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

被引:3
|
作者
Joshi, Devendra [1 ]
Gholami, Hamed [2 ]
Mohapatra, Hitesh [3 ]
Ali, Anis [4 ]
Streimikiene, Dalia [5 ]
Satpathy, Susanta Kumar [6 ]
Yadav, Arvind [1 ]
机构
[1] Koneru Lakshmaiah Educ Fdn, Dept CSE, Vaddeswaram 522302, Andhra Pradesh, India
[2] Univ Teknol Malaysia, Fac Engn, Dept Mfg & Ind Engn, Johor Baharu 81310, Malaysia
[3] KIIT Deemed Univ, Sch Comp Engn, Bhubaneswar 751024, Odisha, India
[4] Prince Sattam Bin Abdulaziz Univ, Coll Business Adm, Dept Management, Al Kharj 11942, Saudi Arabia
[5] Vilnius Univ, Kaunas Fac, Muitines 8, LT-44280 Kaunas, Lithuania
[6] Vignans Fdn Sci Technol & Res, Dept Comp Sci & Engn, Vadlamudi 522213, Andhra Pradesh, India
关键词
stochastic production scheduling; mixed integer programming; geological uncertainty; net present value; branch and cut; TRADITIONAL OPTIMIZATION; PIT; DESIGN; ALGORITHMS;
D O I
10.3390/su14169819
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:19
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