Short-term Production Scheduling Research Articles

Improving block caving production by incorporating operational risks in short-term scheduling

In caving mines, productivity and safety can be affected by geotechnical hazards, such as rockbursts, inrushes, collapses, and air blasts. These operational risks decrease the availability of drawpoints and impact compliance with the short-term mine production schedule. In these cases, the mine production schedule has to be updated, modifying the amount of mineral extracted from the drawpoints and often producing sub-optimal results. This study proposes a new methodology to incorporate operational risks in the short-term scheduling process in Block Caving mines. The proposed methodology employs a logistic regression model to define the drawpoints at high-risk and a multi-objective optimization model that determines the daily production to meet the grades and tonnage targets, and at the same time, minimize the operational risk. The proposed methodology is implemented in a case study in which the methodology is shown to reduce tonnage at high-risk of experiencing an event of inrush of fine material while meeting the production targets. The results of the present work demonstrate that it is possible to integrate operational risk in the short-term production scheduling of Block Caving mines, allowing the planner to modify the hierarchy of objectives to adapt to the mine's requirements.

Food Production Scheduling: A Thorough Comparative Study between Optimization and Rule-Based Approaches

This work addresses the lot-sizing and production scheduling problem of multi-stage multi-product food industrial facilities. More specifically, the production scheduling problem of the semi-continuous yogurt production process, for two large-scale Greek dairy industries, is considered. Production scheduling decisions are made using two approaches: (i) an optimization approach and (ii) a rule-based approach, which are followed by a comparative study. An MILP model is applied for the optimization of short-term production scheduling of the two industries. Then, the same problems are solved using the commercial scheduling tool ScheduleProTM, which derives scheduling decisions using simulation-based techniques and empirical rules. It is concluded that both methods, despite having their advantages and disadvantages, are suitable for addressing complex food industrial scheduling problems. The optimization-based approach leads to better results in terms of operating cost reduction. On the other hand, the complexity of the problem and the experience of production engineers and plant operators can significantly impact the quality of the obtained solutions for the rule-based approach.

Integrated simulation and optimization framework for quantitative analysis of near-face stockpile mining

A new conceptual mining method called the near-face stockpile (NFS), which combines the in-pit crushing and conveying IPCC system with a pre-crusher stockpile, has recently been proposed for large open pits with sufficient pit bottom width. In the past, stockpiles mainly act as a "buffer" to improve the stability of the production system. However, in NFS ore will be dumped into the pre-crusher stockpile located at the bottom of the current pit and be fed into the crusher after blending to the desired head grade, instead of being dumped directly into the crusher. Theoretically, this design not only retains the high efficiency and high output of IPCC, but also endows the mining system with better quantity and quality stability. Verifying these advantages and objectively evaluating the NFS method has become a problem worth studying. Since the NFS method exhibits distinctive layout and feeding mechanisms, which distinguish it from other mining methods, a novel simulation model is required for the accurate modeling of the NFS method and quantitatively evaluating the performance of NFS methods. In addition, this paper also proposes an optimization model for short-term production scheduling for NFS method based on the mixed integer linear programming (MILP) method. An oil sands mine case study is implemented to verify the proposed simulation model. One year simulation results reveal that compared to the traditional mining method, the overall production increased by 5.06%, the transporting distance of minerals by trucks was reduced by 17.87%, and the shovels’ and crusher's utilization increased by 4.96% and 4.85%, respectively, when using NFS method.

Thermal-Hydraulic-Mechanical (THM) Modelling of Short-Term Gas Storage in a Depleted Gas Reservoir—A Case Study from South Germany

This study addresses the use of former gas storage facilities as short-term storage for renewable energy through power-to-gas (PtG) technology in Germany. Three test cases with coupled thermal-hydromechanical (THM) modelling were conducted to evaluate short-term injection and production schedules. The operating rates were controlled by the upper and lower limits of the wellbore pressure. The maximum difference in pore pressure and effective stress was 0.6 MPa in all cases. Fault reactivation analysis was performed on the THM models to estimate fault stability. The critical pore pressure for safe reservoir operation was determined to be 1.25 times the original pore pressure, corresponding to a WBHP value of 20.25 MPa. The upper limit of the gas injection rate for safe storage operation was estimated to be between 100,000 and 150,000 m3/day. The thermal stresses were found to be negligible for short-term cases. The storage capacity of PtG technology was reported to be up to 1,322,400 kWh/d of renewable electricity, which can contribute to Germany becoming a greenhouse gas neutral country by 2050. The workflows and results of the study are applicable to all gas storage in a porous medium, including methane, CO2, and hydrogen.

