Open pit mining of rock minerals and the affected areas requiring further development are a serious challenge for shaping the positive image of the mining industry among the public. The direction and method of post-mining land reclamation are important for this image, which should take into account various factors describing the mining area, including social preferences. The article presents an example solution – fuzzy system (FSDR) – which supports the selection of the direction of reclamation of post-mining areas created after the termination of operations of open pit gravel and sand natural aggregate mines. The article presents selected factors determining the selection of the direction and possible reclamation variants as input and output data of the fuzzy system. The rules base of the developed system, as well as the mechanisms of inference and defuzzification, were also characterized. The application of the developed system is presented on selected examples.
The model is developed for the intellectualized decision-making support system on financing of cyber security means of transport cloud-based computing infrastructures, given the limited financial resources. The model is based on the use of the theory of multistep games tools. The decision, which gives specialists a chance to effectively assess risks in the financing processes of cyber security means, is found. The model differs from the existing approaches in the decision of bilinear multistep quality games with several terminal surfaces. The decision of bilinear multistep quality games with dependent movements is found. On the basis of the decision for a one-step game, founded by application of the domination method and developed for infinite antagonistic games, the conclusion about risks for players is drawn. The results of a simulation experiment within program implementation of the intellectualized decision-making support system in the field of financing of cyber security means of cloudbased computing infrastructures on transport are described. Confirmed during the simulation experiment, the decision assumes accounting a financial component of cyber defense strategy at any ratios of the parameters, describing financing process.
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.
Complex structural engineering projects that involve information-gathering and decision-makingprocesses need to be approached with appropriate systems and tools. As transactional databasesare found to be insufficient for this purpose, engineers are adopting multidimensional informationsystems that have been successfully used in other areas of management, especially business.
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.