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1.
Web application for hierarchical organizational structure optimization
Davorin Kofjač, Blaž Bavec, Andrej Škraba, 2015, original scientific article

Abstract: Background and Purpose: In a complex strictly hierarchical organizational structure, undesired oscillations may occur, which have not yet been adequately addressed. Therefore, parameter values, which define fluctuations and transitions from one state to another, need to be optimized to prevent oscillations and to keep parameter values between lower and upper bounds. The objective was to develop a simulation model of hierarchical organizational structure as a web application to help in solving the aforementioned problem. Design/Methodology/Approach: The hierarchical structure was modeled according to the principles of System Dynamics. The problem of the undesired oscillatory behavior was addressed with deterministic finite automata, while the flow parameter values were optimized with genetic algorithms. These principles were implemented as a web application with JavaScript/ECMAScript. Results: Genetic algorithms were tested against well-known instances of problems for which the optimal analytical values were found. Deterministic finite automata was verified and validated via a three-state hierarchical organizational model, successfully preventing the oscillatory behavior of the structure. Conclusion: The results indicate that the hierarchical organizational model, genetic algorithms and deterministic finite automata have been successfully implemented with JavaScript as a web application that can be used on mobile devices. The objective of the paper was to optimize the flow parameter values in the hierarchical organizational model with genetic algorithms and finite automata. The web application was successfully used on a three-state hierarchical organizational structure, where the optimal flow parameter values were determined and undesired oscillatory behavior was prevented. Therefore, we have provided a decision support system for determination of quality restructuring strategies.
Keywords: hierarchical organizational structure, genetic algorithms, deterministic finite automata, system dynamics, optimization, human resources
Published: 04.04.2017; Views: 846; Downloads: 82
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2.
A numerical simulation of metal injection moulding
Boštjan Berginc, Miran Brezočnik, Zlatko Kampuš, Borivoj Šuštaršič, 2009, original scientific article

Abstract: Metal injection moulding (MIM) is already a well-established and promising technology for the mass production of small, complex, near-net-shape products. The dimensions and mechanical properties of MIM products are influenced by the feedstock characteristics, the process parameters of the injection moulding, as well as the debinding and the sintering. Numerical simulations are a very important feature of the beginning of any product or technology development. In the article two different techniques for measuring the rheological properties of MIM feedstocks are presented and compared. It was established that capillary rheometers are more appropriate for MIM feed stocks, while on the other hand, parallel-plate rheometers are only suitable for shear rates lower than 10 s[sup]{-1}. Later on we used genetic algorithms to determine the model coefficients for some numerical simulation software. The results of the simulation of the filling phase and a comparison with the experimental results are presented in the article.
Keywords: metal injection moulding, numerical simulation, genetic algorithms
Published: 14.03.2017; Views: 716; Downloads: 124
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3.
Development of a web application for dynamic production scheduling in small and medium enterprises
Davorin Kofjač, Andrej Knaflič, Miroljub Kljajić, 2010, original scientific article

Abstract: This article describes the development of a web-based dynamic job-shop scheduling system for small and medium enterprises. In large enterprises, scheduling is mainly performed with appropriate technology by human experts; many small and medium enterprises lack the resources to implement such a task. The main objective was to develop a cost-effective, efficient solution for job-shop scheduling in small and medium enterprises with an emphasis on accessibility, platform independence and ease of use. For these reasons, we decided to develop a web-based solution with the main emphasis on the development of an intelligent and dynamic user interface. The solution is built upon modular programming principles and enables dynamic scheduling on the basis of artificial intelligence, i.e. genetic algorithms. The solution has been developed as a standalone information system, which allows the management of virtually all scheduling activities through an administration panel. In addition, the solution covers the five main functionalities that completely support the scheduling process, i.e. making an inventory of resources available in the company, using it in the process of production planning, collecting data on production activities, distribution of up-to-date information and insight over events in the system.
Keywords: dynamic job-shop scheduling, genetic algorithms, web application development
Published: 10.07.2015; Views: 1098; Downloads: 329
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4.
AGRA: analysis of gene ranking algorithms
Simon Kocbek, Rune Saetre, Gregor Štiglic, Jin-Dong Kim, Igor Pernek, Yoshimasa Tsuruoka, Peter Kokol, Sophia Ananiadou, Jun-ichi Tsujii, 2011, short scientific article