Optimal mining cut definition and short-term open pit production scheduling under geological uncertainty

Short-term planners must define an operational schedule based on blasthole data, which might be incomplete or imperfect. As a result, there is uncertainty in the true grade of each Selective Mining Unit. This can impact the definition of mining cuts and dig-limits, and the fulfillment of long-term targets. In this work, we propose a stochastic mixed integer program to define an operational mining cut configuration and a short-term schedule under grade uncertainty. The objective of the model is to maximize revenue and minimize deviations from production targets. We test the model using real data from a copper deposit and use grade simulations for quantifying the uncertainty in blasthole data. The results show that the stochastic model achieves higher profit margins and lower deviations from production targets compared to a deterministic variant based on a single estimated scenario. At the same time, the numerical experiments show the potential impact of blasthole uncertainty in profit and compliance of short-term schedules. Therefore, the use of this stochastic formulation can help planners find optimal mining cuts and improve target fulfillment and profitability of short-term operational plans in the presence of grade uncertainty. Finally, we propose several extensions to include new sources of uncertainty.

Utilisation of geometallurgical predictions of processing plant reagents and consumables for production scheduling under uncertainty

ABSTRACT Consumption rates of reagents and consumables are typically accounted for in mine production scheduling by simple cost adjustment factors per rock type or mining block, ignoring geological uncertainty, blending, and geometallurgical information. To overcome these limitations, this article first creates empirical geometallurgical prediction models of reagents and consumables by tracking blended rock properties that are matched with observed consumption rates at the operating processing plant. The prediction models are then integrated in a simultaneous stochastic optimisation model for short-term production scheduling. Improvements over a conventional short-term production schedule are demonstrated in a case study at the Tropicana Gold mining complex.

An Integrated Stochastic Deep Learning–Short-Term Production Scheduling–Optimal Control Framework for General Batch Processes

An Integrated Stochastic Deep Learning–Short-Term Production Scheduling–Optimal Control Framework for General Batch Processes

Integrating geometallurgical ball mill throughput predictions into short-term stochastic production scheduling in mining complexes

This article presents a novel approach to integrate a throughput prediction model for the ball mill into short-term stochastic production scheduling in mining complexes. The datasets for the throughput prediction model include penetration rates from blast hole drilling (measurement while drilling), geological domains, material types, rock density, and throughput rates of the operating mill, offering an accessible and cost-effective method compared to other geometallurgical programs. First, the comminution behavior of the orebody was geostatistically simulated by building additive hardness proportions from penetration rates. A regression model was constructed to predict throughput rates as a function of blended rock properties, which are informed by a material tracking approach in the mining complex. Finally, the throughput prediction model was integrated into a stochastic optimization model for short-term production scheduling. This way, common shortfalls of existing geometallurgical throughput prediction models, that typically ignore the non-additive nature of hardness and are not designed to interact with mine production scheduling, are overcome. A case study at the Tropicana Mining Complex shows that throughput can be predicted with an error less than 30 t/h and a correlation coefficient of up to 0.8. By integrating the prediction model and new stochastic components into optimization, the production schedule achieves weekly planned production reliably because scheduled materials match with the predicted performance of the mill. Comparisons to optimization using conventional mill tonnage constraints reveal that expected production shortfalls of up to 7% per period can be mitigated this way.

A Genetic Algorithm-Based Approach for Optimizing Short-term Production Schedules of Multi-mine Mineral Value Chains

A Genetic Algorithm-Based Approach for Optimizing Short-term Production Schedules of Multi-mine Mineral Value Chains

Mining design and short-term production scheduling by using 3D modeler in Coal mining at PT. Internasional Prima Coal, Palaran District, Samarinda City, East Kalimantan Province

PT. International Prima Coal is a subsidiary of a combination of two companies, namely PT. Bukit Asam (Persero) Tbk and PT. Mega Raya Kusuma which is engaged in coal mining in East Kalimantan, with an area of 3,238 hectares of Mining Business Permit in Bantuas and Handil Bakti Villages, Palaran District, Samarinda City which is planning to open a new block, namely North Eagle 3 block for the sustainability of coal production. This is the background for the author to research mining design and production schedule. This study aims to analyze resources and reserves, design mining, determine the combination of tools and monthly production scheduling. The research method used is qualitative and quantitative methods. the quantitative method will produce data such as the value of resources and reserves, slope geometry, SR (Stripping Ratio), and the amount of production per month. The qualitative method will produce data such as haul road designs, mining methods, mining sequences. Based on the results of modeling of coal deposits in the northern pit eagle 3 has 3 seams (Q050, Q070, Q080) with a total measured resource calculated by the polygon method is 726,898 tons. The design of the mining pit limit at an elevation of 0 masl is the contour of the Q070 seam structure with a total coal reserve of 66,516 tons and OB 651,952 bcm with SR 10. The haul road is made of 2 lanes with a straight road width of 9 m and a bend road width of 18 m. The form of drainage that is made is a channel with a trapezoid shape and a settling pond. The mining method used is open pit with down-dip excavation. Mining sequences and production schedules are divided into 4 mining sequences to obtain a production target of ±16,000 tons/month.

