A method for integrated process simulation in the mining industry

This paper proposes a method of Integrated Process Simulation (MIPS), which considers the dynamic, stochastic and systemic characteristics of mining operations to support investment decisions in this industry. This MIPS supports development of a Decision Support System (DSS) that considers product quality, process productivity and production costs. A case study is described that used the MIPS to make better investment decisions. The MIPS has proven, in practice, to be effective in several applications; for example, in defining the maintenance policy for critical equipment in an iron ore concentration plant; the process for removing impurities and simulating the company's budget to evaluate the viability of different business plans.     

A two-step method for forecasting spare parts demand using information on component repairs

Forecasting spare parts demand is notoriously difficult, as demand is typically intermittent and lumpy. Specialized methods such as that by Croston are available, but these are not based on the repair operations that cause the intermittency and lumpiness of demand. In this paper, we do propose a method that, in addition to the demand for spare parts, considers the type of component repaired. This two-step forecasting method separately updates the average number of parts needed per repair and the number of repairs for each type of component. The method is tested in an empirical, comparative study for a service provider in the aviation industry. Our results show that the two-step method is one of the most accurate methods, and that it performs considerably better than Croston’s method. Moreover, contrary to other methods, the two-step method can use information on planned maintenance and repair operations to reduce forecasts errors by up to 20%. We derive further analytical and simulation results that help explain the empirical findings.     

Intermittent demand: Linking forecasting to inventory obsolescence

The standard method to forecast intermittent demand is that by Croston. This method is available in ERP-type solutions such as SAP and specialised forecasting software packages (e.g. Forecast Pro), and often applied in practice. It uses exponential smoothing to separately update the estimated demand size and demand interval whenever a positive demand occurs, and their ratio provides the forecast of demand per period. The Croston method has two important disadvantages. First and foremost, not updating after (many) periods with zero demand renders the method unsuitable for dealing with obsolescence issues. Second, the method is positively biased and this is true for all points in time (i.e. considering the forecasts made at an arbitrary time period) and issue points only (i.e. considering the forecasts following a positive demand occurrence only). The second issue has been addressed in the literature by the proposal of an estimator (Syntetos-Boylan Approximation, SBA) that is approximately unbiased. In this paper, we propose a new method that overcomes both these shortcomings while not adding complexity. Different from the Croston method, the new method is unbiased (for all points in time) and it updates the demand probability instead of the demand interval, doing so in every period. The comparative merits of the new estimator are assessed by means of an extensive simulation experiment. The results indicate its superior performance and enable insights to be gained into the linkage between demand forecasting and obsolescence.     

Improved pollution forecasting hybrid algorithms based on the ensemble method

In a new fine particle concentrations forecasting model, the Hampel identifier outlier correction preprocessing detects and corrects the outliers in the original series. Empirical wavelet transform method decomposes the corrected series into a set of subseries adaptively, and each subseries are used to train the Stacking ensemble method. In the Stacking ensemble forecasting method, the outlier robust extreme learning machine meta-learner combines different Elman neural network base learners and outputs the forecasting results of different subseries. Different forecasting subseries are combined and then reconstructed by inverse empirical wavelet transform reconstruction method to get the final forecasting fine particle concentrations results. It has been proved in the study that the model proposed in the study has better accuracy and wide applicability comparing to the existing models.     

A mega-trend-diffusion grey forecasting model for short-term manufacturing demand

Accurate short-term demand forecasting is critical for developing effective production plans; however, a short forecasting period indicates that the product demands are unstable, rendering tracking of product development trends difficult. Determining the actual developing data patterns by using forecasting models generated using historical observations is difficult, and the forecasting performance of such models is unfavourable, whereas using the latest limited data for forecasting can improve management efficiency and maintain the competitive advantages of an enterprise. To solve forecasting problems related to a small data set, this study applied an adaptive grey model for forecasting short-term manufacturing demand. Experiments involving the monthly demand data for thin film transistor liquid crystal display panels and wafer-level chip-scale packaging process data showed that the proposed grey model produced favourable forecasting results, indicating its appropriateness as a short-term forecasting tool for small data sets.     

Development of improved adaptive approaches to electricity demand forecasting

This paper develops a short-term forecasting system for hourly electricity load demand based on Unobserved Components set up in a State Space framework. The system consists of two options, a univariate model and a non-linear bivariate model that relates demand to temperature. In order to handle the rapidly sampling interval of the data, a multi-rate approach is implemented with models estimated at different frequencies, some of them with ‘periodically amplitude modulated’ properties. The non-linear relation between demand and temperature is identified via a Data-Based Mechanistic approach and finally implemented by Radial Basis Functions. The models also include signal extraction of daily and weekly components. Both models are tested on the basis of a thorough experiment in which other options, like ARIMA and Artificial Neural Networks are also used. The models proposed compare very favourably with the rest of alternatives in forecasting load demand.     

