Designing customer-oriented catalogs in e-CRM using an effective self-adaptive genetic algorithm

Analysis of customer interactions for electronic customer relationship management (e-CRM) can be performed by way of using data mining (DM), optimization methods, or combined approaches. The microeconomic framework for data mining addresses maximizing the overall utility of an enterprise where transaction of a customer is a function of the data available on that customer. In this paper, we investigate an alternative problem formulation for the catalog segmentation problem. Moreover, a self-adaptive genetic algorithm has been developed to solve the problem. It includes clever features to avoid getting trapped in a local optimum. The results of an extensive computational study using real and synthetic data sets show the performance of the algorithm. In comparison with classical catalog segmentation algorithms, the proposed approach achieves better performance in Fitness and CPU-time.     

An Efficient Solution for Model Checking Graph Transformation Systems

This paper presents an efficient solution for modeling checking graph transformation systems. The approach transforms AGG specifications into Bogor models and supports both attributed typed graphs and layered transformations. Resulting models are amenable to check interesting properties expressed as combinations of LTL (Linear Temporal Logic) and graph transformation rules. The first experimental results are encouraging and show that in most cases our proposal improves existing approaches, both in terms of performance and expressiveness.     

A Wavelet-ANFIS Hybrid Model for Groundwater Level Forecasting for Different Prediction Periods

Artificial neural network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) have an extensive range of applications in water resources management. Wavelet transformation as a preprocessing approach can improve the ability of a forecasting model by capturing useful information on various resolution levels. The objective of this research is to compare several data-driven models for forecasting groundwater level for different prediction periods. In this study, a number of model structures for Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Wavelet-ANN and Wavelet-ANFIS models have been compared to evaluate their performances to forecast groundwater level with 1, 2, 3 and 4 months ahead under two case studies in two sub-basins. It was demonstrated that wavelet transform can improve accuracy of groundwater level forecasting. It has been also shown that the forecasts made by Wavelet-ANFIS models are more accurate than those by ANN, ANFIS and Wavelet-ANN models. This study confirms that the optimum number of neurons in the hidden layer cannot be always determined by using a specific formula but trial-and-error method. The decomposition level in wavelet transform should be determined according to the periodicity and seasonality of data series. The prediction of these models is more accurate for 1 and 2 months ahead (for example RMSE?=?0.12, E?=?0.93 and R ²?=?0.99 for wavelet-ANFIS model for 1 month ahead) than for 3 and 4 months ahead (for example RMSE?=?2.07, E?=?0.63 and R ²?=?0.91 for wavelet-ANFIS model for 4 months ahead).     

Circular Functions Based Comprehensive Analysis of Plastic Creep Deformations in the Fiber Reinforced Composites

Analytically based model is presented for behavioral analysis of the plastic deformations in the reinforced materials using the circular (trigonometric) functions. The analytical method is proposed to predict creep behavior of the fibrous composites based on basic and constitutive equations under a tensile axial stress. New insight of the work is to predict some important behaviors of the creeping matrix. In the present model, the prediction of the behaviors is simpler than the available methods. Principal creep strain rate behaviors are very noteworthy for designing the fibrous composites in the creeping composites. Analysis of the mentioned parameter behavior in the reinforced materials is necessary to analyze failure, fracture, and fatigue studies in the creep of the short fiber composites. Shuttles, spaceships, turbine blades and discs, and nozzle guide vanes are commonly subjected to the creep effects. Also, predicting the creep behavior is significant to design the optoelectronic and photonic advanced composites with optical fibers. As a result, the uniform behavior with constant gradient is seen in the principal creep strain rate behavior, and also creep rupture may happen at the fiber end. Finally, good agreements are found through comparing the obtained analytical and FEM results.     

