The sharing economy is becoming increasingly popular in many sectors also thanks to the fact that not only young generation, but people of almost all ages are digitally literate, and therefore they readily participate in sharing economy practices. A very important part of their motivation comes from the knowledge that it is a great way to save money, but a no less important reason is the philosophical aspect, which is consistent with efforts to promote more sustainable consumption practices that prioritize access over ownership. The sharing economy has become a subject of interest not only among researchers and practitioners but also among policymakers and stakeholders who are concerned about the further extension of digital ecosystems. Fortunately, theorists from many fields, such as the commercial economy, gift economy, entrepreneurship, digital transformation, etc., pay due attention to this phenomenon. However, there is still room for focused exploration of this entrepreneurial and innovative approach from specific views in order to bring useful findings and insights on given issues. The intention of this study is to analyze the existing related literature and categorize sharing economy-based business models in relation to traditional business models. Potentially, this study can also contribute to a better understanding of the perspectives of the sharing economy in the transition of SMEs towards the Industry 4.0 paradigm.

In this study, a systematic review on production scheduling based on reinforcement learning (RL) techniques using especially bibliometric analysis has been carried out. The aim of this work is, among other things, to point out the growing interest in this domain and to outline the influence of RL as a type of machine learning on production scheduling. To achieve this, the paper explores production scheduling using RL by investigating the descriptive metadata of pertinent publications contained in Scopus, ScienceDirect, and Google Scholar databases. The study focuses on a wide spectrum of publications spanning the years between 1996 and 2024. The findings of this study can serve as new insights for future research endeavors in the realm of production scheduling using RL techniques.

Characterising existing approaches to operational complexity, it can be stated that for those metrics are characterful different factors used as variables such as product structure, machine composition, number of technological functions performed by machine, and others. Moreover, the complexity metrics using the information content as a basis can be divided into two groups: those which define complexity as an absolute entropy quantity, and metrics defining complexity as the relative entropy magnitude. Our view on the operational complexity is based on an assumption that process complexity value obtained as a sum of the partial complexities is not so important than a balanced operational complexity value expressing relative quantity to equilibrium levels. Accordingly, this paper introduces the novel operational complexity measure that initially identifies operational complexities of individual machines based on the number of parts, machines and operations. In the subsequent steps, these sub-measures are used to define summary complexity measure involving two balanced operational complexity characteristics. The novel measure can be effectively used to find the most suitable layout design alternative. For the purpose to prove its effectiveness, on two practical cases where tested its practicability by comparing it to the complexity indicator expressing the sum of the partial complexities.

Manufacturing systems can be considered as a network of machines/workstations, where parts are produced in flow shop or job shop environment, respectively. Such network of machines/workstations can be depicted as a graph, with machines as nodes and material flow between the nodes as links. The aim of this paper is to use sequences of operations and machine network to measure static complexity of manufacturing processes. In this order existing approaches to measure the static complexity of manufacturing systems are analyzed and subsequently compared. For this purpose, analyzed competitive complexity indicators were tested on two different manufacturing layout examples. A subsequent analysis showed relevant potential of the proposed method.