Eastern-European Journal of Enterprise Technologies

Construction of a simulation model of the work of a transport and forwarding enterprise in organizing multimodal cargo transportation

2024-12-02T20:19:19+02:00

The object of this study is the process of rendering transport and forwarding services when organizing multimodal cargo transportation in international traffic.

The task to tackle was predetermined by the need to devise recommendations for choosing the optimal cargo transportation route, taking into account the individual requirements of the customer and performance indicators of the forwarders at the transport and forwarding company.

A simulation model of the work of a transport and forwarding company in the organization of multimodal transportation of goods was built and implemented in the GPSS World simulation automation software. The model involves the optimization of the duration of service provision and makes it possible to determine the required number of transportation department specialists to enable effective cooperation with customers.

When constructing the model, the duration of business processes in the organization of multimodal transportation was taken into account. In addition, the model accounts for the probability of errors and service delays, as well as the average delay time when errors occur, which will make it possible to take into account the additional work time of the forwarder to eliminate them.

The application of the built model in practice will allow specialists at transport and forwarding companies to organize multimodal transportation according to several alternative routes that meet the individual needs of the customer. It will also make it possible to determine the optimal number of forwarders needed to work at the multimodal transportation department.

In this case, the duration of rendering transport and forwarding services will be reduced by 12–16 %, the waiting time in queues for service will decrease by 9–13 %, and the efficiency of the work of specialists at the multimodal transportation department will increase by 9–14 %

Devising a method for assessing the efficiency in managing logistics operations of motor transport enterprises

2024-12-12T19:30:22+02:00

The object of this study is the logistics operations of a motor transport company. The task addressed is to improve the effectiveness of assessing the state of efficiency in managing logistics activities of motor vehicle enterprises, regardless of the level of hierarchy of the motor vehicle enterprise. Underlying the research is the game chaos algorithm (GCA) applied for assessing the state of efficiency in managing the logistics operations of motor transport enterprises. Evolving artificial neural networks are used to train GCA.

A comprehensive model for assessing the effectiveness of the operation of logistics activities of motor transport enterprises was built.

The model is proposed to be used in the operational management of logistics activities of motor transport enterprises. In addition, the model built allows for the following:

− assessing possible risks of disrupting the task of providing goods and services in organizations and organizational networks;

− determining the influence of performance evaluation indicators of the logistic support system in organizations and organizational networks on each other;

– establishing the influence of a group of indicators for evaluating the effectiveness of logistics support in organizations and organizational networks on a separate indicator.

The study also proposes a method for evaluating the effectiveness of managing logistics activities of motor transport enterprises. The originality of the method is that it makes it possible to avoid hitting the global and local optimum due to additional improved procedures based on the use of game chaos theory.

The simulation results showed an increase in the effectiveness of assessing the operational efficiency of the management of logistics activities of motor transport enterprises at the level of 14–16 % due to the use of additional improved procedures

Development of a neural network approach for risk management in helicopter technical condition diagnostics

2024-09-28T01:46:35+03:00

The object of the study is the quality of helicopter maintenance based on digital diagnostic tools. To ensure the required quality, quantitative risk assessment models for the in-depth and express diagnostics system of helicopter gas turbine engines in a neural network environment are proposed. The assessment of diagnostic efficiency is based on the analysis of probable control risks by standard deviations, which distinguishes the proposed approach from the traditional one. Two diagnostic modes are considered: rapid diagnostics exemplified by vibration diagnostics, and in-depth diagnostics, including both vibration diagnostics and pyrometric control. These diagnostic methods make it possible to implement a remote monitoring system at aircraft repair facilities, which significantly reduces maintenance labor intensity. As a result, it was found that control risks depend not only on the metrological level of measuring instruments but also on a combination of the statistical nature of control agents in their system composition according to the following characteristics: statistical parameters of the controlled indicator, distribution laws, and values of uncertainty of measuring instruments, as well as the uncertainty of control standards (tolerances). In the modeling process, risks were assessed as a function of the ratio of uncertainties of measuring instruments to the uncertainty of the controlled parameter, with varying values of the standard (tolerance). This approach will allow, in practice, the creation of a more effective system for monitoring and collecting statistical information on the operational reliability of the Mi-8 helicopter engine, where the quality of control is predicted to a greater extent based on the metrological indicators of the measuring instruments and methods

