Search results for: multiple linear regression model

Authors: F. A. M. Elfaki, Amar Abobakar, M. Azram, M. Usman

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This paper discusses regression analysis of partly interval-censored failure time data, which is occur in many fields including demographical, epidemiological, financial, medical and sociological studies. For the problem, we focus on the situation where the survival time of interest can be described by the additive hazards model in the present of partly interval-censored. A major advantage of the approach is its simplicity and it can be easily implemented by using R software. Simulation studies are conducted which indicate that the approach performs well for practical situations and comparable to the existing methods. The methodology is applied to a set of partly interval-censored failure time data arising from anti D in Rhesus D negative studies.

Keywords: Anti D in Rhesus D negative, Cox’s model, EM algorithm.

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Authors: Yunyoung Nam, Junghun Ryu, Yoo-Joo Choi, We-Duke Cho

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This paper presents a novel approach for representing the spatio-temporal topology of the camera network with overlapping and non-overlapping fields of view (FOVs). The topology is determined by tracking moving objects and establishing object correspondence across multiple cameras. To track people successfully in multiple camera views, we used the Merge-Split (MS) approach for object occlusion in a single camera and the grid-based approach for extracting the accurate object feature. In addition, we considered the appearance of people and the transition time between entry and exit zones for tracking objects across blind regions of multiple cameras with non-overlapping FOVs. The main contribution of this paper is to estimate transition times between various entry and exit zones, and to graphically represent the camera topology as an undirected weighted graph using the transition probabilities.

Keywords: Surveillance, multiple camera, people tracking, topology.

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Authors: Salim Ibrir

Abstract:

Sufficient linear matrix inequalities (LMI) conditions for regularization of discrete-time singular systems are given. Then a new class of regularizing stabilizing controllers is discussed. The proposed controllers are the sum of predictive and memoryless state feedbacks. The predictive controller aims to regularizing the singular system while the memoryless state feedback is designed to stabilize the resulting regularized system. A systematic procedure is given to calculate the controller gains through linear matrix inequalities.

Keywords: Singular systems, Discrete-time systems, Regularization, LMIs

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Authors: K. Petcharaporn, S. Kumchoo

Abstract:

The acidity (citric acid) is the one of chemical content that can be refer to the internal quality and it’s a maturity index of tomato, The titratable acidity (%TA) can be predicted by a non-destructive method prediction by using the transmittance short wavelength (SW-NIR) spectroscopy in the wavelength range between 665-955 nm. The set of 167 tomato samples divided into groups of 117 tomatoes sample for training set and 50 tomatoes sample for test set were used to establish the calibration model to predict and measure %TA by partial least squares regression (PLSR) technique. The spectra were pretreated with MSC pretreatment and it gave the optimal result for calibration model as (R = 0.92, RMSEC = 0.03%) and this model obtained high accuracy result to use for %TA prediction in test set as (R = 0.81, RMSEP = 0.05%). From the result of prediction in test set shown that the transmittance SW-NIR spectroscopy technique can be used for a non-destructive method for %TA prediction of tomato.

Keywords: Tomato, quality, prediction, transmittance, titratable acidity, citric acid.

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Authors: Mahnaz Hosseinzadeh, Aliyeh Kazemi

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In this paper, a method is proposed for solving Fuzzy Multi-Objective Linear Programming problems (FMOLPP) with fuzzy right hand side and fuzzy decision variables. To illustrate the proposed method, it is applied to the problem of selecting suppliers for an automotive parts producer company in Iran in order to find the number of optimal orders allocated to each supplier considering the conflicting objectives. Finally, the obtained results are discussed.

Keywords: Fuzzy multi-objective linear programming problems, triangular fuzzy numbers, fuzzy ranking, supplier selection problem.

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Authors: Zhaoan Wang

Abstract:

Research in predictive maintenance modeling has improved in the recent years to predict failures and needed maintenance with high accuracy, saving cost and improving manufacturing efficiency. However, classic prediction models provide little valuable insight towards the most important features contributing to the failure. By analyzing and quantifying feature importance in predictive maintenance models, cost saving can be optimized based on business goals. First, multiple classifiers are evaluated with cross-validation to predict the multi-class of failures. Second, predictive performance with features provided by different feature selection algorithms are further analyzed. Third, features selected by different algorithms are ranked and combined based on their predictive power. Finally, linear explainer SHAP (SHapley Additive exPlanations) is applied to interpret classifier behavior and provide further insight towards the specific roles of features in both local predictions and global model behavior. The results of the experiments suggest that certain features play dominant roles in predictive models while others have significantly less impact on the overall performance. Moreover, for multi-class prediction of machine failures, the most important features vary with type of machine failures. The results may lead to improved productivity and cost saving by prioritizing sensor deployment, data collection, and data processing of more important features over less importance features.

