Wyniki wyszukiwania - BazTech

1

SAFE-MASS Sociotechnical Array Framework for Evolving Maritime Autonomous Surface Ships

Praestegaard Larsen B., Rauffet P., Espes D.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

|

2026

|

Vol. 20 No. 1

229--248

EN

This paper explores the sociotechnical risk management challenges faced by Maritime Autonomous Surface Ships (MASS) with an emphasis on cybersecurity. As the maritime sector increasingly embraces autonomous vessels to enhance efficiency and safety, it confronts new cybersecurity vulnerabilities and challenges. The paper outlines a comprehensive approach to identifying and mitigating cyber risks by examining the sociotechnical considerations within MASS. It underscores the importance of understanding how cyber threats can compromise the interaction between humans and systems, potentially impacting vessel operations performance and safety. Through a detailed description of the Sociotechnical Array Framework for Evolving Maritime Autonomous Surface Ships (SAFE-MASS), which functions as a sociotechnical transition taxonomy, and by explaining how this can be used for securing MASS this research contributes valuable insights into developing safer and more efficient maritime operations, signaling a trans-formative shift in the industry’s future, especially by examining information technology (IT) and operational technology (OT) integration within MASS highlights the critical need for robust cybersecurity measures in this emerging field.

2

Integrating Artificial Intelligence into Naval Capability Development

Ljulj A., Štambuk I., Slapničar V.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

|

2026

|

Vol. 20 No. 1

173--185

EN

The rapid advancement of artificial intelligence (AI) is transforming naval capabilities, reshaping ship design, lifecycle management, operational decision-making, and autonomous maritime systems. Naval platforms are among the most complex engineered systems, characterised by long service lives, safety-critical functions, and demanding operational environments, making AI integration both strategically attractive and technically challenging. This paper presents an engineering-oriented review of AI applications in the naval domain, focusing on their role across the capability development lifecycle. To illustrate practical implementation, a Random Forest regression model is developed to support early-stage prediction of the block coefficient of naval ships. The review highlights significant opportunities associated with AI integration, including enhanced decision-making, improved design efficiency, and increased operational effectiveness. However, successful AI adoption requires technological advancement alongside organisational adaptation, strong governance, and sustained investment in human expertise. AI should therefore be understood not as a replacement for naval engineering expertise, but as a force multiplier that augments analytical capacity and accelerates innovation across the maritime domain.

3

Enhancing Container Handling Operations in Maritime Terminals Using the Ant Colony Optimization

El Khaldi Adam, Soudi Youness, Jahnouni Kamelia, Hachimi Hanaa, El Mokhi Chakib

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

|

2026

|

Vol. 20 No. 1

83--92

EN

Numerous studies have underscored the significance of scheduling and optimization challenges within maritime terminals. This dissertation examines how to optimize container movements specifically for export operations, simultaneously taking into account the operating sequences of yard cranes and trucks. It also considers any potential interference that may arise among yard cranes. A survey of existing literature on yard crane scheduling indicates a lack of work addressing both unproductive crane moves and possible crane-to-crane interferences at the same time, which constitutes an innovative element in our study. Initially, the container loading scheduling task is formulated as a mixed-integer linear program, where the objective function aims to minimize the overall handling time required by the yard cranes. The mathematical model incorporates various assumptions that address interference effects and non-productive movements. In order to tackle this problem, an Adaptive Large Neighborhood Search (ALNS) heuristic is introduced. This strategy proves effective in managing optimization issues in container terminals, regardless of the size of the problem—whether it involves 10, 20, or even 100 containers. The data utilized for validating the method are intentionally generated, allowing for differences in both the number of containers and the amount of accessible handling equipment. Extensive testing verified the ALNS algorithm’s usefulness. Various situations were tested by combining various removal and insertion strategies, and the results demonstrated the ALNS method’s robustness.

