The paper deals with a production scheduling process, which is a problematic and it requires considering a lot of various factors while making the decision. Due to the specificity of the production system analysed in the practical example, the production scheduling problem was classified as a Job-shop Scheduling Problem (JSP). The production scheduling process, especially in the case of JSP, involves the analysis of a variety of data simultaneously and is well known as NP-hard problem. The research was performed in partnership with a company from the automotive industry. The production scheduling process is a task that is usually performed by process engineers. Thus, it can often be affected by mistakes of human nature e.g. habits, differences in experience and knowledge of engineers (their know-how), etc. The usage of heuristic algorithms was proposed as the solution. The chosen methods are genetic and greedy algorithms, as both of them are suitable to resolve a problem that requires analysing a lot of data. The paper presents both approaches: practical and theoretical aspects of the usefulness and effectiveness of genetic and greedy algorithms in a production scheduling process.

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Nowadays, modern production processes are undergoing intensive technological development, according to Industry 4.0 and Industry 5.0 assumptions. Despite the separate differentiation and numbering of these terms, the assumptions of the two approaches do not contradict each other. Industry 5.0 is a type of an extension of the drive for the highest possible degree of integration of automated systems (along with the pillars assumed in Industry 4.0) by finding a place where humans prove to be irreplaceable and their needs are identified as the most essential, central aspect. This leads to the implementation of semi-automated processes in which the cooperation between human and machine is the key. The paper presents an analysis and the results of the studies performed in company that produces vehicle control systems in automotive. The research includes quarter-a-year of studies and observation of production process. The studies aimed identifying waste in production process and proposing improvement methods, with particular reference to automated operations. Implementation of proposed improvements was mainly based on re-programming automated systems, but also on adding new process of cleaning brakes, that allowed to reduce the number of scrapped parts. Moreover, the implicated solutions allowed to achieve reduction of production process cycle time, financial savings and risk of the defects.

Studies have been performed to improve the process of waste management. They were fulfilled by changing the base of waste logistics management using a combination of intelligent algorithms and the IMPACT IoT platform instead of a human factor. The research was carried out on the example of real data with respect to waste management in a given area. The proposed solution includes a program that simulates the filling of specific waste containers located in various areas. The determined aspects are inconveniences on the routes, affecting the time of moving between the receiving points and the distances between the containers. The variability of the speed and intensity of the containers filling up over time is an additional factor taken into account. The proposed methods yielded the performance of the control of the containers’ filling status in real time, which apparently results in the possibility of a reaction to the current demand just in time. The proposed solution enables the improvement of the waste logistics management process, including avoiding the too-frequent emptying of containers or overfilling them. The combination of the device prototype, the simulation program, and the developed algorithms opens the possibility for further research in the smart city and optimization areas.