Abstract: Often, the most informative genes have to be selected from different gene setsand several computer gene ranking algorithms have been developed to cope with the problem. To help researchers decide which algorithm to use, we developed the Analysis of Gene Ranking Algorithms (AGRA) system that offers a novel technique for comparing ranked lists of genes. The most important feature of AGRA is that no previous knowledge of gene ranking algorithms is needed for their comparison. Using the text mining system FACTA (Tsuruoka et al., 2008), AGRA defines what we call Biomedical Concept Space (BCS) for each gene list and offers comparison of the gene lists in six different BCS categories. The uploaded gene lists can be compared using two different methods. In the first method, the overlap between each pair of two gene lists of BCSs is calculated. The second method offers a text field where specific biomedical concept can be entered. AGRA searches for this concept in each genelistsć BCS, highlights the rank of the concept and offers a visual representation of concepts ranked above and below it.
Keywords: genetic algorithms, genetski algoritmi
Published: 05.06.2012; Views: 1526; Downloads: 101
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5.
Predicting defibrillation success by "genetic" programming in patients with out-of-hospital cardiac arrest
Matej Podbregar, Miha Kovačič, Aleksandra Podbregar-Marš, Miran Brezočnik, 2003, original scientific article

Abstract: In some patients with ventricular fibrillation (VF) there may be a better chance of successful defibrillation after a period of chest compression and ventilation before the defibrillation attempt. It is therefore important to know whether a defibrillation attempt will be successful. The predictive powerof a model developed by "genetic" programming (GP) to predict defibrillation success was studied. Methods and Results: 203 defibrillations were administered in 47 patients with out-of-hospital cardiac arrest due to a cardiac cause. Maximal amplitude, a total energy of power spectral density, and the Hurst exponent of the VF electrocardiogram (ECG) signal were included in the model developed by GP. Positive and negative likelihood ratios of the model for testing data were 35.5 and 0.00, respectively. Using a model developed by GP on the complete database, 120 of the 124 unsuccessful defibrillations would have been avoided, whereas all of the 79 successful defibrillations would have been administered. Conclusion: The VF ECG contains information predictive of defibrillation success. The model developed by GP, including data from the time-domain, frequency-domain and nonlinear dynamics, could reduce the incidence of unsuccessful defibrillations.
Keywords: optimisation methods, evolutionary optimisation methods, genetic algorithms, genetic programming, defibrillation, cardiac arrest prediction
Published: 01.06.2012; Views: 1296; Downloads: 76
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6.
A model of data flow in lower CIM levels
Igor Drstvenšek, Ivo Pahole, Jože Balič, 2004, original scientific article

Abstract: After years of work in fields of computer-integrated manufacturing (CIM), flexible manufacturing systems (FMS), and evolutionary optimisation techniques, several models of production automation were developed in our laboratories. The last model pools the discoveries that proved their effectiveness in the past models. It is based on the idea of five levels CIM hierarchy where the technological database (TDB) represents a backbone of the system. Further on the idea of work operation determination by an analyse of the production system is taken out of a model for FMS control system, and finally the approach to the optimisation of production is supported by the results of evolutionary based techniques such as genetic algorithms and genetic programming.
Keywords: computer integrated manufacturing, flexible manufacturing systems, evolutionary optimisation techniques, production automation, CIM hierarchy, technological databases, production optimisation, genetic algorithms, genetic programming
Published: 01.06.2012; Views: 1479; Downloads: 79
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7.
Designing the layout of single- and multiple-rows flexible manufacturing system by genetic algorithms
Mirko Ficko, Miran Brezočnik, Jože Balič, 2004, original scientific article

Abstract: The paper presents a model of designing of the flexible manufacturing system (FMS) in one or multiple rows with genetic algorithms (GAs). First the reasons for studying the layout of devices in the FMS are discussed. After studying the properties of the FMS and perusing the methods of layout designing the genetic algorithms methods was selected as the most suitable method for designing the FMS. The genetic algorithm model, the most suitable way of coding the solutions into the organisms and the selected evolutionary and genetic operators are presented. In the model, the automated guided vehicles (AGVs) for transport between components of the FMS were used. In this connection, the most favourable number of rows and the sequence of devices in the individual row are established by means of genetic algorithms. In the end the test results of the application made and the analysis are discussed.
Keywords: flexible manufacturing systems, facility layout, optimization, genetic algorithms
Published: 01.06.2012; Views: 1324; Downloads: 77
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8.
A model of simulation environment for prediction and optimisation of production processes
Igor Drstvenšek, Mirko Ficko, Ivo Pahole, Jože Balič, 2004, original scientific article