4

Decision Support System Using Modern Methods of Collision Avoidance in Collision Situations at Sea

Mohamed-Seghir M.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

|

2026

|

Vol. 20 No. 1

55--60

EN

Due to the rapid growth of maritime transport, many researchers have developed advanced methods aimed at increasing navigation safety and reducing operating costs, while maintaining compliance with the International Regulations for Preventing Collisions at Sea (COLREGs). Navigating a ship in potential collision situations requires decision-making under conditions of uncertainty and ambiguity – particularly with respect to concepts such as collision risk and safe speed. These concepts are subjective and not clearly defined. In response to these challenges, this paper presents an artificial intelligence-based method that takes into account the navigator's role as a decision-maker. The proposed solution is designed for integration with existing collision avoidance systems. A universal simulator was developed to evaluate the effectiveness of an algorithm for determining a safe ship trajectory in collision situations. Example navigation scenarios were conducted and presented using this simulator.

5

Challenges in the Supply Chain Management Process for Offshore Wind Farms. A Scoping Review of Decision Support Tools Utilizing Discrete Event Simulation

Rybowski M.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

|

2026

|

Vol. 20 No. 1

187--200

EN

Offshore wind energy (OWE) has become a key component of the global transition toward renewable energy; however, its supply chains remain highly complex due to harsh marine conditions, weather dependency, logistical constraints, and high capital intensity. In this context, decision support systems (DSSs) based on discrete event simulation (DES) are increasingly applied to improve planning and operational efficiency. This study aims to systematically identify offshore wind supply chain (OWSC) challenges addressed in the literature, evaluate the application of DES-based DSS, assess the methodological quality of existing studies, and highlight research gaps and future directions. A PRISMA-guided scoping review was conducted using a predefined protocol, covering English-language journal, conference, and technical publications from 2010 to 2025. Following database searches, deduplication, and screening, 30 studies were included from an initial set of 712 records. The results show that DES is widely adopted, with 63% of studies using pure DES and 37% employing hybrid simulation–optimization approaches; 67% of studies included case-based validation. Seven major categories of challenges were identified: weather and metocean conditions, vessel and fleet management, installation processes, port and logistics operations, operations and maintenance, information and coordination, and cost/time optimization. Reported benefits of DES-based DSS include improvements in cost efficiency, time performance, system availability, and resource utilization. The findings confirm that DES constitutes a robust and effective foundation for decision support in offshore wind logistics, particularly under uncertainty and resource constraints, while hybrid approaches further enhance its capabilities. Nevertheless, significant gaps remain, including inconsistent modeling assumptions (especially regarding metocean workability), limited transparency in verification and validation processes, and insufficient coverage of emerging areas such as floating wind, decommissioning, and digital integration (e.g., IoT, AI, and digital twins). These findings underline the need for improved standardization, reporting practices, and benchmark datasets in future research.

6

Modeling determinants of production management using distributed intelligence

Wittbrodt Piotr, Łapuńka Iwona

Management Systems in Production Engineering

|

2026

|

nr 2 (34)

181--190

EN

This article presents a decision-making model to support production management, developed with the application of distributed intelligence (DI). A systematic literature review (SLR) identified a significant gap in the integration of distributed decision-making methods with existing production systems. The proposed model incorporates dynamic selection of resources – workstations and employees – for production tasks, taking into account their competencies, availability, and technological constraints. The optimization process is performed using a particle swarm optimization (PSO) algorithm, implemented in the MATLAB environment. The model was validated through a case study conducted in a metalworking company. Empirical findings confirm the hypothesis that introducing elements of distributed intelligence into the production decision-making process enhances the quality of outcomes. Furthermore, the reorganization of the organizational structure was shown to reduce the time required to access information, enabling flexible adaptation of the production workflow to the company’s evolving operational conditions. This research contributes to the development of decision support systems and outlines future directions for predictive and autonomous production planning.

7

Zagospodarowanie wód opadowych w koncepcji Smart City - studium przypadku z wykorzystaniem narzędzi komputerowych

