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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.

With the advantages of new technologies and rising demand from customers, it is necessary to improve the manufacturing process. This necessity was recognized by the industry; therefore, the concept of Industry 4.0 has been implemented in various areas of manufacturing and services. The backbone and main aspect of Industry 4.0 is digitalization and the implementation of technologies into processes. While this concept helps manufacturers with the modernization and optimization of many attributes of the processes, Industry 5.0 takes a step further and brings importance to the human factor of industry practice, together with sustainability and resilience. The concept of Industry 5.0 contributes to the idea of creating a sustainable, prosperous, and human-friendly environment within companies. The main focus of the article is to analyze the existing literature regarding what is missing from the successful implementation of human centricity into industry practice, namely in small and medium-sized factories (SMEs). These findings are then presented in the form of requirements and barriers for the implementation of human centricity into SME factories, which can serve as guidelines for implementing human-centered manufacturing using axiomatic design theory in SMEs, which can serve as a roadmap for practitioners.

As mass customization becomes more pervasive in many sectors, researchers need to update traditional approaches to the optimization of critical performance and design parameters in order to help companies in their effort to implement this strategy. In general, implementation of mass customization from a manufacturing perspective is frequently focused on shortening cycle times, reducing production cost, and increasing throughput rate of parts. In this paper, process structure modularity impact on manufacturing lead times and throughput rates is explored. An important precondition to explore these relationships is the awareness that process modularity is conceptualized and quantified in an appropriate way. For this purpose, three independent modularity measures were employed to provide more reliable assessment of this system property. The relationships were investigated on the basis of simulation experiments using deterministic models of alternative process structures. For the purpose of the relationships exploration, two case studies were conducted, theoretical and practical ones. The results from the experiments showed that there are moderate correlations between process modularity and manufacturing lead time (ρ = −0.45), as well as between process modularity and throughput rate (ρ = 0.45).

In principle, modular or integral character of manufacturing lines depends on the topological designs of products and determined operation tasks. On the other hand, in specific situations there is an articulated need for modular design in smart manufacturing systems since modular layouts are a crucial step towards agile production via smart manufacturing. The aim of this paper is to explore how the modular layout relates to manufacturing lead time (MLT) and to operational complexity of smart manufacturing systems. For this purpose, topologically different models of alternative process layouts were simulated and tested, while MLT values were obtained using Tecnomatix Plant Simulation. The obtained positive findings of this research could be useful not only in selection of the most suitable process design from the alternative ones, but especially in deepening the knowledge and bettering understanding of the concept of optimal network modularity.

Product variety complexity assessment plays a vital role in system design, as it has tremendous negative effects on manufacturing complexity in assembly systems and supply chains. On the other hand, practitioners and researchers frequently consider the number of product variants as a sufficient measure to be used to manage this kind of complexity. However, as shown in this study, such a measure does not reflect all pertinent features of complexity. Therefore, the main goal of the paper is to develop a measurement method for product variety complexity that adequately reflects relevant relations between the portfolio of optional components and the number of product variants. As presented in the paper, the concept of information theory can be effectively applied to measure product variety complexity. Moreover, such a measure can also be useful to better understand this system’s properties in order to reduce the level of variety-induced complexity. As such, the proposed method can be viewed as a complementary tool for reducing manufacturing complexity in terms of mass customization. The developed complexity metric was successfully tested on a realistic design example.

This Special Issue is a collection of some of the latest advancements in designing and scheduling smart manufacturing systems [...]

Research of manufacturing cell design problems is still pertinent today, because new manufacturing strategies, such as mass customization, call for further improvement of the fundamental performance of cellular manufacturing systems. The main scope of this article is to find the optimal cell design(s) from alternative design(s) by multi-criteria evaluation. For this purpose, alternative design solutions are mutually compared by using the selected performance criteria, namely operational complexity, production line balancing rate, and makespan. Then, multi-criteria decision analysis based on the analytic hierarchy process method is used to show that two more-cell solutions better satisfy the determined criteria of manufacturing cell design performance than three less-cell solutions. The novelty of this research approach refers to the use of the modification of Saaty’s scale for the comparison of alternatives in pairs based on the objective assessment of the designs. Its benefit lies in the exactly enumerated values of the selected criteria, according to which the points from the mentioned scale are assigned to the alternatives.