planning, the design of the most profitable ultimate PIT limit is a prerequisite to developing a feasible mining sequence. Currently, the design of an ultimate PIT is achieved through a computer program in most mining companies. The extraction of minerals in OPEN mining methods needs a lot of caPITal investment, which may take several decades. Before the extraction, the PIT limit, which influences the stripping ratio, damp locations, ore processing site and access routes, should be designed. So far, a large number of algorithms have been developed to optimize the PIT limits. These algorithms are categorized into two groups: heuristic and rigorous. In this paper, a new approach is presented to optimize the PIT limit based on Dijkstra’ s algorithm which is based on mathematical relations. This algorithm was implemented on a 2D economic graph model and can find the true optimal solution. The results were compared with those from the dynamic programming (DP) algorithm. This algorithm showed to have less time complexity compared to the dynamic programming algorithm and to be easier to write dynamic computer programs.

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.

Several studies have been reported about the importance of excavator positioning on OPEN PIT. It is essential to study and evaluate the slope stability of non-metallic deposits to understand how various factors affect it. In this work, the relationship between slope stability and the positioning of excavators in non-metallic deposits (as sand and peat) was investigated in order to increase the safety and efficiency of excavators during excavation processes. A two-stage method is proposed. A model of excavator on the PIT slope was designed and imported to ''Rocscience Slide 2D'' software with taking all slope parameters constant, except for the slope angle, excavator positioning on the slope and pressure of excavator on slope (NGP). After that the factor of safety value was collected using the Spencer Method. Moving forward with the Two-Level, Three-Factor Full Factorial Design at the Design Expert software to study the effect of each factor on the response variable (FoS), as well as the effects of interactions between factors.  The control of NGP value can reduce slope stability in average of 5 to 30% and slope angle can increase FoS by up to 5 to 15%. The study found that rational excavator positioning can significantly reduce the risk of slope failure and improve the overall productivity of the excavation process in non-metallic material deposits. Furthermore, the information obtained could be useful for the development of excavator artificial intelligence systems to control the excavator positioning on OPEN-PIT slope and minimize the effect of factor different factor on slope stability.

In OPEN-PIT mining, different designs are created, such as optimal ultimate PIT limit and production planning. In order to determine the ultimate PIT limit, two approaches are generally used based on geological and economic block models. In this paper, according to the longterm trend of metals price and mining costs, some suggestions were made to design the ultimate PIT limit using the geological block model. In addition, a grade-based objective function was presented for determining the ultimate PIT limit. Then, in order to solve the problem, a heuristic algorithm was developed to simultaneously determine the ultimate PIT limit and the sequence of block mining. For a 2D geological block model, the final PIT was generated using the proposed algorithm. Furthermore, to validate the generated PIT limit, the results of a 3D geological block model were compared with those of the Lerchs-Grossman algorithm. The comparison showed that the two PITs corresponded to each other with an accuracy value of 97. 7 percent.