The use of simulation analysis requires a large number of models and computational time. Reduce the calculation time in complex complex simulation and statistical modeling, allowing the implementation of parallel programming technologies in the implemented models. This paper sets the task of parallelizing the algorithmization of simulation modeling of the dynamics of a certain indicator (using the example of a model of the dynamics of cargo volume in a storage warehouse). The model is presented in the form of lines for calculating input and output flows, specified as: a moving average autoregressive model with trend components; flows of the described processes, specified according to the principle of limiting the limitation on the volume (size) of the limiting parameter, with strong stationarity of each of them. A parallelization algorithm using OpenMP technology is proposed. The efficiency indicators of the parallel algorithm are estimated: speedup, calculated as the ratio of the execution time of the sequential and parallel algorithm, and efficiency, reflecting the proportion of time that computational threads spend in calculations, and representing the ratio of the speedup to the sequential result of the processors. The dependence of the execution of the sequential and parallel algorithm on the number of simulations has been constructed. The efficiency of the parallel algorithm for the main stages of the simulation implementation was obtained at the level of 73%, the speedup is 4.38 with the number of processors 6. Computational experiments demonstrate a fairly high efficiency of the proposed parallel algorithm.

Keywords: simulation modeling, parallel programming, parallel algorithm efficiency, warehouse loading model, OpenMP technology

The work is aimed at developing and testing an algorithm for choosing the location of a new cargo storage warehouse, taking into account stochastic flows of cargo supplies to the warehouse and to consumers from the warehouse. When choosing a warehouse location, the costs that accompany the activities of a logistics company related to the organization of warehousing in the selected location, with the maintenance of the warehouse, storage of cargo, delivery of cargo from suppliers to the warehouse and from the warehouse to consumers are taken into account. The paper proposes an algorithm for solving the problem of choosing the location of a cargo storage warehouse, taking into account the forecast of the dynamics of cargo deliveries to the warehouse and to consumers from the warehouse. A mathematical toolkit is described that allows estimating the dynamics of costs for the organization and operation of a warehouse in conditions of non-stationary flows of incoming and outgoing cargo from a warehouse based on the application of the statistical modeling method. The approbation was carried out. The proposed toolkit has a novelty in terms of accounting for non-stationary flows of incoming and outgoing cargo to the warehouse and real transport routes when choosing the location of the warehouse.

Keywords: warehouse location, dynamics of warehouse costs, statistical modeling, mathematical model, logistics

An analysis of the life cycle of food products, the relationships between indicators of production, consumption, food losses, as well as the quality of life of the population will allow us to form a number of measures to reduce food losses and food waste. The paper sets the tasks of identifying the sources of food losses based on a statistical study of the level of losses in the regions of the Russian Federation and an analysis of the life cycle of a food product. The analysis of indicators of production and product losses is carried out on the example of potatoes, vegetables, fruits, meat, milk, eggs. The correlation analysis of indicators of production, consumption of products, and food losses showed that the greatest losses occur at the stages of production, processing, and sale in retail chains. Clustering of the regions of the Russian Federation according to indicators of food losses has been performed. Clusters with high production, consumption, and loss rates and with low production, consumption, and loss rates were formed for each type of food under study. A model of the life cycle of food products in BPMN 2.0 notation is constructed. The stages of the life cycle characterized by food losses have been identified. General recommendations on food loss management have been formed.

Keywords: food losses, food loss modeling, correlation analysis, cluster analysis, business process modeling

Often in practice, construction times are estimated using deterministic methods, for example, based on a network schedule of the construction plan with deterministic values for the timing of specific works. This approach does not reflect the reality associated with the probabilistic nature of risks and leads to a systematic underestimation of the time and, as a consequence, the cost of construction. The research proposes to use a Markov discrete heterogeneous Markov chain to assess the risks of non-completion of construction in due time. The states of the Markov process are proposed to correspond to the stages of construction of the object. Probabilities of system transitions from state to state are proposed to be estimated on the basis of empirical data on previously implemented projects and/or expertly, taking into account the risks characterising construction conditions in dynamics. The dynamic model of the construction plan development allows to determine such characteristics as: the probability of the construction plan realisation within the established terms, the probability that the object will ever be completed, the time of construction to the stage of completion with a given degree of reliability; unconditional probabilities of the system states (construction stage) in a given period of time relative to the beginning of construction. The model has been tested. The proposed model allows us to estimate the time of completion of construction, to assess the risks of failure to complete construction within the established deadlines in the planned conditions of construction realisation, taking into account the dynamics of risks.

Keywords: construction time, risk assessment, markov model, discrete Markov chain, inhomogeneous random process

One of the tasks of data preprocessing is the task of eliminating gaps in the data, i.e. imputation task. The paper proposes algorithms for filling gaps in data based on the method of statistical simulation. The proposed gap filling algorithms include the stages of clustering data by a set of features, classifying an object with a gap, constructing a distribution function for a feature that has gaps for each cluster, recovering missing values ​​using the inverse function method. Computational experiments were carried out on the basis of statistical data on socio-economic indicators for the constituent entities of the Russian Federation for 2022. An analysis of the properties of the proposed imputation algorithms is carried out in comparison with known methods. The efficiency of the proposed algorithms is shown.

Keywords: imputation algorithm, data gaps, statistical modeling, inverse function method, data simulation