Trocki Adam, Brodziak Rafał, Mazurkiewicz Karolina

Instal

|

2026

|

nr 3

37--45

PL

Wzrost natężeń opadów będący efektem zmian klimatycznych prowadzi m.in. do zagrożeń powodziowych w miastach, co skutkuje potrzebą opracowania nowych metod zarządzania systemami zagospodarowania wód opadowych. Celem pracy było stworzenie metodyki wykorzystania narzędzi komputerowych w ocenie i usprawnianiu funkcjonowania kanalizacji deszczowej w kontekście koncepcji Smart City. W studium przypadku dla wybranego obszaru miejskiego przeprowadzono analizę istniejącej sieci kanalizacyjnej z wykorzystaniem systemów GIS oraz modeli hydrodynamicznych, które pozwoliły na identyfikację krytycznych miejsc i ocenę funkcjonowania sieci podczas różnych scenariuszy opadowych. Na tej podstawie zaproponowano działania retencyjne, obejmujące m.in. zbiorniki retencyjne, urządzenia podczyszczające oraz system monitoringu i sterowania. Opracowana metodyka umożliwia planowanie inwestycji i modernizacji infrastruktury w sposób wspierający zrównoważony rozwój miasta, ograniczający ryzyko lokalnych podtopień oraz podnoszący odporność systemu na ekstremalne warunki pogodowe. Wyniki wskazują, że integracja narzędzi komputerowych z inteligentnym zarządzaniem siecią kanalizacyjną jest efektywnym rozwiązaniem w ramach strategii Smart City.

EN

Climate change and the associated increase in precipitation intensity, causing, among other things, flood risks in cities, point to the need to develop new methods for managing stormwater systems. The aim of the study was to develop a methodology for using computer tools to assess and improve the performance of stormwater drainage systems within the context of the Smart City concept. In a case study of a selected urban area, an analysis of the existing drainage system was carried out using GIS and hydrodynamic models, enabling the identification of critical locations and the assessment of system performance under various rainfall scenarios. On this basis, retention measures were proposed, including retention reservoirs, pre-treatment devices and a monitoring and control system. The developed methodology enables the planning of investments and infrastructure modernisation in a way that supports sustainable urban development, reduces the risk of local flooding and increases the system’s resilience to extreme weather conditions. The results indicate that integrating computer tools with intelligent sewerage network management is an effective solution within the Smart City strategy.

8

LEAS: An AI-based Demonstrator as Decision Support Tool for Traffic Monitoring at VTS Centers

Stach Thomas, Koch Paul

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

|

2025

|

Vol. 19 No. 1

301--308

EN

Maritime traffic is prevalent worldwide, with particularly high density in coastal waters. To ensure safety and efficiency, Vessel Traffic Service (VTS) centers monitor and coordinate maritime traffic. For this purpose, VTS centers utilize various sensor and communication technologies such as radar, Automatic Identification System (AIS), electro-optical systems or radio communication. Additionally, any Vessel Traffic Service Operator (VTSO) is motivated to utilize a Decision Support Tool (DST). The LEAS project addresses emerging challenges at VTS centers. One key challenge results from the continuous evolution of maritime traffic, in particular, its ever increasing automation and autonomization. Another key challenge is the growing shortage of skilled workers. Consequently, it is crucial to process increasing volume of maritime traffic data while maintaining or improving safety and efficiency. DSTs at VTS centers must be adapted to these emerging challenges, accordingly. In the LEAS project, we develop and evaluate a demonstrator which represents a DST. This demonstrator is being developed in close collaboration with VTSOs to address these challenges. Most notably, it has a situation detection which makes use of Artificial Intelligence (AI) methods and displays relevant information in an intuitive Human-Machine Interface (HMI). The demonstrator is evaluated using simulated traffic scenarios in the German Bight and Baltic Sea, with VTSOs as test subjects. This paper provides an overview of the project and demonstrator. First, we introduce the key requirements for the demonstrator and discuss their impact on the system architecture. Next, we present its AI-based situation detection. We explain the underlying formalism of the situation detection and resolution as well as its implementation in the demonstrator. Finally, we evaluate the capabilities and limitations. The paper concludes with an outlook to future work with focus on potential deployment at DST at VTS centers.