MILP performance improvement strategies for short-term batch production scheduling: a chemical industry use case

This paper presents the development and mathematical implementation of a production scheduling model utilizing mixed-integer linear programming (MILP). A simplified model of a real-world multi-product batch plant constitutes the basis. The paper shows practical extensions to the model, resulting in a digital twin of the plant. Apart from sequential arrangement, the final model contains maintenance periods, campaign planning and storage constraints to a limited extend. To tackle weak computational performance and missing model features, a condensed mathematical formulation is introduced at first. After stating that these measures do not suffice for applicability in a restrained time period, a novel solution strategy is proposed. The overall non-iterative algorithm comprises a multi-step decomposition approach, which starts with a reduced scope and incrementally complements the schedule in multiple subproblem stages. Each of those optimizations holds less decision variables and makes use of warmstart information obtained from the predecessor model. That way, a first feasible solution accelerates the subsequent improvement process. Furthermore, the optimization focus can be shifted beneficially leveraging the Gurobi solver parameters. Findings suggest that correlation may exist between certain characteristics of the scheduling scope and ideal parameter settings, which yield potential for further investigation. Another promising area for future research addresses the concurrent multi-processing of independent MILPs on a single machine. First observations indicate that significant performance gains can be achieved in some cases, though sound dependencies were not discovered yet.

Efficiency comparison of proactive approaches to deal with machine failures*

Due to the innovations brought by Industry 4.0, decision-makers must rethink the premisses and methods used to solve production planning problems. We study short-term production planning and scheduling problems subject to disruptions, incorporating characteristics of Industry 4.0 into the analyzed scenario. We propose and compare two proactive approaches for dealing with machine failures on the shop floor. The methods aim to find sequences with low probabilities of infeasibility at the scheduling level even with the occurrence of disruptions. One proactive approach performs simulations before the resolution of the planning problems to define slacks added to the capacity constraints of the short-term production planning problem. On the other hand, the second approach determines these slacks based on the probability distributions of the failures. According to the experiments, we conclude that the proposed approaches find similar results for most instance sets when comparing the averages and standard deviations. The results indicate that is not necessary to perform simulations before the resolution of the planning problems; a quick analysis of the probability distributions could be used by the decision-maker.

Production scheduling in industrial mining complexes with incoming new information using tree search and deep reinforcement learning

Industrial mining complexes have implemented digital technologies and advanced sensors to monitor and gather real-time data about their different operational aspects, starting from the supply of materials from the mineral deposits involved to the products provided to customers. However, technologies are not available to respond in real-time to the incoming new information to adapt the short-term production schedule of a mining complex. A short-term production schedule determines the daily/weekly/monthly sequence of extraction, the destination of materials and utilization of processing streams. This paper presents a novel self-learning artificial intelligence algorithm for mining complexes that learns, from its own experience, to adapt the short-term production scheduling decisions by responding to incoming new information. The algorithm plays the game of short-term production scheduling on its own using a Monte Carlo tree search to train a deep neural network agent that adapts the short-term production schedule with incoming new information. The deep neural network agent evaluates the short-term production scheduling decisions and, in parallel, performs searches using the Monte Carlo tree search to generate experiences. The experiences are then used to train the agent. The agent improves the strength of the tree search, which results in an even stronger self-play to generate better experiences. An application of the proposed algorithm at a real-world copper mining complex shows its exceptional performance to adapt the 13-week short-term production schedule almost in real-time. The adapted production schedule successfully meets the different production requirements and makes better use of the processing capabilities, while also increasing copper concentrate production by 7% and cash flows by 12% compared to the initial production schedule. A video of the proposed algorithm can be found at https://youtu.be/_gSbzxMc_W8.