Keywords: Automated supply chain, intelligent manufacturing, predictive maintenance machine learning, feature engineering, model interpretation.

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Authors: Souhir Ben Amor, Heni Boubaker, Lotfi Belkacem

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This aims of this paper is to forecast the electricity spot prices. First, we focus on modeling the conditional mean of the series so we adopt a generalized fractional -factor Gegenbauer process (k-factor GARMA). Secondly, the residual from the -factor GARMA model has used as a proxy for the conditional variance; these residuals were predicted using two different approaches. In the first approach, a local linear wavelet neural network model (LLWNN) has developed to predict the conditional variance using the Back Propagation learning algorithms. In the second approach, the Gegenbauer generalized autoregressive conditional heteroscedasticity process (G-GARCH) has adopted, and the parameters of the k-factor GARMA-G-GARCH model has estimated using the wavelet methodology based on the discrete wavelet packet transform (DWPT) approach. The empirical results have shown that the k-factor GARMA-G-GARCH model outperform the hybrid k-factor GARMA-LLWNN model, and find it is more appropriate for forecasts.

Keywords: k-factor, GARMA, LLWNN, G-GARCH, electricity price, forecasting.

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Authors: Chau-Kuang Chen, John Hughes, Jr., A. Dexter Samuels

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The prediction models for the United States Medical Licensure Examination (USMLE) Steps 1 and 2 performances were constructed by the Monte Carlo simulation modeling approach via linear regression. The purpose of this study was to build robust simulation models to accurately identify the most important predictors and yield the valid range estimations of the Steps 1 and 2 scores. The application of simulation modeling approach was deemed an effective way in predicting student performances on licensure examinations. Also, sensitivity analysis (a/k/a what-if analysis) in the simulation models was used to predict the magnitudes of Steps 1 and 2 affected by changes in the National Board of Medical Examiners (NBME) Basic Science Subject Board scores. In addition, the study results indicated that the Medical College Admission Test (MCAT) Verbal Reasoning score and Step 1 score were significant predictors of the Step 2 performance. Hence, institutions could screen qualified student applicants for interviews and document the effectiveness of basic science education program based on the simulation results.

Keywords: Prediction Model, Sensitivity Analysis, Simulation Method, USMLE.

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Authors: P. Acharjee, S. K. Goswami

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Particle Swarm Optimization (PSO) with elite PSO parameters has been developed for power flow analysis under practical constrained situations. Multiple solutions of the power flow problem are useful in voltage stability assessment of power system. A method of determination of multiple power flow solutions is presented using a hybrid of Particle Swarm Optimization (PSO) and local search technique. The unique and innovative learning factors of the PSO algorithm are formulated depending upon the node power mismatch values to be highly adaptive with the power flow problems. The local search is applied on the pbest solution obtained by the PSO algorithm in each iteration. The proposed algorithm performs reliably and provides multiple solutions when applied on standard and illconditioned systems. The test results show that the performances of the proposed algorithm under critical conditions are better than the conventional methods.

Keywords: critical conditions, ill-conditioned systems, localsearch technique, multiple power flow solutions, particle swarmoptimization.

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Authors: A. Abedian, M. H. Ghiasi, B. Dehghan-Manshadi

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A stiffened laminated composite panel (1 m length × 0.5m width) was optimized for minimum weight and deflection under several constraints using genetic algorithm. Here, a significant study on the performance of a penalty function with two kinds of static and dynamic penalty factors was conducted. The results have shown that linear dynamic penalty factors are more effective than the static ones. Also, a specially combined linear-exponential function has shown to perform more effective than the previously mentioned penalty functions. This was then resulted in the less sensitivity of the GA to the amount of penalty factor.

Keywords: Genetic algorithms, penalty function, stiffenedcomposite panel, finite element method.

