Integrated Parametric Graph Closure and Branch-and-Cut Algorithm for Open Pit Mine Scheduling under Uncertainty

Open pit mine production scheduling is a computationally expensive large-scale mixed-integer linear programming problem. This research develops a computationally efficient algorithm to solve open pit production scheduling problems under uncertain geological parameters. The proposed solution approach for production scheduling is a two-stage process. The stochastic production scheduling problem is iteratively solved in the first stage after relaxing resource constraints using a parametric graph closure algorithm. Finally, the branch-and-cut algorithm is applied to respect the resource constraints, which might be violated during the first stage of the algorithm. Six small-scale production scheduling problems from iron and copper mines were used to validate the proposed stochastic production scheduling model. The results demonstrated that the proposed method could significantly improve the computational time with a reasonable optimality gap (the maximum gap is 4%). In addition, the proposed stochastic method is tested using industrial-scale copper data and compared with its deterministic model. The results show that the net present value for the stochastic model improved by 6% compared to the deterministic model

Suspended sediment yield modeling in Mahanadi River, India by multi-objective optimization hybridizing artificial intelligence algorithms

River sediment produced through weathering is one of the principal landscape modification processes on earth. Rivers are an integral part of the hydrologic cycle and are the major geologic agents that erode the continents and transport water and sediments to the oceans. Estimation of suspended sediment yield is always a key parameter for planning and management of any river system. It is always challenging to model sediment yield using traditional mathematical models because they are incapable of handling the complex non-linearity and non-stationarity. The suspended sediment modeling of the river depends on the number of factors such as rock type, relief, rainfall, temperature, water discharge and catchment area. In this study, we proposed a hybrid genetic algorithm-based multi-objective optimization with artificial neural network (GA-MOO-ANN) with automated parameter tuning model using these factors to estimate the suspended sediment yield in the entire Mahanadi River basin. The model was validated by comparing statistically with other models, and it appeared that the GA-MOO-ANN model has the lowest root mean squared error (0.009) and highest coefficient of correlation (0.885) values among all comparative models (traditional neural network, multiple linear regression, and sediment rating curve) for all stations. It was also observed that the proposed model is the least biased (0.001) model. Thus, the proposed GA-MOO-ANN is the most capable model, compared to other studied models, for estimating the suspended sediment yield in the entire Mahanadi river basin, India. The results also suggested that the proposed GA-MOO-ANN model is unable to estimate suspended sediment yield satisfactorily at gauge stations having very small catchment areas whereas performing satisfactorily on locations having moderate to the large catchment area. The models provide the best result at Tikarapara, the gauge station location in the extreme downstream, having the largest catchment area

Limestone quarry production planning for consistent supply of raw materials to cement plant: A case study from Indian cement industry with a captive quarry

A long term production planning of limestone quarry is presented to supply consistent quantity and quality of limestone to a cement plant. A case study from Indian cement industry is presented where the cement plant has a captive limestone quarry. The objectives of this study are: (a) to investigate how long the limestone quarry can alone supply the desire quality and quantity materials to the cement plant; and (b) to investigated the possibility of extending the quarry life by utilizing some quantity of the limestone from the different source and blending that limestone with the limestone from the quarry to achieve the target quality and quantity of the cement plant. These objectives are achieved by generating the production sequencing of the mining blocks using a sequential branch-and-cut algorithm. The results revealed that up to 15 years, the existing quarry alone can serve the cement plant. If certain quantity of limestone can be brought from the other sources, the life of the study quarry is significantly improved. The life of the quarry increased from 15 years to 85 years. The study also helps to calculate the desire quality of the limestone that will be brought from other sources throughout the life of the quarry

Suspended sediment yield estimation using genetic algorithm-based artificial intelligence models: case study of Mahanadi River, India

The estimation of sediment yield is important in design, planning and management of river systems. Unfortunately, its accurate estimation using traditional methods is difficult as it involves various complex processes and variables. This investigation deals with a hybrid approach which comprises genetic algorithm-based artificial intelligence (GA-AI) models for the prediction of sediment yield in the Mahanadi River basin, India. Artificial neural network (ANN) and support vector machine (SVM) models are developed for sediment yield prediction, where all parameters associated with the models are optimized using genetic algorithms simultaneously. Water discharge, rainfall and temperature are used as input to develop the GA-AI models. The performance of the GA-AI models is compared to that of traditional AI models (ANN and SVM), multiple linear regression (MLR) and sediment rating curve (SRC) method for evaluating the predictive capability of the models. The results suggest that GA-AI models exhibit better performance than other models

Prediction of suspended sediment yield by artificial neural network and traditional mathematical model in Mahanadi river basin, India

© 2017, Springer International Publishing AG. Estimation of sediment yield is essential towards understanding the mass balance between the ocean and land. Direct measurement of suspended sediment is difficult as it needs sufficient time and money. The suspended sediment yield depends on a number of variables, and their inter-relationships are highly non-linear and complex in nature. In this paper, soft computing-based sediment yield estimation algorithms are proposed for the Mahanadi river basin. A multilayer perceptron (MLP) artificial neural network (ANN) with an error back-propagation algorithm using historical monthly hydro-climatic data (temperature, water discharge and rainfall) was employed to predict the suspended sediment yield at the Tikarapara gauging station, which is the farthest downstream station in the Mahanadi river. The results demonstrated that water discharge and rainfall are significant controlling parameters of suspended sediment in the Mahanadi River. The comparative results show that the feed-forward back-propagation with Levenberg–Marquardt (FFBP–LM) is the best model for suspended sediment yield estimation, and provides more reasonable prediction for extremely high and low values. The performance of the sediment rating curve (SRC) model was below expectations as it produced the least accurate results for the peak sediment values, as well as overall model performance. It is also noticed that the multiple linear regressions (MLR) model predicted negative sediment yield at low values; which is completely unrealistic as suspended sediment yield cannot be negative in nature. It was also observed that suspended yield prediction by ANN was superior compared to that using MLR and SRC models. The proposed model will be beneficial for sediment prediction where estimates of suspended sediment values are unavailable