The market of telecommunications services is one of the most important and promising sectors of Russian economics, and its development has an essential impact on development strategies of all industries. In recent times, we observe a tendency for the operators’ business to shift from providing communications services to supplying integrated ICT services. A positive trend line of market growth is predicted for the coming five years. However, the problem of keeping and even expanding the subscriber base is an ongoing task of all telecom companies. One of the possible solutions to this problem is developing a rational tariff policy, which may take into consideration not only the interests of the company and its investors, but also the subscribers’ preferences. One of the main components of the tariff policy is developing new tariff plans, which meet the afore-mentioned requirements.
In the paper, a new concept of tariff plan development is proposed. It is based on identifying stable groups of existing tariff plans and subscribers’ preferences that are non-linearly related with tariff plan characteristics. The proposed method is based on the concept of client lifetime value (CLV) that characterizes discounted profit received from a customer during all the time he consumes services from the company. This approach gives us an opportunity to build-up a CLV forming model, relying on subscriber’s consumption of mobile services and price characteristics of tariff plans. This seems quite important in the conditions of volatility of the high tech market and intensive changes in patterns of subscribers’ consumption of services.
Within the proposed concept, an info-logical model for developing and evaluating a new tariff plan is developed. The model is based on the synthesis of neural networks and genetic algorithm. The proposed model allows us to make assessment of combinations of tariff plans’ price characteristics created by telecom company specialists, and to determine an optimal combination representing local or global maximum of CLV in the given time interval. This may be done for each subscriber’s consumption profile and for the given period.
The approach gives us an opportunity to choose a tariff plan (from existing and newly created tariffs) for every subscriber cluster, which satisfies subscribers and investor preferences while providing maximum company profit.
This article presents a new approach to designing decision-making systems for socio-economic and ecological planning using parallel real-coded genetic algorithms (RCGAs), aggregated with simulation models by objective functions. A feature of this approach is the use of special agent-processes, which are autonomous genetic algorithms (GAs) acting synchronously in parallel streams and exchanging periodically by the best potential decisions. This allows us to overcome the premature convergence problem in local extremums. In addition, it was shown that the combined use of different crossover and mutation operators significantly improves the time efficiency of RCGAs, as well as the quality of the decisions obtained (proximity to optimum), providing a more diverse population of potential decisions (individuals). In this paper, several suggested crossover and mutation operators are used, in particular, a modified simulated binary crossover (MSBX) and scalable uniform mutation operator (SUM), which is based on quantization of the feasible region of the search space (dividing the feasible region on small subranges with equal lengths) while taking into account the common amount of interacting agent-processes and the maximum number of internal iterations of GAs forming potential decisions through selection, crossover and mutation. Such a functional dependence of the parameters of heuristic operators on the corresponding process characteristics, aggregated with the combined probabilistic use of various crossover and mutation operators, makes it possible to get maximum effect from the multi-processes architecture. As a result, the computational possibilities of RCGAs for solving large-scale optimization problems (hundreds and thousands of decision variables, multiple objective functions) become dependent only on the physical characteristics of the existing computing clusters. This makes it possible to efficiently use supercomputer technologies. An important advantage of the proposed system is the implemented integration between the developed parallel RCGA (implemented in C++ and MPI) and the simulation modelling system AnyLogic (Java) using JNI technology. Such an approach allows one to synthesize real world optimization problems in decision-making systems of socio-economic and ecological planning, using simulation methods supported by AnyLogic. The result is an effective solution to singleobjective and multi-objective optimization tasks of large dimension, in which the objective functionals are the result of simulation modeling and cannot be obtained analytically.
Digital transformation is a highly topical task for many companies. Implementation and use of breakthrough technologies are an essential part of this process. Nowadays the terms “innovation” and “information technologies (IT)” are treated as equals insofar as IT is exactly what can provide execution of innovative strategy and the digital transformation of a company’s business. Due to the high speed of IT market growth and the emergence of new technologies, companies usually implement them without justified selection and prioritization, and this leads to the high rate of failed innovative IT projects. Often such projects fail to result in commercially successful products or services by which a company can distinguish itself from competitors to consumers. Still the most widespread approach for evaluation and ranking of innovative IT projects concentrates on the expected financial outcomes without due attention to strategic alignment of a project. This research suggests an approach for ranking innovative IT projects in big companies. The approach entails complex evaluation of expected results of projects on the strategic, environmental, organizational and technological domains of a company. This approach is based on a modified Tornyatzky–Fleischer IT innovation adoption model. During the first stage of research, the term and definition of innovation have been discussed as well as features of innovative IT projects. The second stage is dedicated to comparison analysis of evaluation approaches for innovative projects as well as to choosing an IT adoption model for further adaptation. On the third stage approbation of the method developed been carried out in one of the Russian big IT/internet companies. The results of two-year period of approach approbation have proved its suitability and suggested the prospects for further development.
