In the field experiments performed in 1999–2001 the profitability of late blight control in accordance with three decision support systems: NegFry, Simphyt and Stephan with routine fungicide program was compared. Potato protection carried out according to the recommendations of the decision support systems guaranteed higher profitability of late blight control than when potato was protected routinely. The highest profitability was recorded for susceptible variety Bekas protected according to NegFr.
Cross-docking is a strategy that distributes products directly from a supplier or manufacturing plant to a customer or retail chain, reducing handling or storage time. This study focuses on the truck scheduling problem, which consists of assigning each truck to a door at the dock and determining the sequences for the trucks at each door considering the time-window aspect. The study presents a mathematical model for door assignment and truck scheduling with time windows at multi-door cross-docking centers. The objective of the model is to minimize the overall earliness and tardiness for outbound trucks. Simulated annealing (SA) and tabu search (TS) algorithms are proposed to solve large-sized problems. The results of the mathematical model and of meta-heuristic algorithms are compared by generating test problems for different sizes. A decision support system (DSS) is also designed for the truck scheduling problem for multi-door cross-docking centers. Computational results show that TS and SA algorithms are efficient in solving large-sized problems in a reasonable time.
The suitability of a land plot in a real estate market could be identified as a good investment because the land plot is deemed as popular. This activity is important for economic growth, who is one of the sustainable development goals. Mostly, all research in this field is focused on sustainability as well as the opinions of professionals. However, this field should be explored from another side which is based on real geodata. Criteria and its weight are very important in decision support systems. The correct criteria can help in selection of the best real estate object for an investment, but it is not only useful but also and a challenging task that has not yet been solved. The methods of research are data graphical analysis, correlation, decision supporting systems, etc. The research aims at determining the significance of the connections and using them as the criteria in the selected decision supporting method. In addition, it will be determined which decision supporting method defines the most suitable object for investment. These new criteria are proposed for operation in the land use models. Furthermore, it has been identified as one criterion, which is significant in the urban and agrarian territories. Also it turned out, that the land plot is the most active when it is as far from a densely built-up residential territory as possible and as close to a school, and when the land plot is as large as possible.
The paper features a comprehensive approach to risk management worked out during the ValueSec project (EU 7th Framework Programme). The motivation for research was presented, along with the course of the research, achieved project results and validation results. The methodology of risk management and a supporting tool were developed as a result of the project. They help decision makers to make complex strategic decisions about security measures. These complex decision-related problems were the reason to launch the research. The elaborated methodology is based on three pillars: assessment of the considered security measure ability to reduce risk, costs and benefits analysis with respect to the security measure application, and analysis of legal, social, cultural, and other restrictions that might impair or even destroy the efficiency of the functioning measures. In the project these restrictions are called qualitative criteria. The main added value of the ValueSec project is the elaboration of a special software module to analyse impacts of qualitative criteria on the considered measure. Based on the methodology, a ValueSec Toolset prototype was developed. The prototype was then validated in the following application domains: mass event, railway transport security, airport and air transport security, protection against flood, and protection of smart grids against cyber-attacks.
The article herein presents the method and algorithms for forming the feature space for the base of intellectualized system knowledge for the support system in the cyber threats and anomalies tasks. The system being elaborated might be used both autonomously by cyber threat services analysts and jointly with information protection complex systems. It is shown, that advised algorithms allow supplementing dynamically the knowledge base upon appearing the new threats, which permits to cut the time of their recognition and analysis, in particular, for cases of hard-to-explain features and reduce the false responses in threat recognizing systems, anomalies and attacks at informatization objects. It is stated herein, that collectively with the outcomes of previous authors investigations, the offered algorithms of forming the feature space for identifying cyber threats within decisions making support system are more effective. It is reached at the expense of the fact, that, comparing to existing decisions, the described decisions in the article, allow separate considering the task of threat recognition in the frame of the known classes, and if necessary supplementing feature space for the new threat types. It is demonstrated, that new threats features often initially are not identified within the frame of existing base of threat classes knowledge in the decision support system. As well the methods and advised algorithms allow fulfilling the time-efficient cyber threats classification for a definite informatization object.
This article discusses the results of studies using the developed artificial neural networks in the analysis of the occurrence of the four main mechanisms destroying the selected forging tools subjected to five different surface treatment variants (nitrided layer, pad welded layer and three hybrid layers, i.e. AlCrTiSiN, Cr/CrN and Cr/AlCrTiN). Knowledge of the forging tool durability, needed in the process of artificial neural network training, was included in the set of training data (about 800 records) derived from long-term comprehensive research carried out under industrial conditions. Based on this set, neural networks with different architectures were developed and the results concerning the intensity of the occurrence of thermal-mechanical fatigue, abrasive wear, mechanical fatigue and plastic deformation were generated for each type of the applied treatment relative to the number of forgings, pressure, friction path and temperature.