A topical task of developing optimization algorithms for the parametric synthesis of the supersonic passenger transport (SST) aerodynamic configuration under epistemic uncertainty (lack of sufficient knowledge to determine input parameters of the aerodynamic calculation) is addressed in the paper. These algorithms are proposed to be used when the values of the initial calculation parameters are set by experts due to the lack of accurate or statistical data about the design object that is a fuzzy formulation. To solve such a problem, Liu's uncertainty theory was chosen, which provides computational efficiency when performing optimization calculations and allows using existing deterministic algorithms such as Black-Box Models when calculating objective and restriction functions. Solving the problem of parametric synthesis of the SST aerodynamic configuration in a fuzzy formulation, we propose the optimization models, the schemes for designing and using algorithms based on these models and present the results of computational studies. Proposed method application is considered to a generalized SST.

In this paper authors present simulation model of an alloptical network, consisting of dual 4 × 4 switches. The analytical model of the considered all-optical network is a two-phase queuing system with a limitation on the shared buffer size. Authors have provided numerical results of system states probabilities including packet loss probability in transient mode, depending on the ratio of service and arrival rates, and also transient time. A comparison of analytical and simulation modeling is also carried out.

Предложена математическая модель гетерогенной мультимодальной системы передачи данных, особенностью которой является рассмотрение марковски-модулированных пуассоновских потоков в качестве входных потоков модальности, каждый из которых управляется одной марковской цепью с конечным числом состояний, заданных матрицей бесконечно малых характеристик. Каждая входная модальность определенного типа обслуживается в течение случайного времени по экспоненциальному закону распределения вероятностей. Построенная модель была исследована численно и сделаны выводы о перспективных направлениях дальнейших исследований.

The goal of this article is to analyze a repairable double hot standby system equipped with a single repair facility, employing Marked Markov Processes. It is assumed that elements’ life- and repair times follow arbitrary distributions. The proposed approach facilitates the calculation of the system’s reliability characteristics and allows investigating their sensitivity to the shape of input distributions. Building on prior research that investigated the reliability function and mean system lifetime, the current article focuses on analyzing the steady-state probabilities of the system. For this, two system repair scenarios are considered. To validate the new method, numerical examples that are compared to previously obtained analytical results, demonstrating a high degree of accuracy, are presented. Additionally, further numerical experiments illustrate the sensitivity of steady-state reliability measures to the initial parameters of the system.

In the current paper, a retrial tandem queueing system consisting of two stations in series is investigated. Each station is represented by a single server and a limited size buffer. Customers arrive at the first station according to a Markovian Arrival Process (MAP). The service time at the first and the second server has a Phase type (P H) distribution. The novelty of the model under consideration is the presence of a common orbit for blocked customers both at the first and second stations. Unlike other few studies of retrial tandem systems with a common orbit, our model is more general and we obtain analytical results using matrix-analytic technique. We derive the sufficient conditions for existence and absence of the stationary regime in the system, calculate the steady-state distribution of the number of customers in the orbit and at the stations and derive formulas for the most significant performance measures.