Applying artificial optimization methods for transformer model reduction of lumped parameter models

Detailed R-C-L-M models of power transformers, which are based on lumped parameters, are used extensively not only for transient analysis of power transformers to determine electrical stresses in windings, but also for studying transients in power systems. Models with few elements are generally more practicable for power system studies but at the expense of accuracy. The use of artificial methods to reduce an R-C-L-M model is the main contribution of this paper. Advantages of the suggested method include: (1) a reduced loss of accuracy compared with the original model and (2) the flexibility to choose the number of model elements to achieve the desired model depending on size and accuracy. The ability of three different artificial methods, genetic algorithm, particle swarm optimization, and bacterial foraging algorithm, to model reduction is evaluated using measurements on an actual 400 kV test object and the results are compared with those obtained by common analytical formulae.     

A multiobjective integrated multiproject scheduling and multiskilled workforce assignment model considering learning effect under uncertainty

Today, organizations try to decline academically expenses using humans and resources in addition to rising managers and operators' satisfaction. Meantime, a very important step in the process of decision is the assignment of human resources, particularly in connection with research and development (R&D) projects in which the system is highly dependent on the capabilities of human resources. In this study, we tried all the assumptions that come true in the real world, considered a model for applied R&D projects to reduce costs and increase the efficiency of projects. Therefore, an integrated multiproject scheduling and multiskill human resource assignment model under uncertainty has developed for R&D projects. Furthermore, it is assumed that the activity processing time is related to human resources assignment that means the learning effect is considered. To demonstrate the proposed model efficiency, the various dimensions instance problem was solved accurately and efficiently in GAMS software, and the results have been reported. In addition, the proposed model is validated through the input parameter sensitivity analysis. The results indicate a suitable performance of the proposed fuzzy mathematical programming model is due to the complexity of the problem.     

First-principles DFT study of SO2 and SO3 adsorption on 2PANI: a model for polyaniline response

Interaction of SO_x (x?=?2,3) molecules on active sites of dianiline (as a model for polyaniline, denoted here as 2PANI) was studied using density functional theory at the BLYP-D/6-31+G(d) level of theory. Natural population analysis was used to find out the charge distribution as well as the net transferred charge of SO_x upon adsorption on 2PANI and the result has been compared with Mulliken charge analysis to evaluate the sensing ability of 2PANI. The computed density of states point to the remarkable orbital hybridization between SO_x and 2PANI during the adsorption process. As a consequence, the results of UV–VIS confirm the sensing ability of 2PANI toward SO₂ and SO₃. Based on our results, it can be found that at proper configuration the SO₂ and SO₃ molecules can be adsorbed on 2PANI with adsorption energies (E_ads) of ?18.2 and ?62.9?kJ/mol (BSSE), respectively.     

Multi carrier energy systems and energy hubs: Comprehensive review,survey and recommendations

In this paper, the multi carrier energy (MCE) systems are reviewed from different point of views including mathematical models, integrated components and technologies, uncertainty management, planning objectives, environmental pollution, resilience, and robustness. The basic of MCE systems is formed by combination of cooling, heating and power (CCHP). The natural gas and electricity are the main inputs to MCE systems and the cooling, heating, and electricity are the common outputs. The regular energy converters in the MCE systems are combined heat and power (CHP), gas boiler, absorption-electrical chillers, power to gas (P2G) and fuel-cell. The generic energy storages are electrical, heating, cooling, hydrogen, carbon dioxide (CO₂) and hydro systems.     

A new mathematical model for integrating all incidence matrices in multi-dimensional cellular manufacturing system

In the past several years, many studies have been carried out on cellular manufacturing based on a two-dimensional machine–part incidence matrix. Since workers have important role in doing jobs on machines, assignment of workers to cells becomes a crucial factor for fully utilization of cellular manufacturing systems. In this paper, an attempt is made to solve cell formation problem and minimize the number of voids and exceptional elements in a three dimensional (cubic) machine–part–worker incidence matrix. The proposed mathematical model captures the capability of workers in doing different jobs. To demonstrate the effectiveness of the proposed model, the solution of some test problems is compared with the literature method.