9

Digital Twin Technology in Maritime: A MAAP Innovation Strategy

Baylon Angelica, Santos Eduardo Ma

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

|

2025

|

Vol. 19 No. 4

1229--1235

EN

Following the fourth industrial revolution and recent advances in information and communication technologies, the digital twinning concept is attracting the attention of maritime academia and the maritime industry worldwide. A digital twin is a representation in digital form of a physical item, thing, or system: a vessel, a car, a wind turbine, a power grid, a pipeline, or equipment such as a thruster or an engine. One of the key initiatives at the MAAP is to apply Digital Twin technology in the development of MASS (Maritime Autonomous Surface Ship), e-navigation, ship engine room management, training, and validation of operational concepts associated with smart and autonomous ships. To this end, the progress realized in adapting and exploring digital twin (DT) technologies at MAAP will be presented. In particular, the Kognitwin technology system (a Digital Twin system) developed by Kongsberg Maritime and other systems applicable to decision-making that ensure cost-effective, safer, and sustainable operations will be described. The focus will be placed on using digital twin technology in some of the grey areas: Optimization of Fleet with Virtual Transition of Ship Control System, Enhancing the Port and Terminal Operations, Awareness Situation about Operational Parameters, End-To-End Supply Chain Optimization, Amplified Security Ensuring Safety and Better vessel design and operation.

10

Dependence of the Maximum Approach Distance on the Shape of the Ship's Safe Domain

Burmaka Igor, Zhukov Dmytro

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

|

2025

|

Vol. 19 No. 1

265--268

EN

In work analytical expressions are resulted for a calculation minimum - possible distance of rapprochement in the case of application of domains of elliptic and difficult form. Shown graphic dependence minimum - possible distance of rapprochement from foreshortening of ships which are drawn together, for the domains of both types. It is shown that the domains of elliptic and difficult form have a similar character of change minimum - possible distance of rapprochement depending on foreshortening of ships.

11

Predictive modeling and decision support using machine learning in business contexts

Bojanowska Agnieszka, Kulisz Monika

Advances in Science and Technology. Research Journal

|

2025

|

Vol. 19, no. 10

385--395

EN

With the growing emphasis on data-driven decision making, artificial intelligence (AI) methods have become increasingly important in managerial practice. This study aims to develop and evaluate supervised machine learning models for predicting customer brand loyalty and satisfaction based on selected behavioral, attitudinal, and programmatic attributes. This paper presents a lightweight decision support application that leverages machine learning techniques—specifically, Artificial Neural Networks (ANN) and Support Vector Machines (SVM)—to predict key customer-related indicators: brand loyalty and satisfaction. The models were trained on behavioral and attitudinal inputs and achieved excellent predictive performance, with test accuracies reaching 100%. The novelty of this study lies in the deployment of these models within an intuitive graphical user interface (GUI), enabling real-time predictions by non-technical users. Unlike traditional approaches focused solely on algorithm development, this research demonstrates a practical implementation of computational intelligence for operational and tactical business decision-making. The tool supports managers in profiling customers, optimizing loyalty programs, and enhancing customer engagement strategies through accessible AI-powered insights.

12

Zastosowanie telemetrycznego monitoringu w czasie rzeczywistym jako podstawa Systemu Wspierania Decyzji dla Zbiornika Sulejowskiego

Chamerska Aleksandra, Skąpski Ksawery, Jarosiewicz Paweł, Izydorczyk Katarzyna, Biernacki Mariusz, Frątczak Wojciech

Gospodarka Wodna

|

2023

|

Nr 10

13--15

PL

Antropopresja wywiera znaczący wpływ na ekosystemy wodne, stwarzając pilną potrzebę skutecznego monitoringu i zarządzania wodami powierzchniowymi, zwłaszcza w kontekście postępującej eutrofizacji. Rozwiązaniem, które może przyczynić się do poprawy efektywności podejmowania decyzji w zarządzaniu zlewniowym, jest monitoring w czasie rzeczywistym (monitoring real-time). Wydłużony czas reakcji, brak ciągłości wyników i niepewność wynikająca z pracy próbkobiorcy to główne ograniczenia tradycyjnych metod monitoringowych. Dlatego technologie monitorowania wody w czasie rzeczywistym są obiecującym rozwiązaniem pozwalającym przezwyciężyć ograniczenia tradycyjnych metod. W artykule została przedstawiona koncepcja pilotażowej telemetrycznej sieci stacji monitoringu real-time w zlewni rzeki Pilicy, która powstała w ramach projektu LIFE Pilica. Monitoring w czasie rzeczywistym, współdziałający z ekohydrologiczną wiedzą naukową, umożliwia dogłębne zrozumienie „pulsacji” ekosystemów wodnych oraz dynamiki transportu zanieczyszczeń biogenicznych. Ta synergia może w przyszłości umożliwić podejmowanie bardziej świadomych decyzji w zakresie zarządzania zasobami wodnymi, zwłaszcza pod kątem zapobiegania eutrofizacji.