In the article is presented a novel approach to the solution of multi-objective optimizing problems of large-scale dimension systems realized, in particular, in the simulation systems of the class AnyLogic through distributed calculations. A new concept of creation of the distributed evolutionary network is suggested, based on splitting of space of required variables into clusters and assignments isЯкорь offered to each computing element of a network of the cluster on which search of intermediate results by means of interacting genetic algorithms is carried out.
The ar ti cle de scribes the fea tures of the or gani za tion and func tion ing of edu cational re sources in Asian cul tures on the ex am ple of China in the light of socioculturalspecificity, discursive features, ergonomic design parameters. The articleconsiders the general features of the national information and education environmentin China.
THE ROLE OF THE SUBJECTIVITY IN BUSINESS PROCESSES
The article is devoted to the research of the subjectivity, the subjective structure and the reflectiveness in the business-process management of the company. This research was made with the financial support of the government of the Russian Federation (Ministry of education and science of Russia) in frames of the contract №13.G25.31.0096 about “The creation of high technological production of cross-platform systems for processing the non-structured information on the base of freeware for increasing the efficiency of company innovation activity management in modern Russia”
To solve the problem of reduction of the multidimensional vector of indicators methods of factor analysis are used. One of them is the maximum likelihood method (MLM). It allows to identify uncorrelated common factors among the set of correlated quantitative indicators. The uncorrelated common factors can represent initial indicators without significant loss of information. Detection of the common factors is carried out using a special representation of the correlation matrix of the observed indicators. However, the correlation coefficient is not defined for the characteristics measured in nominal scale. In addition, it can not serve as a measure for the strength of the coupling indicators with nonlinear dependence. Traditional methods of factor analysis are ineffective for such situations. Two modifications of the MLM are proposed in the paper. They use the rank Spearman correlation coefficients and Cramer coefficients as measures of relationship between variables. With the help of the Monte Carlo method 12-dimensional vectors with their coordinates dependent on each other with linear and nonlinear dependency were simulated. Then, a comparative analysis of the effectiveness of the traditional MLM and the two proposed modifications of the MLM was carried out for these data. It is shown that only adapted method that uses the Cramer coefficients is able to combine correctly indicators related with nonmonotonic dependency in common factor. On the other hand, this method has a lower efficiency than the other two methods in cases where the dependency between variables is linear or monotonic. To demonstrate the efficiency of these methods on real data the task of reducing the dimension of the dynamics of the relative consumer price growth in the years 2008-2014 for a group of food products has been solved.
One of the most dynamically changing parts of the labor market relates to information technologies. Skillsets demanded by employers in this sphere vary across different industries, organizations and even certain vacancies. The educational system in the most cases lags behind such changes, so that obsolete skillsets are being taught. This article proposes an algorithm of skillsets identification that allows us to extract skills that are needed by companies from different occupational groups in the information technologies sector. Using the unstructured online-vacancies database for the Russian regional labor market, skills are extracted and unified with the use of TF-IDF and n-grams approaches. As a result, key specific skillsets for various occupations are found. The proposed algorithm allows us to identify and standardize key skills which might be applicable to create a system of Russian classification for occupations and skills. In addition, the algorithm allows us to provide lists of the key combinations of skills that are in high demand among companies inside each particular occupation.
This paper is devoted to comparison of the capabilities of various methods to predict the bankruptcy of construction industry companies on a one-year horizon. The authors considered the following algorithms: logit and probit models, classification trees, random forests, artificial neural networks. Special attention was paid to the peculiarities of the training machine learning models, the impact of data imbalance on the predictive ability of models, analysis of ways to deal with these imbalances and analysis of the influence of non-financial factors on the predictive ability of models. In their study, the authors used non-financial and financial indicators calculated on the basis of public financial statements of the construction companies for the period from 2011 to 2017. The authors concluded that the models considered show acceptable quality for use in forecasting bankruptcy problems. The Gini or AUC coefficient (area under the ROC curve) was used as the quality markers of the model. It was revealed that neural networks outperform other methods in predictive power, while logistic regression models in combination with discretization follow them closely. It was found that the effective way to deal with the imbalance data depends on the type of model used. However, no significant impact on the imbalance in the training set predictive ability of the model was identified. The significant impact of nonfinancial indicators on the likelihood of bankruptcy was not confirmed.