The Application of Stochastic Mine Production Scheduling in the Presence of Geological Uncertainty

Devendra Joshi, Hamed Gholami (), Hitesh Mohapatra (), Anis Ali, Dalia Streimikiene, Susanta Kumar Satpathy and Arvind Yadav
Additional contact information
Devendra Joshi: Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522302, Andhra Pradesh, India
Hamed Gholami: Department of Manufacturing and Industrial Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
Hitesh Mohapatra: School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar 751024, Odisha, India
Anis Ali: Department of Management, College of Business Administration, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
Dalia Streimikiene: Kaunas Faculty, Vilnius University, Muitines 8, LT-44280 Kaunas, Lithuania
Susanta Kumar Satpathy: Department of Computer Science and Engineering, Vignan’s Foundation for Science, Technology and Research, Vadlamudi 522213, Andhra Pradesh, India
Arvind Yadav: Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522302, Andhra Pradesh, India

Sustainability, 2022, vol. 14, issue 16, 1-19

Abstract: The scheduling of open-pit mine production is a large-scale, mixed-integer linear programming problem that is computationally expensive. The purpose of this study is to create a computationally efficient algorithm for solving open-pit production scheduling problems with uncertain geological parameters. To demonstrate the effectiveness of the proposed research, a case study of an Indian iron ore mine is presented. Multiple realizations of the resource models were developed and integrated within the stochastic production scheduling framework to capture uncertainty and incorporate it into the mine plan. In this case study, two hybrid methods were developed to evaluate their performance. Model 1 is a combined branch and cut with the longest path, whereas Model 2 is a sequential parametric maximum flow and branch and cut. The results show that both methods produce similar materials, ore, metal, and risk profiles; however, Model 2 generates slightly more (4 percent) discounted cash flow from this study mine than Model 1. The results also show that Model 2’s computational time is 46.64 percent less than that of Model 1.

Keywords: stochastic production scheduling; mixed integer programming; geological uncertainty; net present value; branch and cut (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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