Lane’s Algorithm Revisited

2020 ◽  
Author(s):  
Marcos Goycoolea ◽  
Patricio Lamas ◽  
Bernardo K. Pagnoncelli ◽  
Adriana Piazza
Keyword(s):  
Optimality Conditions ◽  
Continuous Time ◽  
Mining Industry ◽  
Optimal Solution ◽  
Open Pit ◽  
Mixed Integer ◽  
Software Systems ◽  
Real World Data ◽  
Data Set ◽  
Processed Material

In 1964, Kenneth Lane proposed an algorithm to optimize the production schedule of a single-metal, single-processor open pit mine. For this, he proposed a policy based on varying, over time, the so-called “cutoff grade”—or grade threshold used to determine if extracted material should be ore (processed material) or waste (thrown away). Lane’s algorithm had a profound impact on the mining industry. However, though it has been used in multiple commercial software systems and has traditionally been taught to every aspiring mining engineer, it is widely considered a heuristic, and little is known regarding the quality of the solutions it produces. In this paper, we formally study Lane’s problem. We show that Lane’s algorithm can be viewed as an approximate dynamic programming scheme and that Lane’s optimality conditions can be formally derived in two different ways: by considering a variant of the problem where the future value function is linearly approximated or by deriving the optimality conditions of a continuous-time version of the problem. We further show that Lane’s algorithm can naturally be extended to this continuous-time version of the problem and that when this algorithm converges, it converges to an optimal solution. Finally, through a reformulation, we show that Lane’s original problem can be solved using convex mixed-integer programming. Though hypothetical counterexamples can be constructed, computational experiments prove that Lane’s algorithm can produce the optimal solution in every real-world data set tested, thereby lending solid support for its practical application. This paper was accepted by Chung Piaw Teo, optimization.

Coal mining-disturbed land reclamation laws

2020 ◽  
pp. 15-26
Author(s):  
T. V. Galanina ◽  
M. I. Baumgarten ◽  
T. G. Koroleva
Keyword(s):  
Large Scale ◽  
Land Reclamation ◽  
Mining Industry ◽  
Development Program ◽  
Soil Disturbance ◽  
Open Pit ◽  
Open Pit Mining ◽  
Disturbed Land ◽  
Critical Problems ◽  
Production Output

Large-scale mining disturbs wide areas of land. The development program for the mining industry, with an expected considerable increase in production output, aggravates the problem with even vaster territories exposed to the adverse anthropogenic impact. Recovery of mining-induced ecosystems in the mineral-extracting regions becomes the top priority objective. There are many restoration mechanisms, and they should be used in integration and be highly technologically intensive as the environmental impact is many-sided. This involves pollution of water, generation of much waste and soil disturbance which is the most typical of open pit mining. Scale disturbance of land, withdrawal of farming land, land pollution and littering are critical problems to the solved in the first place. One of the way outs is highquality reclamation. This article reviews the effective rules and regulations on reclamation. The mechanism is proposed for the legal control of disturbed land reclamation on a regional and federal level. Highly technologically intensive recovery of mining-induced landscape will be backed up by the natural environment restoration strategy proposed in the Disturbed Land Reclamation Concept.

Optimal Consumption and Insurance: A Continuous-time Markov Chain Approach

2008 ◽  
Vol 38 (01) ◽  
pp. 231-257 ◽  
Author(s):  
Holger Kraft ◽  
Mogens Steffensen
Keyword(s):  
Continuous Time ◽  
Optimal Solution ◽  
Decision Problems ◽  
Optimal Consumption ◽  
Continuous Time Markov Chain ◽  
Financial Decision Making ◽  
Markov Chain Approach ◽  
Special Cases ◽  
Financial Decision ◽  
The Individual

Personal financial decision making plays an important role in modern finance. Decision problems about consumption and insurance are in this article modelled in a continuous-time multi-state Markovian framework. The optimal solution is derived and studied. The model, the problem, and its solution are exemplified by two special cases: In one model the individual takes optimal positions against the risk of dying; in another model the individual takes optimal positions against the risk of losing income as a consequence of disability or unemployment.

scholarly journals A GIS Software Module for Environmental Impact Assessment of the Open Pit Mining Projects for Small Mining Operators in Kazakhstan

2021 ◽  
Vol 13 (12) ◽  
pp. 6971
Author(s):  
Mikhail Zarubin ◽  
Larissa Statsenko ◽  
Pavel Spiridonov ◽  
Venera Zarubina ◽  
Noune Melkoumian ◽  
...  
Keyword(s):  
Environmental Impact ◽  
Impact Assessment ◽  
Environmental Impact Assessment ◽  
Mining Industry ◽  
Open Pit ◽  
Open Pit Mining ◽  
Software Module ◽  
Mining Operations ◽  
Open Pit Mines ◽  
Gis Software

This research article presents a software module for the environmental impact assessment (EIA) of open pit mines. The EIA software module has been developed based on the comprehensive examination of both country-specific (namely, Kazakhstan) and current international regulatory frameworks, legislation and EIA methodologies. EIA frameworks and methods have been critically evaluated, and mathematical models have been developed and implemented in the GIS software module ‘3D Quarry’. The proposed methodology and software module allows for optimised EIA calculations of open pit mines, aiming to minimise the negative impacts on the environment. The study presents an original methodology laid out as a basis for a software module for environmental impact assessment on atmosphere, water basins, soil and subsoil, tailored to the context of mining operations in Kazakhstan. The proposed software module offers an alternative to commercial off-the-shelf software packages currently used in the mining industry and is suitable for small mining operators in post-Soviet countries. It is anticipated that applications of the proposed software module will enable the transition to sustainable development in the Kazakh mining industry.

