A robust mixed integer linear programming framework for underground cut-and-fill mining production scheduling

Huang, Shaobo; Li, Guoqing; Ben-Awuah, Eugene; Afum, Bright Oppong; Hu, Nailian

doi:10.1080/17480930.2019.1576576

Cited by 19 publications

(17 citation statements)

References 10 publications

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“…In such cases, the portion of the orebody near the earth surface is evaluated to be exploited by OP mining method to produce early revenue, while the deeper portion is either outrightly evaluated for UG mining or the evaluation is deferred for later years in the future [7]. In general, OP mining methods are characterized by relatively low mining and operating costs, high stripping ratio, and extended time in accessing the mineral ore [4,46] while UG mining is characterized by high mining and operating costs, high-grade ore, and earlier times in retrieving the ore [31,[47][48][49][50].…”

Section: Mineral Projects Evaluationmentioning

confidence: 99%

A Review of Models and Algorithms for Surface-Underground Mining Options and Transitions Optimization: Some Lessons Learnt and the Way Forward

Afum

Ben-Awuah

2021

Mining

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It is important that the strategic mine plan makes optimum use of available resources and provides continuous quality ore to drive sustainable mining and profitability. This requires the development of a well-integrated strategy of mining options for surface and/or underground mining and their interactions. Understanding the current tools and methodologies used in the mining industry for surface and underground mining options and transitions planning are essential to dealing with complex and deep-seated deposits that are amenable to both open pit and underground mining. In this study, extensive literature review and a gap analysis matrix are used to identify the limitations and opportunities for further research in surface-underground mining options and transitions optimization for comprehensive resource development planning.

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Section: Mineral Projects Evaluationmentioning

confidence: 99%

A Review of Models and Algorithms for Surface-Underground Mining Options and Transitions Optimization: Some Lessons Learnt and the Way Forward

Afum

Ben-Awuah

2021

Mining

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“…However, the second stage is optimization of the nonlinear functional. To find the optimal solution, we use gradient descent [27,28] and penalty functions [29][30][31]. Then problem (10,18,22,23,28,29) takes the form (18,22,23,(30)(31)(32)(33)(34)36) with the iteration rule: The stopping criterion will be considered the achievement of such a value of k so that the inequality Let us consider formulas (30)(31)(32)(33)(34)(35)) in more detail.…”

Section: Stagementioning

confidence: 99%

“…To find the optimal solution, we use gradient descent [27,28] and penalty functions [29][30][31]. Then problem (10,18,22,23,28,29) takes the form (18,22,23,(30)(31)(32)(33)(34)36) with the iteration rule: The stopping criterion will be considered the achievement of such a value of k so that the inequality Let us consider formulas (30)(31)(32)(33)(34)(35)) in more detail. Formula (30) -objective function with penalties Constraint (31) reflects the difference between the volume of products delivered to the customer and the value of the demand of the same customer.…”

Section: Stagementioning

confidence: 99%

A mathematical model for the formation of the pricing policy and the plan of the production and transport system in a timber-processing enterprise

Rogulin¹

2021

Bus. Inform.

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The formation of supply chains for raw materials is closely related to production problems involving the determination of prices for sold goods. The question often arises about the need to study the sources of raw materials and the methodology for pricing the goods produced, taking into account a large number of external aspects of the market. Often, only particular approaches to solving production problems are considered in the literature, and methods for solving the complex problem of forming supply chains for raw materials and pricing are poorly developed. This paper presents a mathematical model that makes it possible to assess the feasibility of interaction between a timber industry enterprise and a commodity exchange, with the daily formation of a price vector over the entire planning horizon. A two-stage algorithm for finding a suboptimal solution is considered, which at the first stage is based on linear optimization, and at the second, on gradient descent with the use of penalty functions. The model was tested on the data of the commodity and raw materials exchange of Russia and one of the enterprises of the Primorsky Territory. The result of testing was the volume of production of each type of product over the entire planning horizon, the volume of delivery of raw materials from regions to enterprises, as well as the methods of delivery of goods to the consumer and the policy of pricing. It is shown that almost all goods should increase in price due to a reduction in the excess volume of applications (demand) over the entire planning horizon, with the exception of two types of products. It is noted that the exchange can provide the necessary volume of raw materials for high-capacity production, which demonstrates the possibility, if necessary, to increase the volume of raw materials purchases. It is shown which goods will be included in the release plan more often than others when optimizing the price vector. The ways of delivery of final types of products are analyzed. The disadvantages and advantages of the mathematical model and algorithm are presented.

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“…In general, mine production scheduling is the determination of the sequence and processing of mineralized material that maximizes the overall net present value subject to technical and operational constraints, while shielding the mining operation from risk [1], [2]. Strategic mining decisions could be made at different time scales entailing varying objectives for long-term, medium-term, and short-term plans.…”

Section: Introductionmentioning

confidence: 99%

“…Pourrahimian [9] and Pourrahimian and Askari-Nasab [10] proposed a theoretical optimization framework based on a MILP model for long-term production scheduling in underground block-caving aiming to maximize net present value (NPV) within acceptable technical and operational constraints. Huang et al [2] developed a MILP model for an underground cut-and-fill mining production scheduling, which aims to maximize the NPV and generate a practical, operational, and long-term mining schedule. However, these deterministic formulations described above consider a single estimated orebody model and are incapable of dealing with in-situ variability of orebodies [11].…”

Section: Introductionmentioning

confidence: 99%

A Stochastic Mixed Integer Programming Framework for Underground Mining Production Scheduling Optimization Considering Grade Uncertainty

Huang

Ben-Awuah

et al. 2020

IEEE Access

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Conventional mine planning approaches use an estimated orebody model as input to generate optimal production schedules. The smoothing effect of some geostatistical estimation methods cause most of the mine plans and production forecasts to be unrealistic and incomplete. With the development of simulation methods, the risks from grade uncertainty in ore reserves can be measured and managed through a set of equally probable orebody realizations. In order to incorporate grade uncertainty into the strategic mine plan, a stochastic mixed integer programming (SMIP) formulation is presented to optimize an underground cut-and-fill mining production schedule. The objective function of the SMIP model is to maximize the net present value (NPV) of the mining project and minimize the risk of deviation from the production targets. To demonstrate the applicability of the SMIP model, a case study on a cut-and-fill underground gold mining operation is implemented.

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A robust mixed integer linear programming framework for underground cut-and-fill mining production scheduling

Cited by 19 publications

References 10 publications

A Review of Models and Algorithms for Surface-Underground Mining Options and Transitions Optimization: Some Lessons Learnt and the Way Forward

A Review of Models and Algorithms for Surface-Underground Mining Options and Transitions Optimization: Some Lessons Learnt and the Way Forward

A mathematical model for the formation of the pricing policy and the plan of the production and transport system in a timber-processing enterprise

A Stochastic Mixed Integer Programming Framework for Underground Mining Production Scheduling Optimization Considering Grade Uncertainty

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