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Authors: Bhekisipho Twala, Mantepu Masetshaba, Ramapulana Nkoana

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Multiple classifier systems combine several individual classifiers to deliver a final classification decision. However, an increasingly controversial question is whether such systems can outperform the single best classifier, and if so, what form of multiple classifiers system yields the most significant benefit. Also, multi-target tracking detection using multiple sensors is an important research field in mobile techniques and military applications. In this paper, several multiple classifiers systems are evaluated in terms of their ability to predict a system’s failure or success for multi-sensor target tracking tasks. The Bristol Eden project dataset is utilised for this task. Experimental and simulation results show that the human activity identification system can fulfil requirements of target tracking due to improved sensors classification performances with multiple classifier systems constructed using boosting achieving higher accuracy rates.

Keywords: Single classifier, machine learning, ensemble learning, multi-sensor target tracking.

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Authors: Ekaterina Artiukhina, Panagiotis Grammelis

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Torrefaction of biomass pellets is considered as a useful pretreatment technology in order to convert them into a high quality solid biofuel that is more suitable for pyrolysis, gasification, combustion, and co-firing applications. In the course of torrefaction, the temperature varies across the pellet, and therefore chemical reactions proceed unevenly within the pellet. However, the uniformity of the thermal distribution along the pellet is generally assumed. The torrefaction process of a single cylindrical pellet is modeled here, accounting for heat transfer coupled with chemical kinetics. The drying sub-model was also introduced. The nonstationary process of wood pellet decomposition is described by the system of non-linear partial differential equations over the temperature and mass. The model captures well the main features of the experimental data.

Keywords: Torrefaction, biomass pellets, model, heat and mass transfer.

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Authors: Abdullah Madhi Alsharif, Jamal Uddin

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A liquid curved jet has many applications in different industrial and engineering processes, such as the prilling process for generating small spherical pellets (fertilizer or magnesium). The liquids used are usually molten and contain small quantities of polymers and therefore can be modelled as non-Newtonian liquids. In this paper, we model the viscoelastic liquid jet by using the Oldroyd- B model. An asymptotic analysis has been used to simplify the governing equations. Furthermore, the trajectory and a linear temporal stability in the presence of gravity and rotation have been determined.

Keywords: gravity, prilling, rotation, viscoelastic jets.

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Authors: Chaiwat Waree

Abstract:

This research aims at development of the Multiple Intelligences Measurement of Elementary Students. The structural accuracy test and normality establishment are based on the Multiple Intelligences Theory of Gardner. This theory consists of eight aspects namely linguistics, logic and mathematics, visual-spatial relations, body and movement, music, human relations, self-realization/selfunderstanding and nature. The sample used in this research consists of elementary school students (aged between 5-11 years). The size of the sample group was determined by Yamane Table. The group has 2,504 students. Multistage Sampling was used. Basic statistical analysis and construct validity testing were done using confirmatory factor analysis. The research can be summarized as follows; 1. Multiple Intelligences Measurement consisting of 120 items is content-accurate. Internal consistent reliability according to the method of Kuder-Richardson of the whole Multiple Intelligences Measurement equals .91. The difficulty of the measurement test is between .39-.83. Discrimination is between .21-.85. 2). The Multiple Intelligences Measurement has construct validity in a good range, that is 8 components and all 120 test items have statistical significance level at .01. Chi-square value equals 4357.7; p=.00 at the degree of freedom of 244 and Goodness of Fit Index equals 1.00. Adjusted Goodness of Fit Index equals .92. Comparative Fit Index (CFI) equals .68. Root Mean Squared Residual (RMR) equals 0.064 and Root Mean Square Error of Approximation equals 0.82. 3). The normality of the Multiple Intelligences Measurement is categorized into 3 levels. Those with high intelligence are those with percentiles of more than 78. Those with moderate/medium intelligence are those with percentiles between 24 and 77.9. Those with low intelligence are those with percentiles from 23.9 downwards.

Keywords: Multiple Intelligences, Measurement, Elementary Students.

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Authors: Fatemeh Panjeh Ali Beik

Abstract:

In the present work, we propose a new method for solving the matrix equation AXB=F . The new method can be considered as a generalized form of the well-known global full orthogonalization method (Gl-FOM) for solving multiple linear systems. Hence, the method will be called extended Gl-FOM (EGl- FOM). For implementing EGl-FOM, generalized forms of block Krylov subspace and global Arnoldi process are presented. Finally, some numerical experiments are given to illustrate the efficiency of our new method.