Auto-sharing parameters for transfer learning based on multi-objective optimization

2021 ◽  
pp. 1-13
Author(s):  
Hailin Liu ◽  
Fangqing Gu ◽  
Zixian Lin
Keyword(s):  
Transfer Learning ◽  
Optimization Problem ◽  
Data Sets ◽  
Multi Objective Optimization ◽  
Particle Swarm Optimizer ◽  
Real World Data ◽  
Data Set ◽  
Target Task ◽  
Main Research ◽  
Multi Objective

Transfer learning methods exploit similarities between different datasets to improve the performance of the target task by transferring knowledge from source tasks to the target task. “What to transfer” is a main research issue in transfer learning. The existing transfer learning method generally needs to acquire the shared parameters by integrating human knowledge. However, in many real applications, an understanding of which parameters can be shared is unknown beforehand. Transfer learning model is essentially a special multi-objective optimization problem. Consequently, this paper proposes a novel auto-sharing parameter technique for transfer learning based on multi-objective optimization and solves the optimization problem by using a multi-swarm particle swarm optimizer. Each task objective is simultaneously optimized by a sub-swarm. The current best particle from the sub-swarm of the target task is used to guide the search of particles of the source tasks and vice versa. The target task and source task are jointly solved by sharing the information of the best particle, which works as an inductive bias. Experiments are carried out to evaluate the proposed algorithm on several synthetic data sets and two real-world data sets of a school data set and a landmine data set, which show that the proposed algorithm is effective.

Optimization of the Receipt and Delivery Schedules for a Transfer Tank Farm of Products Pipeline Network

2017 ◽  
Author(s):  
Li Wang ◽  
Changchun Wu ◽  
Lili Zuo ◽  
Yanfei Huang ◽  
Haihong Chen
Keyword(s):  
Material Balance ◽  
Optimal Solution ◽  
Planning Horizon ◽  
Oil Products ◽  
Mixed Integer ◽  
Pipeline Network ◽  
Schedule Optimization ◽  
Tank Farm ◽  
Dynamic Time ◽  
Products Pipeline

Transfer tank farms play an important role in an oil products pipeline network, which receive oil products from upstream pipelines and deliver them to downstream pipelines. The scheduling problem for oil products supply chain is very complicated because of numerous constraints to be considered. The published literatures on schedule optimization of oil products pipeline network usually focus on the batch plans of each pipeline, without consideration on the receipt and delivery schedule of transfer tank farm. In this paper, a mixed-integer linear programming (MILP) model is developed for the schedule optimization of transfer tank farm. The objective of the model is to minimize switching times of the tank operations of a tank farm during a planning horizon, while fulfilling the products transmission requirements of the upstream and downstream pipelines of the tank farm. The constraints of the model include material balance, the operational rules of tanks, the topological structure constraints of the tank farm, the settling period of the oil products stored in dedicated tank and so on. To satisfy the constraint of fulfilling the specific transmission requirements of pipelines, concepts of static and dynamic time slot are proposed. A continuous time representation is used to obtain accurate optimal schedules and decrease scale of the model by reducing the number of variables. The model is solved by CPLEX solver for a transfer tank farm of an oil products pipeline network in China. Some examples are tested under different scenarios and the results show that global optimal solution can be obtain at acceptable computational costs.

scholarly journals Time-ResNeXt for epilepsy recognition based on EEG signals in wireless networks

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Shaoqiang Wang ◽  
Shudong Wang ◽  
Song Zhang ◽  
Yifan Wang
Keyword(s):  
Deep Learning ◽  
Network Structure ◽  
Signal Recognition ◽  
Eeg Signals ◽  
Real World Data ◽  
Data Set ◽  
Practical Applications ◽  
Epilepsy Diagnosis ◽  
Single Data ◽  
Electroencephalogram Eeg

Abstract To automatically detect dynamic EEG signals to reduce the time cost of epilepsy diagnosis. In the signal recognition of electroencephalogram (EEG) of epilepsy, traditional machine learning and statistical methods require manual feature labeling engineering in order to show excellent results on a single data set. And the artificially selected features may carry a bias, and cannot guarantee the validity and expansibility in real-world data. In practical applications, deep learning methods can release people from feature engineering to a certain extent. As long as the focus is on the expansion of data quality and quantity, the algorithm model can learn automatically to get better improvements. In addition, the deep learning method can also extract many features that are difficult for humans to perceive, thereby making the algorithm more robust. Based on the design idea of ResNeXt deep neural network, this paper designs a Time-ResNeXt network structure suitable for time series EEG epilepsy detection to identify EEG signals. The accuracy rate of Time-ResNeXt in the detection of EEG epilepsy can reach 91.50%. The Time-ResNeXt network structure produces extremely advanced performance on the benchmark dataset (Berne-Barcelona dataset) and has great potential for improving clinical practice.