EN

Anthropopressure has a significant impact on water ecosystems, resulting in an urgent need for efficient surface waters monitoring and management, especially in the context of the progressing eutrophication. A solution that may contribute to the improvement of the catchment area management decision-making efficiency is the real-time monitoring. Extended reaction times, lack of continuity of results and incertitude related to the work of persons taking samples are the main limitations of the conventional monitoring methods. Therefore, real-time water monitoring technologies are a promising solution that may eliminate the constraints related to traditional methods. The article presents the concept of a pilot real-time telemetric monitoring stations network in the Pilica river basin under the LIFE Pilica project. Real-time monitoring combined with ecohydrological scientific knowledge allows for a deep understanding of the water ecosystems 'pulsations' and the biogenic pollutions transport dynamics. In the future, this synergy may lead to taking more informed decisions in the scope of water resources management, especially in order to prevent the eutrophication.

13

Prediction of Selected Mechanical Properties in Austempered Ductile Iron with Different Wall Thickness by the Decision Support Systems

Jaśkowiec Krzysztof, Opaliński Andrzej, Kustra Piotr, Jach D., Wilk-Kołodziejczyk Dorota

Archives of Foundry Engineering

|

2023

|

Vol. 23, iss. 2

137--144

EN

The structure of Austempered Ductile Iron (ADI) is depend of many factors at individual stages of casting production. There is a rich literature documenting research on the relationship between heat treatment and the resulting microstructure of cast alloy. A significant amount of research is conducted towards the use of IT tools for indications production parameters for thin-walled castings, allowing for the selection of selected process parameters in order to obtain the expected properties. At the same time, the selection of these parameters should make it possible to obtain as few defects as possible. The input parameters of the solver is chemical composition Determined by the previous system module. Target wall thickness and HB of the product determined by the user. The method used to implement the solver is the method of Particle Swarm Optimization (PSO). The developed IT tool was used to determine the parameters of heat treatment, which will ensure obtaining the expected value for hardness. In the first stage, the ADI cast iron heat treatment parameters proposed by the expert were used, in the next part of the experiment, the settings proposed by the system were used. Used of the proposed IT tool, it was possible to reduce the number of deficiencies by 3%. The use of the solver in the case of castings with a wall thickness of 25 mm and 41 mm allowed to indication of process parameters allowing to obtain minimum mechanical properties in accordance with the PN-EN 1564:2012 standard. The results obtained by the solver for the selected parameters were verified. The indicated parameters were used to conduct experimental research. The tests obtained as a result of the physical experiment are convergent with the data from the solver.

14

Binary Classification of Agricultural Crops Using Sentinel Satellite Data and Machine Learning Techniques

Bertellini Paolo, D'Addese Gianluca, Franchini Giorgia, Parisi Simone, Scribano Carmelo, Zanirato Daniele, Bertogna Marko

Annals of Computer Science and Information Systems

|

2023

|

Vol. 35

859--864

EN

The advent of high-resolution satellite imagery, such as Sentinel 1 and Sentinel 2, has provided valuable data for various applications, including crop classification. This paper presents a study on the classification of agricultural fields using indices derived from Sentinel satellite imagery. Specifically, we focus on creating binary classifiers capable of distinguishing between different crops, namely Tomatoes, Soy, Sugar Beets, Rice, and Wheat. The paper investigates various preprocessing techniques to create a dataset suitable for machine learning methods, such as Random Forests, which require a fixed number of features. Additionally, we demonstrate that linear interpolation and out-of-scale values have equivalent performance in terms of classification accuracy. Furthermore, we address the issue of imbalanced datasets commonly encountered in agricultural field classification. We explore different balancing techniques that can significantly improve the performance of machine learning methodologies. The motivation for this work stems from the growing interest in Agriculture 4.0, and it serves as a valuable tool to verify farmers' claims, especially in relation to state subsidies for specific crops of interest. Understanding the crop type present in the field represents highly valuable information that can serve as a foundation for subsequent analyses or as input for calibrating models, such as Decision Support Systems. Overall, this study contributes to the field of agricultural research and provides insights into the application of Machine Learning techniques for crop classification using satellite imagery. The findings offer practical implications for monitoring and optimizing agricultural practices in the context of precision farming and sustainable agriculture.