Keywords: Matrix equations, Iterative methods, Block Krylovsubspace methods.

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Authors: C.Ramesh, V.Vaidehi

Abstract:

Ultra-wide band (UWB) communication is one of the most promising technologies for high data rate wireless networks for short range applications. This paper proposes a blind channel estimation method namely IMM (Interactive Multiple Model) Based Kalman algorithm for UWB OFDM systems. IMM based Kalman filter is proposed to estimate frequency selective time varying channel. In the proposed method, two Kalman filters are concurrently estimate the channel parameters. The first Kalman filter namely Static Model Filter (SMF) gives accurate result when the user is static while the second Kalman filter namely the Dynamic Model Filter (DMF) gives accurate result when the receiver is in moving state. The static transition matrix in SMF is assumed as an Identity matrix where as in DMF, it is computed using Yule-Walker equations. The resultant filter estimate is computed as a weighted sum of individual filter estimates. The proposed method is compared with other existing channel estimation methods.

Keywords: Channel estimation, Kalman filter, UWB, Channel model, AR model

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Authors: M. H. Kargarnovin, A. Mamandi

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This paper presents investigation effects of a sharp edged gust on aeroelastic behavior and time-domain response of a typical section model using Jones approximate aerodynamics for pure plunging motion. Flutter analysis has been done by using p and p-k methods developed for presented finite-state aerodynamic model for a typical section model (airfoil). Introduction of gust analysis as a linear set of ordinary differential equations in a simplified procedure has been carried out by using transformation into an eigenvalue problem.

Keywords: Aeroelastic response, jones approximation, pure plunging motion, sharp edged gust.

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Authors: S. Grishin

Abstract:

This article is dedicated to development of mathematical models for determining the dynamics of concentration of hazardous substances in urban turbulent atmosphere. Development of the mathematical models implied taking into account the time-space variability of the fields of meteorological items and such turbulent atmosphere data as vortex nature, nonlinear nature, dissipativity and diffusivity. Knowing the turbulent airflow velocity is not assumed when developing the model. However, a simplified model implies that the turbulent and molecular diffusion ratio is a piecewise constant function that changes depending on vertical distance from the earth surface. Thereby an important assumption of vertical stratification of urban air due to atmospheric accumulation of hazardous substances emitted by motor vehicles is introduced into the mathematical model. The suggested simplified non-linear mathematical model of determining the sought exhaust concentration at a priori unknown turbulent flow velocity through non-degenerate transformation is reduced to the model which is subsequently solved analytically.

Keywords: Urban ecology, time-dependent mathematical model, exhaust concentration, turbulent and molecular diffusion, airflow velocity.

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Authors: Raja Das, M. K. Pradhan

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This paper presents a comparative study between two neural network models namely General Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) are used to estimate radial overcut produced during Electrical Discharge Machining (EDM). Four input parameters have been employed: discharge current (Ip), pulse on time (Ton), Duty fraction (Tau) and discharge voltage (V). Recently, artificial intelligence techniques, as it is emerged as an effective tool that could be used to replace time consuming procedures in various scientific or engineering applications, explicitly in prediction and estimation of the complex and nonlinear process. The both networks are trained, and the prediction results are tested with the unseen validation set of the experiment and analysed. It is found that the performance of both the networks are found to be in good agreement with average percentage error less than 11% and the correlation coefficient obtained for the validation data set for GRNN and BPNN is more than 91%. However, it is much faster to train GRNN network than a BPNN and GRNN is often more accurate than BPNN. GRNN requires more memory space to store the model, GRNN features fast learning that does not require an iterative procedure, and highly parallel structure. GRNN networks are slower than multilayer perceptron networks at classifying new cases.

Keywords: Electrical-discharge machining, General Regression Neural Network, Back-propagation Neural Network, Radial Overcut.

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Authors: Burcu Kaya, Jan-Martin Kaiser, Karl-Friedrich Becker, Tanja Braun, Klaus-Dieter Lang

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Quality of the molded packages is strongly influenced by the process parameters of the transfer molding. To achieve a better package quality and a stable transfer molding process, it is necessary to understand the influence of the process parameters on the package quality. This work aims to comprehend the relationship between the process parameters, and to identify the optimum process parameters for the transfer molding process in order to achieve less voids and wire sweep. To achieve this, a DoE is executed for process optimization and a regression analysis is carried out. A systematic approach is represented to generate models which enable an estimation of the number of voids and wire sweep. Validation experiments are conducted to verify the model and the results are presented.

