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.

The in-factory transport influences production rate and quality, so it is characterized by the shortest displacement time and maximum utilization of available means of transport, accompanied by their lowest operating wear. In the paper, an improvement of factory transport management is suggested, employing the traffic analysis methods of industrial vehicles. The efficiency of internal logistics was analyzed with the use of the WiFi-based technique for monitoring industrial vehicles. In the research, the in-building location system Optimatik BI was used and implemented in a company manufacturing PVC profiles for the market of building materials and interior equipment. Stopping times of forklift trucks, their routes and covered distances were estimated. On this ground, factors of their effective use were calculated. Further, it was possible to analyze the traffic of the forklift trucks and to generate heat maps that, in turn, made it possible to identify problems and develop a suitable improvement plan. A mathematical model that allows for efficiency optimization by determining the best number of employees handling the transport was developed for one of the analyzed logistics tasks.

This paper deals with an assessment of the effectiveness of resource management in production processes. A comprehensive and standardized approach to the issue of effectiveness contributes to the maximization of the potential of production resources. In this paper, the concept of effectiveness was used in the sense of the praxeological approach, and its components were distinguished by availability, performance and quality, which were assessed through the prism of losses occurring in production processes. The main purpose of this work was to develop a method for assessing the effectiveness of the use of available resources when implementing production processes. The method is based on a set of quantitative indicators and takes into account the categorization of production losses. Losses in availability, performance and quality in terms of areas of production, logistics, and maintenance, as well as organization and management, were identified. It was developed in response to the research gap identified in the literature review, which showed that there is a lack of a quantitative method for assessing the effectiveness of production processes. The method also takes into account the categorization of production losses, and at the same time allows the contribution of the main areas (production and production-related) with regard to the generation of these losses. The method is primarily intended to identify the causes of problems occurring in any production process, to determine their importance in a measurable way, and to monitor the course of processes after introducing changes of a different nature. The method was verified on a real example in a branch of an automotive industry company located in Lower Silesia.

The risk assessment determines threats that could appear during the execution of production system goals. Thus, manufacturing companies need to evaluate and react to the risk as well as it is possible. Due to the complexity and variable character of the mining system, as well as different types of parameters obtained at subsequent stages of the horizontal transport process, the risk was assessed according to various methods. Three stages were distinguished in the horizontal transport system of copper ore studied in the paper: tyre haulage of the shot copper ore to the transfer point, the so-called grate, transferring the output to belt conveyors and crushing solid rock into smaller pieces; transport on belt conveyors. Risk factors have been characterized for each stage. To assess the risk of the loading and haulage processes, an ANN model was built to predict the amount of ore extracted. A general linear neural network model was also built to confirm the results of the correlation analysis. One of the methods recommended for risk assessment in manufacturing companies is the Failure Mode and Effects Analysis (FMEA), which allows for calculating the risk and prioritizing it. The FMEA method allows identifying only these elements of the production system that are the most sensitive to the impact of risk factors. For the next stages of the horizontal transport process, the risk was assessed according to the FMEA method. The risk levels at each stage were different. Production systems are composed of many elements that create the reliability structure of this system. Unfortunately, the FMEA method does not allow for analyzing the reliability structure of the production system. Therefore, the new idea presented in this paper is a method of total risk assessment of the horizontal transport system of copper ore, which does take its reliability structure into account. The currently used methods of risk analysis and assessment do not take into consideration the reliability structure of the production system. The proposed method can be applied for risk assessment in other production systems characterized by a diversified structure.

The paper presents a hybrid method for the effective management of human resources when executing production processes. The method was proposed for managing production operators in systems of relatively high complexity—with the possibility of multiple alternative ways of designating operators to production stations. The method combines a novel approach of assessing the effectiveness of production operators, the use of an intelligent algorithm and the risk assessment method. The concept of effectiveness was used in the sense of a praxeological approach. Availability, performance and quality were defined as its components, and were evaluated through the prism of losses occurring in production processes. The aim of the study is to develop a hybrid method in order to delegate production operators to tasks, not only on the basis of a competence metric, but also on the basis of an individual effectiveness indicator and the risk assessment of production process stages. For this purpose, a novel method of assessing the effectiveness of the use of available resources when implementing production processes (based on a set of quantitative indicators and taking into account the categorization of production losses) was juxtaposed using the Tabu Search algorithm and the FMEA method. The method enables a near-optimal solution to be generated—one in which operators are assigned to production tasks in the most effective way possible, which in turn minimizes both the risk of production delays and quality defects due to operator fault.