Rapid video assessment for monitoring testing facility fraud

2018 ◽  
Vol 35 (8) ◽  
pp. 1508-1518
Author(s):  
Rosembergue Pereira Souza ◽  
Luiz Fernando Rust da Costa Carmo ◽  
Luci Pirmez
Keyword(s):  
Processing Time ◽  
Threshold Value ◽  
Real World Data ◽  
Data Set ◽  
Content Type ◽  
Video Assessment ◽  
Technical Requirements ◽  
Testing Facility ◽  
Rapid Processing ◽  
Temporal Differencing

Purpose The purpose of this paper is to present a procedure for finding unusual patterns in accredited tests using a rapid processing method for analyzing video records. The procedure uses the temporal differencing technique for object tracking and considers only frames not identified as statistically redundant. Design/methodology/approach An accreditation organization is responsible for accrediting facilities to undertake testing and calibration activities. Periodically, such organizations evaluate accredited testing facilities. These evaluations could use video records and photographs of the tests performed by the facility to judge their conformity to technical requirements. To validate the proposed procedure, a real-world data set with video records from accredited testing facilities in the field of vehicle safety in Brazil was used. The processing time of this proposed procedure was compared with the time needed to process the video records in a traditional fashion. Findings With an appropriate threshold value, the proposed procedure could successfully identify video records of fraudulent services. Processing time was faster than when a traditional method was employed. Originality/value Manually evaluating video records is time consuming and tedious. This paper proposes a procedure to rapidly find unusual patterns in videos of accredited tests with a minimum of manual effort.

scholarly journals Modern Solution for Fast and Accurate Inventorization of Open-Pit Mines by the Active Remote Sensing Technique—Case Study of Mikoszów Granite Mine (Lower Silesia, SW Poland)

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6853
Author(s):  
Jaroslaw Wajs ◽  
Paweł Trybała ◽  
Justyna Górniak-Zimroz ◽  
Joanna Krupa-Kurzynowska ◽  
Damian Kasza
Keyword(s):  
Computer Software ◽  
Mining Industry ◽  
Ground Surface ◽  
Open Pit ◽  
Mining Operations ◽  
Buffer Zones ◽  
Sw Poland ◽  
The Impact ◽  
Cloud Of Points ◽  
Lower Silesia

Mining industry faces new technological and economic challenges which need to be overcome in order to raise it to a new technological level in accordance with the ideas of Industry 4.0. Mining companies are searching for new possibilities of optimizing and automating processes, as well as for using digital technology and modern computer software to aid technological processes. Every stage of deposit management requires mining engineers, geologists, surveyors, and environment protection specialists who are involved in acquiring, storing, processing, and sharing data related to the parameters describing the deposit, its exploitation and the environment. These data include inter alia: geometries of the deposit, of the excavations, of the overburden and of the mined mineral, borders of the support pillars and of the buffer zones, mining advancements with respect to the set borders, effects of mining activities on the ground surface, documentation of landslide hazards and of the impact of mining operations on the selected elements of the environment. Therefore, over the life cycle of a deposit, modern digital technological solutions should be implemented in order to automate the processes of acquiring, sharing, processing and analyzing data related to deposit management. In accordance with this idea, the article describes the results of a measurement experiment performed in the Mikoszów open-pit granite mine (Lower Silesia, SW Poland) with the use of mobile LiDAR systems. The technology combines active sensors with automatic and global navigation system synchronized on a mobile platform in order to generate an accurate and precise geospatial 3D cloud of points.

scholarly journals Location Routing Problem with Consideration of CO2 Emissions Cost: A Case Study

2020 ◽  
Vol 21 (2) ◽  
pp. 225-234
Author(s):  
Ananda Noor Sholichah ◽  
Y Yuniaristanto ◽  
I Wayan Suletra
Keyword(s):  
Co2 Emissions ◽  
Time Windows ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Distribution Centers ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Routing ◽  
Study Results

Location and routing are the main critical problems investigated in a logistic. Location-Routing Problem (LRP) involves determining the location of facilities and vehicle routes to supply customer's demands. Determination of depots as distribution centers is one of the problems in LRP.  In LRP, carbon emissions need to be considered because these problems cause global warming and climate change. In this paper, a new mathematical model for LRP considering CO2 emissions minimization is proposed. This study developed a new  Mixed Integer Linear Programming (MILP)  model for LRP with time windows and considered the environmental impacts.  Finally, a case study was conducted in the province of Central Java, Indonesia. In this case study, there are three depot candidates. The study results indicated that using this method in existing conditions and constraints provides a more optimal solution than the company's actual route. A sensitivity analysis was also carried out in this case study.

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