15

Conceptual Model Creation for Automated Self-training System of Functional Control and Detection of Railway Transport

Oralbekova Ayaulym, Amanova Marzhana, Rustambekova Kamila, Kaskatayev Zhanat, Kisselyova Olga, Nurgaliyeva Roza

International Journal of Electronics and Telecommunications

|

2021

|

Vol. 67, No. 4

603--608

EN

In order to improve the operational reliability and service life of the main systems, components and assemblies (SCA) of railway transport (RT), it is necessary to timely detect (diagnose) their defects, including the use of the methods of intellectual analysis and data processing. One of the promising approaches to the synthesis of the SCA functional control system is the use of intelligent technology (INTECH) methods. This technology is based on maximizing the information capacity of an automated decision support system for detecting faults during its training.

16

Mining clinical pathways for daily insulin therapy of diabetic children

Deja Rafal, Froelich Wojciech, Deja Grazyna

International Journal of Applied Mathematics and Computer Science

|

2021

|

Vol. 31, no. 1

107--121

EN

We propose a decision support framework (DSF) assisting insulin therapy of diabetic children. Our DSF relies on a medical treatment graph (MTG), which models and graphically represents clinical pathways. Using the MTG, it is possible to plan and adapt medical decisions dependent upon the current health state of a patient and the progress of the treatment. Our MTG fits well with the requirements of clinical practice. The presented work is a cooperative effort of researchers in computer science and medicine. The MTG model has been thoroughly tested and validated using real-world clinical data. The usefulness of the approach has been confirmed by physicians.

17

An AHP-based Decision Support System for Personnel Selection for Manager Position in Businesses

Calp Muhammed Hanefi, Agarwal Shivani

Annals of Computer Science and Information Systems

|

2021

|

Vol. 28

1--7

EN

Humanity is one of the most important resources for businesses. Because, with human resources, the data of the institution can be obtained and information can be produced by processing. Thus, human resources make the business a learning and dynamic organization and ensure its continuity. In enterprises, personnel selection (in terms of quantity or quality) is carried out within the scope of Human Resources Management. This selection process usually takes place when a group of decision makers evaluates the candidates according to some criteria and their own opinions. However, this situation prevents an objective and fair selection. For this reason, in this study, a decision support system (DSS) has been developed by using the Analytical Hierarchy Process (AHP), one of the Multi-Criteria Decision Making (MCDM) methods, to ensure objectivity and to select the most suitable personnel for the job description. The said DSS provides the selection of the marketing manager among the personnel working in an enterprise. For this, the 10 employees working in the marketing department of the enterprise for the longest time were taken into account. When the results are examined, it is seen that the most qualified personnel can be selected successfully in cases where customer satisfaction, performance value and number of projects are prioritized

18

Application of the Decision Support System RODOS in the Event of Nuclear Reactor Accident with Scenario of Release of Radioactive Isotopes to the Atmosphere

Jaroszek Adam Sylwester

Prace Instytutu Elektrotechniki

|

2020

|

Z. 281

49--60

PL

Systemy wspomagania decyzji cieszą się coraz większą popularnością ze względu na szybkie dostarczanie informacji o rozwoju sytuacji radiacyjnej po awarii jądrowej z uwolnieniem izotopów promieniotwórczych do powietrza atmosferycznego. Systemy te są wykorzystywane jako narzędzie bezpośrednio stosowane w sytuacji awaryjnej oraz jako narzędzie do przeprowadzenia przed inwestycyjnych obliczeń z zakresu planowania awaryjnego. Artykuł przedstawia podstawowe funkcjonalności systemu wspomagania decyzji RODOS. Program służy do przeprowadzania prognoz rozwoju zdarzeń radiacyjnych z uwolnieniem izotopów promieniotwórczych do atmosfery w wyniku awarii w elektrowniach jądrowych.