Real options approach-based demand forecasting method for a range of products with highly volatile and correlated demand

To achieve a competitive edge needed for marketing highly competitive products, modern enterprises have actively sought to provide the marketplace with an expansive range of products with high random volatility of demand and correlations between demands of product. Consequently, traditional forecasting methods for separately forecasting demand for these products are likely to yield significant deviations. Therefore, this study develops a real options approach-based forecasting model to accurately predict future demand for a given range of products with highly volatile and correlated demand. Additionally, this study also proposes using Monte Carlo simulation to solve the demand forecasting model. The real options approach associated with Monte Carlo simulation not only deals effectively with random variation involving a particular demand stochastic diffusion process, but can handle the correlations in product demand.     

A wavelet smoothing method to improve conditional sales forecasting

This article proposes a wavelet smoothing method to improve conditional forecasts generated from linear regression sales response models. The method is applied to the forecasted values of the predictors to remove forecast errors and thereby improve the overall forecasting performance of the models. Eight empirical studies are presented in which the purpose was to forecast detergent sales in the Netherlands, and wavelet smoothing was compared with a moving average and a band-pass filter. All methods were found to improve forecasts. Wavelet smoothing provided the best results when applied on highly volatile marketing time series. In contrast, it was less effective when applied on highly aggregated and smooth time series. An advantage of wavelets is that they are flexible enough to allow for data characteristics like abrupt changes, spikes and cyclical changes that are usually associated with price changes and promotions.     

A hybrid metaheuritic technique developed for hourly load forecasting

Electricity load forecasting has become one of the most functioning tools in energy efficiency and load management and utility companies which has been made very complex due to deregulation. Due to the importance of providing a secure and economic electricty for the consumers, having a reliable and robust enough forecast engine in short‐term load management is very needful. Fuzzy inference system is one of primal branches of Artificial Intelligence techniques which has been widely used for different applications of decision making in complex systems. This paper aims to develop a Fuzzy inference system as a main forecast engine for Short term Load Forecasting (STLF) of a city in Iran. However, the optimization of this platform for this special case remains a basic problem. Hence, to address this issue, the Radial Movement Optimization (RMO) technique is proposed to optimize the whole Fuzzy platform. To support this idea, the accuracy of the proposed model is analyzed using MAPE index and an average error of 1.38% is obtained for the forecast load demand which represents the reliability of the proposed method. Finally, results achieved by this method, demonstrate that an adaptive two‐stage hybrid system consisting of Fuzzy & RMO can be an accurate and robust enough choice for STLF problems. © 2016 Wiley Periodicals, Inc. Complexity 21: 521–532, 2016     

A note on the two-stage hybrid flow shop problem with dedicated machines

In this paper we introduce a new dominance rule for the two-stage hybrid flow shop problem with dedicated machines. The rule is then used to construct a dominating set. The efficiency of the proposed rule is shown through an analysis of the dominating set cardinality.     

A grey-based fitting coefficient to build a hybrid forecasting model for small data sets

In the current rapidly changing manufacturing conditions, controlling manufacturing systems effectively and efficiently is a critical issue for enterprises, especially in their early stages. However, it is often difficult to make correct decisions, with the insufficient information available at such times. We thus develop a two-stage modeling procedure to build a predictive model using few samples. We first use three conventional approaches to establish forecasting models, and then implement pre-testing with the proposed grey-based fitness measuring index to determine the weights to create a hybrid model. Two datasets, including color filter manufacturing data and the Asia-Pacific Economic Cooperation energy database, are evaluated in the experiment, and the results show that the proposed method not only has good forecasting performance, but also reduces the influence forecasting errors. Accordingly, the proposed procedure is thus considered a feasible approach for small-data-set forecasting.     

A new forecasting method of discrete dynamic system

Usually, a linear differential equation is used to represent continuous dynamic systems, but a linear difference equation is used to represent discrete dynamic systems. AGO is one of the most important characteristics of grey theory, and its main purpose is to reduce the random of data. A linear differential equation, instead of a linear difference equation, is used to replace the grey differential equation to analyze discrete systems in this paper. The k-order derivatives of 1-AGO data are calculated after cubic spline interpolation of them, and the model parameters are estimated by means of the deterministic convergence scheme. ARIMA models are used to analyze the leading indicator in advance, and Fourier series with suitably chosen values of parameters is used for fitting the leading indicator. The model presented in this paper is called Grey Dynamic Model GDM(1,1,1).     

A new adaptive method for extrapolative forecasting algorithms

A quite serious problem when using time series forecasting methods is choosing the smoothing parameter (or parameters). Several methods have been developed, which employ variable, adaptively determined, smoothing factors. A new adaptive method for updating the value of smoothing parameters is introduced in this paper. The proposed model for exponential smoothing methods using one, two and three smoothing parameters is described and the accuracy of the method is measured.     

A novel hybrid multivariate nonlinear grey model for forecasting the traffic-related emissions

In this paper, we proposed a novel forecasting method using grey system theory for the traffic-related emissions at a national level. In our tests, grey relational analysis was used to identify time lags between input and output variables. We introduced a multivariate nonlinear grey model based on the kernel method to improve the accuracy of traffic-related emissions prediction. By solving a convex optimization problem instead of using an ordinary least squares estimation, the proposed model overcame the limitations of the classic grey forecasting models. A model confidence set test on the realistic results of forecasting traffic-related emissions in European Union member countries showed that the proposed model demonstrated a marked superiority over robust linear regression and support vector regression. Based on the non-methane volatile organic compounds from road transport and the relevant factors of the emission from 2004 to 2016, a more stringent European Union emission reduction commitment to the road transport for each year from 2020 to 2029 was suggested. We also investigated the advantages of the proposed model via the analysis on convergence, robustness, and sensitivity.     