Control-aware batch process scheduling

Batch processes are inevitably affected by disturbances. The process control system can often reject them and ensure that the final product is “in spec.” Nevertheless, processing times and the quality of the final product and any intermediates can fluctuate as a result. In turn, this impacts the computation and execution of production schedules. We propose a new scheduling framework that considers the effect of disturbances and the impact of process control on short-term production scheduling of batch processes. Processing time expressions are derived using historical closed-loop process operating data. Subsequently, a two-stage stochastic scheduling problem is formulated, whereby exogenous and endogenous uncertainties are considered at the first stage, with the exception of product demand, which is dealt with in the second stage. Two case studies are presented in detail.

Optimization of truck-shovel allocation in open-pit mines under uncertainty: a chance-constrained goal programming approach

ABSTRACT The truck allocation problem is an important section of the transportation system in open-pit mines. Most available models for the truck scheduling problem do not directly address the stochastic nature of truck-shovel systems by using multi-objective optimization techniques. This paper presents a chance-constrained goal programming (CCGP) model based on four important goals to estimate the impacts of the uncertainty on the efficiency of truck-shovel systems. The proposed model has been implemented using 11 schedule scenarios and different confidence levels (CLs) for loader’s production to determine the best allocation of trucks in an open-pit copper mine. The results display that the model can handle the quality and quantity of material required to achieve the objectives of the short-term production schedule of the mine in all CLs, even in the highest risk level. This model has a remarkable ability to meet the required objectives in terms of uncertainty in mining operations.

Short-term production scheduling in open pit mines with shovel allocations over continuous time frames

Short-term production scheduling in open pit mines with shovel allocations over continuous time frames

Short-term production scheduling in open pit mines with shovel allocations over continuous time frames

Short-term production scheduling in open pit mines with shovel allocations over continuous time frames

A system for underground road condition monitoring

Poor road conditions in underground mine tunnels can lead to decreased production efficiency and increased wear on production vehicles. A prototype system for road condition monitoring is presented in this paper to counteract this. The system consists of three components i.e. localization, road monitoring, and scheduling. The localization of vehicles is performed using a Rao-Blackwellized extended particle filter, combining vehicle mounted sensors with signal strengths of WiFi access points. Two methods for road monitoring are described: a Kalman filter used together with a model of the vehicle suspension system, and a relative condition measure based on the power spectral density. Lastly, a method for taking automatic action on an ill-conditioned road segment is proposed in the form of a rescheduling algorithm. The scheduling algorithm is based on the large neighborhood search and is used to integrate road service activities in the short-term production schedule while minimizing introduced production disturbances. The system is demonstrated on experimental data collected in a Swedish underground mine.

A simulation–optimization framework for short-term underground mine production scheduling

Mine operations are supported by a short-term production schedule, which defines where and when mining activities are performed. However, deviations can be observed in this short-term production schedule because of several sources of uncertainty and their inherent complexity. Therefore, schedules that are more likely to be reproduced in reality should be generated so that they will have a high adherence when executed. Unfortunately, prior estimation of the schedule adherence is difficult. To overcome this problem, we propose a generic simulation–optimization framework to generate short-term production schedules for improving the schedule adherence using an iterative approach. In each iteration of this framework, a short-term schedule is generated using a mixed-integer linear programming model that is simulated later using a discrete-event simulation model. As a case study, we apply this approach to a real Bench and Fill mine, wherein we measure the discrepancies among the level of movement of material with respect to the schedule obtained from the optimization model and the average of the simulated schedule using the mine schedule material’s adherence index. The values of this index decreased with the iterations, from 13.1% in the first iteration to 4.8% in the last iteration. This improvement is explained because the effects of the operational uncertainty within the optimization model can be considered by integrating the simulation. As a conclusion, the proposed framework increases the adherence of the short-term schedules generated over iterations. Moreover, these increases in the adherence of schedules are not obtained at the expense of the Net Present Value.

Joint stochastic short-term production scheduling and fleet management optimization for mining complexes

This article presents a novel stochastic optimization model that simultaneously optimizes the short-term extraction sequence, shovel relocation, scheduling of a heterogeneous hauling fleet, and downstream allocation of extracted materials in open-pit mining complexes. The proposed stochastic optimization formulation considers geological uncertainty in addition to uncertainty related to equipment performances and truck cycle times. The method is applied at a real-world mining complex, stressing the benefits of optimizing the short-term production schedule and fleet management simultaneously. Compared to a conventional two-step approach, where the production schedule is optimized first before optimizing the allocation of the mining fleet, the costs generated by shovel movements are reduced by 56% and lost production due to shovel relocation is cut by 54%. Furthermore, the required number of trucks shows a more balanced profile, reducing total truck operational costs by 3.1% over an annual planning horizon, as well as the required haulage capacity in the most haulage-intense periods by 25%. A metaheuristic solution method is utilized to solve the large optimization problem in a reasonable timespan.