EN

Decision support systems are increasingly popular due to the fast delivery of information about development of the situation during the nuclear accidents. The information provided by decision support systems facilitate proper selection of necessary protective actions and correct allocation of services involved in the activities. The RODOS system is designed for forecasting the dispersion of radioactive isotopes in the atmosphere. It can be used in case of real radiological nuclear emergency as well as for emergency preparedness purpose.

19

Wyznaczanie kluczowych wskaźników wydajności procesu produkcyjnego – część II: badania przemysłowe

Król Dariusz, Skowroński Jan, Zaręba Maciej, Bartecki Krzysztof

Pomiary Automatyka Robotyka

|

2020

|

R. 24, nr 3

19--28

PL

Zastosowanie nowych technologii w Przemyśle 4.0 umożliwia lepszą organizację, monitorowanie, kontrolę oraz skuteczną optymalizację procesów produkcyjnych, szczególnie w zakresie wydajności. Prezentowane rozwiązanie opiera się na hierarchicznej analizie wskaźników efektywności, w tym głównie na kontroli wskaźnika ogólnej efektywności zasobów produkcyjnych OEE. Rosnąca liczba możliwych do uzyskania skwantyfikowanych sygnałów monitorujących pracę maszyn, temperaturę otoczenia czy częstotliwość drgań sprawia, że narzędzia wspomagające decyzje są coraz bardziej wyrafinowane i, poza prezentacją obecnego stanu zasobów, coraz częściej obejmują także analizę predykcyjną. Opisywane narzędzie PUPMT pozwala zidentyfikować kluczowe zdarzenia, które mają istotny wpływ na bieżącą lub przyszłą efektywność produkcji. Umożliwia także analizę typu what-if, dopuszczając symulację wpływu projektowanych zmian, a wyniki tej symulacji uzależnia od skutków podobnych zmian, które miały miejsce w przeszłości w danym przedsiębiorstwie. Dzięki automatycznej identyfikacji potencjalnych zależności rozwiązanie dostosowuje się do specyfiki firmy lub wybranej jednostki produkcyjnej. Początkowe rozdziały zawierają m.in. opis najważniejszych metod wykorzystywanych w rozwiązaniu PUPMT. W dalszej części przedstawiono wybrane wyniki badań przemysłowych, które przeprowadzono na kilkudziesięciu jednostkach produkcyjnych.

EN

The use of new technologies in Industry 4.0 enables better organization, monitoring, control and effective optimization of production processes, especially in terms of efficiency. The solution is based on a hierarchical analysis of key performance indicators, including mainly the control of Overall Equipment Effectiveness (OEE). The growing number of quantifiable signals monitoring machine operation, ambient temperature or even the frequency of vibrations makes decision support tools more and more sophisticated. Moreover, they also include predictive analysis in addition to presentations of the current state of resources. PUPMT tool allows identifying key events that have a significant impact on current or future production efficiency. It also allows the what-iftype analysis, running the simulation of the impact of the proposed changes, and the results of this simulation depend on the effects of similar changes that occurred in the past in a given enterprise. Thanks to the automatic identification of potential dependencies, the proposed solution adapts to the specifics of a given company or even a selected production unit. The paper in the first part contains a description of the essential methods used in the PUPMT tool. The second part presents selected results of industrial research, which were carried out on several dozen production units.

20

Multi-criteria comparative analysis of the use of subtractive and additive technologies in the manufacturing of offshore machinery components

Deja Mariusz, Siemiątkowski Mieczysław Stanisław, Zieliński Dawid

Polish Maritime Research

|

2020

|

nr 3

71--81

EN

The dynamic development of additive manufacturing technologies, especially over the last few years, has increased the range of possible industrial applications of 3D printed elements. This is a consequence of the distinct advantages of additive techniques, which include the possibility of improving the mechanical strength of products and shortening lead times. Offshore industry is one of these promising areas for the application of additive manufacturing. This paper presents a decision support method for the manufacturing of offshore equipment components, and compares a standard subtractive method with an additive manufacturing approach. An analytic hierarchy process was applied to select the most effective and efficient production method, considering CNC milling and direct metal laser sintering. A final set of decision criteria that take into account the specifics of the offshore industry sector are provided.