ORIGINAL PAPER Determination of optimal production rate under price uncertainty—Sari Gunay gold mine, Iran Parviz Sohrabi 1 & Hesam Dehghani 1 & Behshad Jodeiri Shokri 1 Received: 7 September 2020 /Accepted: 4 February 2021 # The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 Abstract Due to the long life, most mining projects face the risk of the parameters such as mineral price, grade, and cost. Uncertainty can lead to unfavorable results of the decisions made by managers and mining investors. Therefore, this paper aims to determine the Sari Gunay gold mine’s production planning, considering the certainty and uncertainty over the mineral price. Finally, the proposed planning will lead to the allocation of fixed or variable production rates throughout the mine life. These findings were assessed by Taylor and Zwiagin methods with 22 different scenarios in all conditions, including (a) price certainty and uncer- tainty such as daily price, 3- and 5-year average, Monte Carlo simulation, and binomial tree; (b) decreasing, increasing, and fixed production rates; and (c) mine life conditions. The scenarios evaluated under the price certainty conditions (scenarios 1 to 12) have lower NPV values than those under the price uncertainty conditions. This is because the price is fixed throughout the mine life. Due to historical price data and high fluctuations of estimated prices, this method’s NPV values fluctuate more than other scenarios evaluated by the Monte Carlo simulation. The binomial tree method scenarios have the lowest NPV value’s fluctuation because the fluctuation of the estimated prices is controlled, and the highest NPV values are related to this method. Out of the 22 scenarios, scenario 17 has the highest NPV value ($512,642,774). According to this scenario, the mine plan is determined, and the annual production rate is reduced to 3,241,977 tons in the first year and 270,165 tons in the last year with the Taylor life of 12 years. Keywords Production planning . mineral price uncertainty . Monte Carlo simulation . binomial tree Introduction Production planning in surface mines is one of the most com- plex operational issues in the design phase (Gershon 1983; Hartman 1992). Making important decisions such as expanding, closing, or outsourcing the mining activities largely depends on the production planning conditions (Quigley and Dimitrakopoulos 2020). Due to the long life, most mining projects face the risk of the parameters such as mineral price, grade, and cost (Akbari et al. 2009). Uncertainty can lead to unfavorable results of the decisions made by managers and mining investors (Dehghani and Ataee-pour 2012). Previous studies and experiences show that the higher the production rate, the lower the project’s price, and consequently, the higher the profit rate (Linder and Wilbourn 1973; Asghar and Kim 2020). In such conditions, the capital costs will increase, and the mine life will decrease. The production rate is generally affected by various uncertainties (Hustrulid et al. 1995). Unfavorable results may be achieved if the production rate is evaluated without considering the uncertainties. One of the most important sources of uncertainty for the evaluation of mining projects is economic uncertainty. The uncertainties over mineral price and operating costs are the most significant eco- nomic uncertainty examples (Cox et al. 1979; Dehghani and Ataee-pour 2012, 2013). Various researchers have done many works to determine the production rate and role of uncertainties, mostly based on the innovative Black-Scholes, Monte Carlo, and binomial tree methods. Lee (2018) estimated the commodity price using Black-Scholes model and binomial tree method and then used the results to evaluate the project using the real option valuation technique. In a study at the Segilola gold mine, Ugwuegbu (2013) used the Monte Carlo simulation method to estimate economic parameters, including metal prices. The * Behshad Jodeiri Shokri b.jodeiri@hut.ac.ir 1 Department of Mining Engineering, Hamedan University of Technology (HUT), Hamedan, Iran Mineral Economics https://doi.org/10.1007/s13563-021-00253-8