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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: 467; Downloads: 37
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Dynamic optimization in supply chains
Drago Pupavac, Jasmina Pašagić Škrinjar, Ratko Zelenika, 2007, original scientific article

Abstract: Production and stock optimization in supply chains is a complex problem because it determines the optimum quantity production in time. Due to their multileveld nature, those problems are mostly solved by different methods and models of dynamic programming. The problem discussed in this paper refers to the determination of optimum quantity of production and stocks within the supply chain in a certain period of time, as well as in each sub-interval of a period, but with the condition that the production and stock expenditure remains minimal and that the predetermined demand in every sub-interval and throughout the entire observation period remains satisfied.
Keywords: transportation, logistics, transportation networks, supply chains, optimization, dynamic programming, traffic
Published: 02.06.2017; Views: 389; Downloads: 53
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Monte Carlo simulation-based approach to optimal bus stops allocation in the Municipality of Laško
Dejan Dragan, Tomaž Kramberger, Martin Lipičnik, 2011, original scientific article

Abstract: The paper addresses the problem of optimal bus stop allocation. The aim is to achieve reduction of costs on account of appropriate re-design of the process of obligatory transportation of children from their homes to the corresponding schools in the Laško municipality. The proposed algorithm relies on optimization based on the Monte Carlo simulation procedure. The number of calculated bus stops is required to be minimal possible, which can still assure maximal service area within the prescribed radius, while keeping the minimal walking distances pupils have to go across from their homes to the nearest bus stop and vice versa. The main issues of the proposed algorithm are emphasised and the working mechanism is explained. The presentation of calculated results is given and comparison with some other existing algorithms is provided. The positions of the calculated bus stops are going to be used for the purpose of physical bus stops implementation in order to decrease the current transportation costs.
Keywords: location problems, maximal covering problems, optimization, Monte Carlo simulation
Published: 05.06.2017; Views: 313; Downloads: 179
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Simulation of crossroad traffic
Boris Tovornik, Drago Sever, Daniel Rogač, 2000, short scientific article

Abstract: It is difficult to find the right tool for making traffic regime models because so many different situations are involved. Simulink has proved to be one of the best. The paper shows the design and the use of components and models in the program Matlab/SIMULINK. It presents the simulation results for the model of a traffic light controlled crossroad and indicates the possibilities for the study of traffic optimisation in junctions.
Keywords: traffic, traffic light controlled crossroads, simulation, optimization
Published: 05.06.2017; Views: 262; Downloads: 39
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Resolution of the stochastic strategy spatial prisoner's dilemma by means of particle swarm optimization
Jianlei Zhang, Chunyan Zhang, Tianguang Chu, Matjaž Perc, 2011, original scientific article

Abstract: We study the evolution of cooperation among selfish individuals in the stochastic strategy spatial prisoner's dilemma game. We equip players with the particle swarm optimization technique, and find that it may lead to highly cooperative states even if the temptations to defect are strong. The concept of particle swarm optimization was originally introduced within a simple model of social dynamics that can describe the formation of a swarm, i.e., analogous to a swarm of bees searching for a food source. Essentially, particle swarm optimization foresees changes in the velocity profile of each player, such that the best locations are targeted and eventually occupied. In our case, each player keeps track of the highest payoff attained within a local topological neighborhood and its individual highest payoff. Thus, players make use of their own memory that keeps score of the most profitable strategy in previous actions, as well as use of the knowledge gained by the swarm as a whole, to find the best available strategy for themselves and the society. Following extensive simulations of this setup, we find a significant increase in the level of cooperation for a wide range of parameters, and also a full resolution of the prisoner's dilemma. We also demonstrate extreme efficiency of the optimization algorithm when dealing with environments that strongly favor the proliferation of defection, which in turn suggests that swarming could be an important phenomenon by means of which cooperation can be sustained even under highly unfavorable conditions. We thus present an alternative way of understanding the evolution of cooperative behavior and its ubiquitous presence in nature, and we hope that this study will be inspirational for future efforts aimed in this direction.
Keywords: cooperation, prisoner's dilemma, particle swarm optimization, stochastic strategies
Published: 19.06.2017; Views: 403; Downloads: 209
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Environmental management of business processes
Vesna Čančer, 2000, original scientific article

Abstract: Since the decision-makers in enterprises will accept the goals of environmental management only if they are motivated enough, comprehensible and useful tools should be generated to support environmentally oriented business decision- making. For that reason, a general optimisation model of the multiphase business process is presented in this paper. This model includes the possibilities for an integrated approach to environmental protection so that it can be applied as a scenario by the business process simulation for the evaluation of environmentally oriented business decisions on business performance. Furthermore, development and application possibilities of the presented model are introduced. Some measures of resource efficiency are developed using the presented optimisation model.
Keywords: management, environment, company, decision making, environmental protection, business, company business, optimization, business process, models
Published: 04.07.2017; Views: 301; Downloads: 56
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Mixed-integer nonlinear programming based optimal time scheduling of construction projects under nonconvex costs
Rok Cajzek, Uroš Klanšek, 2016, original scientific article