Abstract: Paper describes means and methods for computer based optimisation of production processes using a new approach based on technological database (TDB) with genetic algorithm incorporated into a database management system (DBMS). The TDB serves as a store of tools and machine tools from which they can be assigned to different work operations. Work operations are basic entities of orders placed into queues. The goal of the model is to find available resources from the TDB in order to empty the queue in shortest time with lowest costs. To this purpose the model consist the technological database whose DBMS includes a genetic algorithm based optimiser. It checks the orders queue and searches for appropriate combinations of tools and machine tools from the TDB, which can be combined into needed work operations. It also performs an optimisation of time and costs according to so called static parameters of tools and machine tools.
Keywords: production processes, simulation, process planning, technological databases, genetic algorithms
Published: 01.06.2012; Views: 1364; Downloads: 78
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9.
Evolutionary programming of CNC machines
Miha Kovačič, Miran Brezočnik, Ivo Pahole, Jože Balič, Borut Kecelj, 2005, original scientific article

Abstract: The paper proposes a new concept for programming of CNC machines. The concept based on genetic algorithms assures evolutionary generation and optimization of NC programs on the basis of CAD models of manufacturing environment. The structure, undergoing simulated evolution, is the population of NC programs. The NC programs control the machine which performs simple elementary motions. During the evolution the machine movement becomes more and more complex and intelligent solutions emerge gradually as a result of the interaction between machine movements and manufacturing environment. The examples of evolutionary programming of CNC lathe and CNC milling machine tool for different complexities of the blanks and products are presented. The proposed concept showed a high degree of universality, efficiency, and reliability and it can be also simply adopted to other CNC machines.
Keywords: manufacturing systems, NC-programming, CNC lathes, simulated evolution, genetic algorithms
Published: 01.06.2012; Views: 1232; Downloads: 89
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10.
Prediction of maintenance of sinus rhythm after electrical cardioversion of atrial fibrillation by non-deterministic modelling
Petra Žohar, Miha Kovačič, Miran Brezočnik, Matej Podbregar, 2005, original scientific article

Abstract: Atrial fibrillation (AF) is the most common rhythm disorder. Because of the high recurrence rate of AF after cardioversion and because of potential side effects of electrical cardioversion, it is clinically important to predict persistence of sinus rhythm after electrical cardioversion before it is attempted. The aim of our study was the development of a mathematical model by"genetic" programming (GP), a non-deterministic modelling technique, which would predict maintenance of sinus rhythm after electrical cardioversion of persistent AF. PATIENTS AND METHODS: Ninety-seven patients with persistent AF lasting more than 48 h, undergoing the first attempt at transthoracic cardioversion were included in this prospective study. Persistence of AF before the cardioversion attempt, amiodarone treatment, left atrial dimension,mean, standard deviation and approximate entropy of ECG R-R intervals were collected. The data of 53 patients were randomly selected from the database and used for GP modelling; the other 44 data sets were used for model testing. RESULTS: In 23 patients sinus rhythm persisted at 3 months. In the other 21 patients sinus rhythm was not achieved or its duration was less than 3 months. The model developed by GP failed to predict maintenance ofsinus rhythm at 3 months in one patient and in six patients falsely predicted maintenance of sinus rhythm. Positive and negative likelihood ratiosof the model for testing data were 4.32 and 0.05, respectively. Using this model 15 of 21 (71.4%) cardioversions not resulting in sinus rhythm at 3 months would have been avoided, whereas 22 of 23 (95.6%) cardioversions resulting in sinus rhythm at 3 months would have been administered. CONCLUSION: This model developed by GP, including clinical data, ECG data from the time-domain and nonlinear dynamics can predict maintenance of sinus rhythm. Further research is needed to explore its utility in the present or anexpanded form.
Keywords: optimisation methods, evolutionary optimisation methods, genetic algorithms, genetic programming, defibrillation, cardiac arrest prediction, atrial fibrillation, electrical cardioversion, prediction
Published: 01.06.2012; Views: 1613; Downloads: 63
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