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Organizacija
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2010
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vol. 43
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issue 3
102-112
EN
The design of strategies for social systems requires the use of qualitative information owing to the fact that quantitative information can be insufficient to solve the problems involved. The information that the specialists and the decision makers obtain is often incomplete and unreliable. Nevertheless, leaders have to make strategic decisions despite these deficiencies which should be based on the formal models (Kljajić et al. 2000; Škraba et al, 2003; Škraba et al 2007).This paper describes a methodology elaborated to design the strategy of the city of Santa Cruz (on the Canary Islands). It has two main sections: the elaboration of a qualitative model and the use of System Dynamics. We combine them in a way that allows mixing qualitative and quantitative information to achieve a better understanding of the structure of the region, to know the tendencies of the present scenario and to estimate of the effects of alternative strategic decisions. We have obtained these results working with scarce quantitative information. This methodology may be applied to any social systems with similar characteristics.
EN
Background and Purpose: The restructuring of human resources in an organization is addressed in this paper, because human resource planning is a crucial process in every organization. Here, a strict hierarchical structure of the organization is of concern here, for which a change in a particular class of the structure influences classes that follow it. Furthermore, a quick adaptation of the structure to the desired state is required, where oscillations in transitions between classes are not desired, because they slow down the process of adaptation. Therefore, optimization of such a structure is highly complex, and heuristic methods are needed to approach such problems to address them properly. Design/Methodology/Approach: The hierarchical human resources structure is modeled according to the principles of System Dynamics. Optimization of the structure is performed with an algorithm that combines stochastic local search and genetic algorithms. Results: The developed algorithm was tested on three scenarios; each scenario exhibits a different dynamic in achieving the desired state of the human resource structure. The results show that the developed algorithm has successfully optimized the model parameters to achieve the desired structure of human resources quickly. Conclusion: We have presented the mathematical model and optimization algorithm to tackle the restructuring of human resources for strict hierarchical organizations. With the developed algorithm, we have successfully achieved the desired organizational structure in all three cases, without the undesired oscillations in the transitions between classes and in the shortest possible time.
EN
Background: The sugar beet is the main field crop used for sugar production in the temperate climatic zone. The abolishment of the quota system will open new investment opportunities in countries that were forced to abandon sugar industry as the result of the reform in 2006. Present paper describes the modeling of sugar beet production and its processing into sugar for purpose of decision support. Methods: A system dynamics methodology was chosen to model impacts of regional sugar factory investment. We present two basic concepts of system dynamics models at causal loop diagram level. The first holistic model deals with regional planning of new product development and the second one deals with factory model. Results: The holistic model presented main feedback loops and dynamics of main elements in the case of regional investment into sugar industry. The factory model considered the specifics of the beet processing which is a) limited period of beet processing and b) initial adjustment to the production capacity at the start of the production season Conclusions: The model seeks answers to strategic questions related to the whole sugar beet production and processing system and will be used for simulation of different scenarios for sugar production and their impact on economic and environmental parameters at an aggregate level.
Organizacija
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2015
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vol. 48
|
issue 3
177-186
EN
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.
EN
This paper presents the system dynamics model of organic farming development in order to support decision making. The model seeks answers to strategic questions related to the level of organically utilized area, levels of production and crop selection in a long-term dynamic context. The model will be used for simulation of different policy scenarios for organic farming and their impact on economic and environmental parameters of organic production at an aggregate level. Using the model, several policy scenarios were performed.
EN
We describe a GIS-based real-time system for emergency response and management of air pollution accidents in an urban area. The system architecture emphasises the integration of meteorological, chemical and GIS data, dispersion modeling, decision-making and geo-spatial visualization. The threat zones, unsafe areas and safe traffic routes are obtained using an improved Gaussian plume model with a decision-making module and then exported to the Google Earth browser via “kml” file format. Several simulation scenarios were conducted and verified for notable industrial sites in Montenegro using recorded meteorological data. The results demonstrate that emergency response authorities can use the proposed methodology and system as a cost effective and accurate support tool in case of industrial or deliberate air pollution incidents.
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