Abstract: Optimal project scheduling under nonconvex time-cost relations represents a challenging problem in construction management. The nonconvex time-cost relations may appear in a construction project when several different duration options are available for its activities due to alternative technological processes enabled for their realization or wide accessibility of production resources. The source of nonconvexity of the project scheduling optimization problem can also be the project penalty- or bonus-duration relations arranged within the construction contract. The aim of this paper is to present the mixed-integer nonlinear programming (MINLP) based optimal time scheduling of construction projects under nonconvex costs. For this purpose, the MINLP model was developed and applied. A numerical example from literature and an example of construction project time-cost trade-off analysis under practical nonconvex penalty function are given in the paper to demonstrate advantages of MINLP optimization. The example from literature first presented the capability of the MINLP approach to obtain the optimal solution for difficult, highly combinatorial nonconvex discrete project scheduling problem. Thereupon, the following example revealed that the optimal project time-cost curve may take very nonuniform shape on account of discrete nature of activity direct cost options and nonconvex relation between project duration and total cost. In this way, the presented study intends to provide practitioners with new information from the field of optimization techniques for project scheduling as well as an alternative view on performance of total cost when project duration is changed.
Keywords: extreme environments, construction management, discrete optimization, mixed-integer nonlinear programming, nonconvex costs, time scheduling
Published: 12.07.2017; Views: 385; Downloads: 231
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Optimization of machining parameters for turning operation with multiple quality characteristics using Grey relational analysis
Franko Puh, Zoran Jurković, Mladen Perinic, Miran Brezočnik, Stipo Buljan, 2016, original scientific article

Abstract: Optimization of machining processes is essential for achieving of higher productivity and high quality products in order to remain competitive. This study investigates multi-objective optimization of turning process for an optimal parametric combination to provide the minimum surface roughness (Ra) with the maximum material-removal rate (MRR) using the Grey–Based Taguchi method. Turning parameters considered are cutting speed, feed rate and depth of cut. Nine experimental runs based on Taguchi’s L9 (34) orthogonal array were performed followed by the Grey relational analysis to solve the multi- response optimization problem. Based on the Grey relational grade value, optimum levels of parameters have been identified. The significance of parameters on overall quality characteristics of the cutting process has been evaluated by the analysis of variance (ANOVA). The optimal parameter values obtained during the study have been validated by confirmation experiment.
Keywords: ANOVA, Grey relational analysis, multi-objective optimization, Taguchi method, turning
Published: 12.07.2017; Views: 334; Downloads: 222
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Prediction of dimensional deviation of workpiece using regression, ANN and PSO models in turning operation
David Močnik, Matej Paulič, Simon Klančnik, Jože Balič, 2014, original scientific article

Abstract: As manufacturing companies pursue higher-quality products, they spend much of their efforts monitoring and controlling dimensional accuracy. In the present work for dimensional deviation prediction of workpiece in turning 11SMn30 steel, the conventional deterministic approach, such as multiple linear regression and two artificial intelligence techniques, back-propagation feed-forward artificial neural network (ANN) and particle swarm optimization (PSO) have been used. Spindle speed, feed rate, depth of cut, pressure of cooling lubrication fluid and number of produced parts were taken as input parameters and dimensional deviation of workpiece as an output parameter. Significance of a single parameter and their interactive influences on dimensional deviation were statistically analysed and values predicted from regression, ANN and PSO models were compared with experimental results to estimate prediction accuracy. A predictive PSO based model showed better predictions than two remaining models. However, all three models can be used for the prediction of dimensional deviation in turning.
Keywords: artificial neural network, dimensional dviation, particle swarm optimization, regression
Published: 12.07.2017; Views: 290; Downloads: 81
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On vulnerability measures of networks
Rija Erveš, Darja Rupnik Poklukar, Janez Žerovnik, 2013, published scientific conference contribution

Abstract: As links and nodes of interconnection networks are exposed to failures, one of the most important features of a practical networks design is fault tolerance. Vulnerability measures of communication networks are discussed including the connectivities, fault diameters, and measures based on Hosoya- Wiener polynomial. An upper bound for the edge fault diameter of product graphs is proved.
Keywords: discrete optimization, communication network, vulnerability
Published: 21.07.2017; Views: 284; Downloads: 57
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