Identifying the influence of project delivery system (PDS) characteristics on green construction project performance

2024-12-19T19:31:15+02:00

This study examines the influence of Project Delivery System (PDS) features on green construction project performance by comparing Design-Bid-Build (DBB), Design-Build (DB), and Construction Management (CM) methods. The research problem is to analyze the influence of key characteristics (leadership, communication, mindset, teamwork, team chemistry, experience, competence) of the three PDS methods on achieving green construction project performance. The research objects were 109 respondents (team leader, vice president manager, project manager, site manager, site engineer, general superintendent, and supervisor) in five major cities in Indonesia. The results of the MANOVA analysis showed that leadership was the most influential factor in the three methods. CM excels in time, quality, and green building performance, DB excels in Occupational Health and Safety (OHS), and DBB excels in cost. Experience and competence are less influential factors, thus concluding that soft skills play a significant role in the successful implementation of green construction projects, providing valuable insights for industry practitioners and emphasizing the need to prioritize the development of leadership and interpersonal skills alongside technical expertise when implementing green construction projects through various PDS methods in Indonesia, which ultimately contributes to the advancement of sustainable construction practices in the rapidly growing building sector in the region. These findings provide insights for selecting optimal PDS methods in green construction projects

Devising a method for building an educational project team based on the analysis of competency matrix

2024-12-05T09:21:04+02:00

The object of this study is the processes to form teams for educational projects. The issue addressed is the distribution of lecturers based on educational components, taking into account the complex set of functional requirements for the executors of specific educational projects. To successfully implement educational projects, it is proposed to build a reserve of personnel and form effective teams of performers who have the necessary competencies and experience in implementing international projects. A method for determining the individual competencies of executors of educational projects at institutions of higher education has been formalized. It is proposed to evaluate lecturers on the basis of their effectiveness in teaching educational components, which is measured by testing the level of assimilation of program results by students. As a result, a matrix of competences was constructed, containing the evaluations of performers, which helps the management of the institution to make decisions about the extension or termination of contracts with lecturers, as well as possible bonuses. A method for analyzing the matrix of competencies for the formation of an educational project team is to identify lecturers who have the necessary competencies to provide educational components. As a result of applying the method, a project team is formed, which is able to provide all the necessary competencies, taking into account the qualification requirements. The method helps determine the shortage of qualified personnel and the need for their development. The method was verified using an example of an educational project in Kazakhstan to form a team of lecturers that meets the qualification requirements of the educational program. The results show that even a minimum team of three people can provide all 16 competencies at an appropriate level, and the involvement of additional performers expands the possibilities of flexible distribution of responsibilities between them

Implementation of knowledge distillation in developing a prediction model to know the performance of air transportation vocational education using machine learning

2024-12-20T21:52:27+02:00

The object of this research is the performance of air transportation vocational education. The problem in this research that must be solved is the complexity of the model in machine learning which requires a long processing time and requires high resources, so the knowledge transfer process in knowledge distillation must be carried out carefully so that the student model can capture and reproduce knowledge from the teacher's model. without loss of accuracy and problems such as Good Corporate Governance, Organizational Flexibility, and Strategic Change Management variables, which are interrelated and difficult to model accurately. The results obtained are in the form of a model that can predict vocational education performance by utilizing machine learning and knowledge distillation. The interpretation of this research is to apply the XGBoost machine learning algorithm and knowledge distillation. The characteristics and characteristics obtained are that the teacher model has the best performance in terms of loss, while the student model with distillation shows a significant reduction in loss compared to training without distillation. Thus, the distillation process is proven to help student models capture knowledge from teacher models, producing prediction accuracy of up to 90 % and being an efficient alternative in predicting the influence of main factors on the performance of air transportation vocational education. These findings are expected to provide a significant contribution to the development of more efficient and effective prediction models in the context of vocational education, especially in the field of air transportation