Keywords: Epoxy molding compounds, optimization, regression analysis, transfer molding process, voids, wire sweep.

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Authors: Yenni Anggrayni

Abstract:

The lack of awareness of the significant prevalence of school absenteeism in Indonesia, which ultimately results in high rates of school dropouts, is an unresolved issue. Therefore, this study aims to investigate the root causes of chronic absenteeism qualitatively and quantitatively using the bioecological systems paradigm in secondary schools for any reason. This study used an open-ended questionnaire to collect data from 1,148 students in six West Java Province districts/cities. Univariate and stepwise multiple logistic regression analyses produced a prediction model for the components. Analysis results show that poor teacher-student relationships, bullying by peers or teachers, negative perception of education, and lack of parental involvement in learning activities are the leading causes of chronic absenteeism. Another finding is to promote home-school partnerships to improve school climate and parental involvement in learning to address chronic absenteeism.

Keywords: Bullying, chronic absenteeism, dropout of school, home-school partnerships, parental involvement.

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Authors: Youngpo Lee, Jaewoo Lee, Seokho Yoon

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This paper proposes a fast code acquisition scheme for optical code division multiple access (O-CDMA) systems. Unlike the conventional scheme, the proposed scheme employs multiple thresholds providing a shorter mean acquisition time (MAT) performance. The simulation results show that the MAT of the proposed scheme is shorter than that of the conventional scheme.

Keywords: Optical CDMA, acquisition, MAT, multiple-shift

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Authors: M. Zareiee, A. Dideban, P. Nazemzadeh

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This paper deals with the problem of constructing constraints in non safe Petri Nets and then reducing the number of the constructed constraints. In a system, assigning some linear constraints to forbidden states is possible. Enforcing these constraints on the system prevents it from entering these states. But there is no a systematic method for assigning constraints to forbidden states in non safe Petri Nets. In this paper a useful method is proposed for constructing constraints in non safe Petri Nets. But when the number of these constraints is large enforcing them on the system may complicate the Petri Net model. So, another method is proposed for reducing the number of constructed constraints.

Keywords: discrete event system, Supervisory control, Petri Net, Constraint

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Authors: Yu-Ting Tsai, Yu-da Lee, Jin H. Huang

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The nonlinear damping behavior is usually ignored in the design of a miniature moving-coil loudspeaker. But when the loudspeaker operated in air, the damping parameter varies with the voice-coil displacement corresponding due to viscous air flow. The present paper presents an identification model as inverse problem to identify the nonlinear damping parameter in the lumped parameter model for the loudspeaker. Theoretical results for the nonlinear damping are verified by using laser displacement measurement scanner. These results indicate that the damping parameter has the greatly different nonlinearity between in air and vacuum. It is believed that the results of the present work can be applied in diagnosis and sound quality improvement of a miniature loudspeaker.

Keywords: Miniature loudspeaker, non-linear damping, system identification, Lumped parameter model.

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Authors: Marcello Bettoni

Abstract:

As a learning theory tries to borrow from science a framework to found its method, it shows paradoxes and paralysing contraddictions. This results, on one hand, from adopting a learning/teaching model as it were a mere “transfer of data" (mechanical learning approach), and on the other hand from borrowing the complexity theory (an indeterministic and non-linear model), that risks to vanish every educational effort. This work is aimed at describing existing criticism, unveiling the antinomic nature of such paradoxes, focussing on a view where neither the mechanical learning perspective nor the chaotic and nonlinear model can threaten and jeopardize the educational work. Author intends to go back over the steps that led to these paradoxes and to unveil their antinomic nature. Actually this could serve the purpose to explain some current misunderstandings about the real usefulness of Ict within the youth-s learning process and growth.

Keywords: Antinomy, complexity, Leibniz, paradox

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Authors: S. Chokri

Abstract:

Load modeling is one of the central functions in power systems operations. Electricity cannot be stored, which means that for electric utility, the estimate of the future demand is necessary in managing the production and purchasing in an economically reasonable way. A majority of the recently reported approaches are based on neural network. The attraction of the methods lies in the assumption that neural networks are able to learn properties of the load. However, the development of the methods is not finished, and the lack of comparative results on different model variations is a problem. This paper presents a new approach in order to predict the Tunisia daily peak load. The proposed method employs a computational intelligence scheme based on the Fuzzy neural network (FNN) and support vector regression (SVR). Experimental results obtained indicate that our proposed FNN-SVR technique gives significantly good prediction accuracy compared to some classical techniques.

