An efficient and flexible production system can contribute to production solutions. These advantages of flexibility and efficiency are a benefit for small series productions or for individual articles. The aim of this research was to produce a genetic production system schedule similar to the sustainable production scheduling problem of a discrete product assembly plant, with more heterogeneous production lines, and controlled by one-time orders. First, we present a detailed mathematical model of the system under investigation. Then, we present the IT for a solution based on a soft calculation method. In connection with this model, a computer application was created that analyzed various versions of the model with several practical problems. The applicability of the method was analyzed with software specifically developed for this algorithm and was demonstrated on a practical example. The model handles the different products within an order, as well as their different versions. These were also considered in the solution. The solution of this model is applicable in practice, and offers solutions to better optimize production and reduce the costs of production and logistics. The developed software can not only be used for flexible production lines, but also for other problems in the supply chain that can be employed more widely (such as the problem of delivery scheduling) to which the elements of this model can be applied.
Due to globalization and increased market competition, forwarding companies must focus on the optimization of their international transport activities and on cost reduction. The minimization of the amount and cost of fuel results in increased competition and profitability of the companies as well as the reduction of environmental damage. Nowadays, these aspects are particularly important. This research aims to develop a new optimization method for road freight transport costs in order to reduce the fuel costs and determine optimal fueling stations and to calculate the optimal quantity of fuel to refill. The mathematical method developed in this research has two phases. In the first phase the optimal, most cost-effective fuel station is determined based on the potential fuel stations. The specific fuel prices differ per fuel station, and the stations are located at different distances from the main transport way. The method developed in this study supports drivers’ decision-making regarding whether to refuel at a farther but cheaper fuel station or at a nearer but more expensive fuel station based on the more economical choice. Thereafter, it is necessary to determine the optimal fuel volume, i.e., the exact volume required including a safe amount to cover stochastic incidents (e.g., road closures). This aspect of the optimization method supports drivers’ optimal decision-making regarding optimal fuel stations and how much fuel to obtain in order to reduce the fuel cost. Therefore, the application of this new method instead of the recently applied ad-hoc individual decision-making of the drivers results in significant fuel cost savings. A case study confirmed the efficiency of the proposed method.
With the rapid progression of technology and the growing presence of natural language processing applications in everyday life, it is essential to explore how high school students engage with these tools and how they foresee their futures in light of these advancements. The goal of this study is to analyse the usage patterns and future value perceptions of ChatGPT among 70 high school students through a survey-based approach. A key finding highlights that technology has become an integral element of contemporary life, underscoring the historical relevance of Natural Language Processing (NLP) and the eagerness of the younger generation to adopt such emerging technologies. High school students utilise ChatGPT for various purposes, including academic support, social communication, and personal management, across both educational and social contexts. Moreover, the participants conveyed a positive outlook on the potential of ChatGPT to significantly impact their lives in the coming years while acknowledging possible hurdles. Based on the findings of this study, it is clear that NLP tools like ChatGPT have a crucial role in moulding the experiences and anticipations of high school students. This paper, therefore, sets the stage for additional research and development in this area.
Industry 4.0 and Education 4.0 are two concepts that are closely linked, as both represent a shift towards a more technologically advanced and digitally driven future. Industry 4.0 is about the integration of advanced digital technologies into manufacturing and other industrial processes, while Education 4.0 is about the integration of technology, in particular digital technologies, into the teaching and learning process. The relationship between Industry 4.0 and Education 4.0 is mutually reinforcing. Education 4.0 aims to prepare learners for the demands of the 21st century workforce by equipping them with the skills and knowledge they need to thrive in a rapidly changing digital environment. The development of computational thinking skills is a key component of Education 4.0, as it is a foundational skill that underpins many of the digital technologies used in industry today. Computational thinking is a problem-solving approach that involves breaking complex problems into smaller, more manageable parts, identifying patterns and relationships, and creating algorithms to solve problems. Microcontrollers, also known as embedded systems, are small computers used to control electronic devices such as home appliances, cars and toys. Visualisation is an important tool in teaching about microcontrollers because it helps students understand abstract concepts and see how different components fit together. Visualisation helps students to gain a deeper understanding of how microcontrollers work and how electronic devices are controlled. This understanding can be particularly valuable for students who want to pursue a career in fields such as engineering, robotics or computer science.
Manufacturing companies today face an increasingly dynamic environment. Development of production planning systems requires approaches that offer flexibility in development of solutions. Applying the UML (Unified Modeling Language) and MDA (Model Driven Architecture) paradigm can ensure compatibility between applications developed on different platforms. The MDA approach to software development becomes an obvious choice. In this approach, the models drive the process of software development. These models are defined at different levels of abstraction to represent various aspects of the system. The transformation of models from one level of abstraction to another, or the transformation of models to executable code is performed by using automated transformation tools. The strength of MDA lies in the fact that it is based on widely used industry standards for visualizing, storing and exchanging software designs and models. The models in MDA are abstracted at three different levels – the Computation Independent Model (CIM), the Platform Independent Model (PIM) and the Platform Specific Model (PSM). The key to the success of MDA lies in automated or semi-automated model-to-model and model-to-code transformations. In this paper, we examine how a platform-independent model of an MRP-based production planning system can be created using MDA approach, from which several platform-specific systems can be achieved, as required.
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