A new approach of forecasting intermittent demand for spare parts inventories in the process industries

Accurate demand forecasting is of vital importance in inventory management of spare parts in process industries, while the intermittent nature makes demand forecasting for spare parts especially difficult. With the wide application of information technology in enterprise management, more information and data are now available to improve forecasting accuracy. In this paper, we develop a new approach for forecasting the intermittent demand of spare parts. The described approach provides a mechanism to integrate the demand autocorrelated process and the relationship between explanatory variables and the nonzero demand of spare parts during forecasting occurrences of nonzero demands over lead times. Two types of performance measures for assessing forecast methods are also described. Using data sets of 40 kinds of spare parts from a petrochemical enterprise in China, we show that our method produces more accurate forecasts of lead time demands than do exponential smoothing, Croston's method and Markov bootstrapping method.     

A hybrid method for sound-hard obstacle reconstruction

We are interested in solving the inverse problem of acoustic wave scattering to reconstruct the position and the shape of sound-hard obstacles from a given incident field and the corresponding far field pattern of the scattered field. The method we suggest is an extension of the hybrid method for the reconstruction of sound-soft cracks as presented in [R. Kress, P. Serranho, A hybrid method for two-dimensional crack reconstruction, Inverse Problems 21 (2005) 773–784] to the case of sound-hard obstacles. The designation of the method is justified by the fact that it can be interpreted as a hybrid between a regularized Newton method applied to a nonlinear operator equation with the operator that maps the unknown boundary onto the solution of the direct scattering problem and a decomposition method in the spirit of the potential method as described in [A. Kirsch, R. Kress, On an integral equation of the first kind in inverse acoustic scattering, in: Cannon, Hornung (Eds.), Inverse Problems, ISNM, vol. 77, 1986, pp. 93–102. Since the method does not require a forward solver for each Newton step its computational costs are reduced. By some numerical examples we illustrate the feasibility of the method.     

A hybrid graph-genetic method for domain decomposition

A new method is developed for finite element (FE) domain decomposition. This method employs a hybrid graph-genetic algorithm for graph partitioning and correspondingly bisects finite element (FE) meshes.

A weighted incidence graph is first constructed for the FE mesh, denoted by G₀. A coarsening process is then performed using heavy-edge matching. A sequence of such operations is employed in “n” steps, which leads to the formation of G_n with a size suitable for genetic algorithm applications.

Hereafter, G_n is bisected using conventional genetic algorithm. The shortest route tree algorithm is used for the formation of the initial population in genetic algorithm. Then an uncoarsening process is performed and the results are transferred to the graph G_n−1. The initial population for genetic algorithm on G_n−1is constructed from the results of G_n. This process is repeated until G₀ is obtained in the uncoarsening operation. Employing the properties of G₁, the graph G₀ is bisected by the genetic algorithm.     

A hybrid method for computing Lyapunov exponents

In this paper we propose a numerical method for computing all Lyapunov coefficients of a discrete time dynamical system by spatial integration. The method extends an approach of Aston and Dellnitz (Comput Methods Appl Mech Eng 170:223–237, 1999) who use a box approximation of an underlying ergodic measure and compute the first Lyapunov exponent from a spatial average of the norms of the Jacobian for the iterated map. In the hybrid method proposed here, we combine this approach with classical QR-oriented methods by integrating suitable R-factors with respect to the invariant measure. In this way we obtain approximate values for all Lyapunov exponents. Assuming somewhat stronger conditions than those of Oseledec’ multiplicative theorem, these values satisfy an error expansion that allows to accelerate convergence through extrapolation. W.-J. Beyn and A. Lust was supported by CRC 701 ‘Spectral Analysis and Topological Methods in Mathematics’. The paper is mainly based on the PhD thesis [27] of A. Lust.     

A hybrid method for quantum global optimization

This paper gives a quantum algorithm for global optimization. The heart of such approaches employ Grover’s database search (1996; Phys Rev Lett 79(23):4709–4712, 1997a; 79(2):325–328, 1997b). Chi and Kim (1998) show that when the phases of the generalized Grover database search operator are optimally chosen, it is capable of finding a solution by a single query. To apply this method to global optimization requires knowledge of the number of marked points m to calculate the optimal phases, but this value is seldom known. This paper focuses on overcoming this hurdle by showing that an estimate of the optimal phases can be found and used to replace the optimal phases while maintaining a high probability of finding a solution. Merging this finding with a recently discovered dynamic quantum global optimization algorithm (BBW2D) that reduces the problem to finding successively improving regions using Grover’s search, we present a hybrid method that improves the efficiency and reduces the variance of the search algorithm when empirically compared to other existing quantum search algorithms.