Keywords: Neural network, Load Forecasting, Fuzzy inference, Machine learning, Fuzzy modeling and rule extraction, Support Vector Regression.

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Authors: Yongxin Yuan, Kezheng Zuo

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In this paper, a system of linear matrix equations is considered. A new necessary and sufficient condition for the consistency of the equations is derived by means of the generalized singular-value decomposition, and the explicit representation of the general solution is provided.

Keywords: Matrix equation, Generalized inverse, Generalized singular-value decomposition.

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Authors: A.Qaderi, A. Heydarinasab, M. Ardjmand

Abstract:

Poly-β-hydroxybutyrate (PHB) is one of the most famous biopolymers that has various applications in production of biodegradable carriers. The most important strategy for enhancing efficiency in production process and reducing the price of PHB, is the accurate expression of kinetic model of products formation and parameters that are effective on it, such as Dry Cell Weight (DCW) and substrate consumption. Considering the high capabilities of artificial neural networks in modeling and simulation of non-linear systems such as biological and chemical industries that mainly are multivariable systems, kinetic modeling of microbial production of PHB that is a complex and non-linear biological process, the three layers perceptron neural network model was used in this study. Artificial neural network educates itself and finds the hidden laws behind the data with mapping based on experimental data, of dry cell weight, substrate concentration as input and PHB concentration as output. For training the network, a series of experimental data for PHB production from Hydrogenophaga Pseudoflava by glucose carbon source was used. After training the network, two other experimental data sets that have not intervened in the network education, including dry cell concentration and substrate concentration were applied as inputs to the network, and PHB concentration was predicted by the network. Comparison of predicted data by network and experimental data, indicated a high precision predicted for both fructose and whey carbon sources. Also in present study for better understanding of the ability of neural network in modeling of biological processes, microbial production kinetic of PHB by Leudeking-Piret experimental equation was modeled. The Observed result indicated an accurate prediction of PHB concentration by artificial neural network higher than Leudeking- Piret model.

Keywords: Kinetic Modeling, Poly-β-Hydroxybutyrate (PHB), Hydrogenophaga Pseudoflava, Artificial Neural Network, Leudeking-Piret

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Authors: S. Ghorbani, N. I. Polushin

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In this study, an approach to identify factors affecting on surface roughness in a machining process is presented. This study is based on 81 data about surface roughness over a wide range of cutting tools (conventional, cutting tool with holes, cutting tool with composite material), workpiece materials (AISI 1045 Steel, AA2024 aluminum alloy, A48-class30 gray cast iron), spindle speed (630-1000 rpm), feed rate (0.05-0.075 mm/rev), depth of cut (0.05-0.15 mm) and tool overhang (41-65 mm). A single decision tree (SDT) analysis was done to identify factors for predicting a model of surface roughness, and the CART algorithm was employed for building and evaluating regression tree. Results show that a single decision tree is better than traditional regression models with higher rate and forecast accuracy and strong value.

Keywords: Cutting condition, surface roughness, decision tree, CART algorithm.

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Authors: Javier Roca, Etienne Pugnaghi, Gaëtan Libert

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We introduce an extended resource leveling model that abstracts real life projects that consider specific work ranges for each resource. Contrary to traditional resource leveling problems this model considers scarce resources and multiple objectives: the minimization of the project makespan and the leveling of each resource usage over time. We formulate this model as a multiobjective optimization problem and we propose a multiobjective genetic algorithm-based solver to optimize it. This solver consists in a two-stage process: a main stage where we obtain non-dominated solutions for all the objectives, and a postprocessing stage where we seek to specifically improve the resource leveling of these solutions. We propose an intelligent encoding for the solver that allows including domain specific knowledge in the solving mechanism. The chosen encoding proves to be effective to solve leveling problems with scarce resources and multiple objectives. The outcome of the proposed solvers represent optimized trade-offs (alternatives) that can be later evaluated by a decision maker, this multi-solution approach represents an advantage over the traditional single solution approach. We compare the proposed solver with state-of-art resource leveling methods and we report competitive and performing results.

Keywords: Intelligent problem encoding, multiobjective decision making, evolutionary computing, RCPSP, resource leveling.

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