Production scheduling is an optimizing problem that can contribute strongly to the competitive capacity of companies producing goods and services. A way to promote the survival and the sustainability of the organizations in this upcoming era of Industry 4.0 (I4.0) is the efficient use of the resources. A complete failure to stage tasks properly can easily lead to a waste of time and resources, which could result in a low level of productivity and high monetary losses. In view of the above, it is essential to analyse and continuously develop new models of production scheduling. This paper intends to present an I4.0 oriented decision support tool to the dynamic scheduling. After a fist solution has been generated, the developed prototype has the ability to create new solutions as tasks leave the system and new ones arrive, in order to minimize a certain measure of performance. Using a single machine environment, the proposed prototype was validated in an in-depth computational study through several instances of dynamic problems with stochastic characteristics. Moreover, a more robust analysis was done, which demonstrated that there is statistical evidence that the proposed prototype performance is better than single method of scheduling and proved the effectiveness of the prototype.

Even while the scientific community has shown great interest in the analysis of meta-heuristics, the analysis of their parameterization has received little attention. It is the parameterization that will adapt a meta-heuristic to a problem, but it is still performed, mostly, empirically. There are multiple parameterization techniques; however, they are time-consuming, requiring considerable computational effort and they do not take advantage of the meta-heuristics that they parameterize. In order to approach the parameterization of meta-heuristics, in this paper, a self-parameterization framework is proposed. It will automatize the parameterization as an optimization problem, precluding the user from spending too much time on parameterization. The model will automate the parameterization through two meta-heuristics: A meta-heuristic of the solution space and one of the parameter space. To analyze the performance of the framework, a self-parameterization prototype was implemented. The prototype was compared and analyzed in a SP (scheduling problem) and in the TSP (traveling salesman problem). In the SP, the prototype found better solutions than those of the manually parameterized meta-heuristics, although the differences were not statistically significant. In the TSP, the self-parameterization prototype was more effective than the manually parameterized meta-heuristics, this time with statistically significant differences.

Goal: The main goal of this research is to analyse the behaviour of a set of ten lot-sizing methods applied to different application scenarios, within the context of more traditional MRP-based manufacturing environments and on JIT/ Kanbans oriented ones. Design/Methodology/Approach: After an extended literature review, a quantitative research method is used to provide a comparative analysis on the performance of the lot-sizing methods under different simulated application scenarios, with variations in demand and peaks of seasonality. Moreover, a final summary provides the error deviations for lot-sizing methods regarding increases in demand variations and seasonality indexes. Results: The study analyses lot-sizing methods and discusses benefits and risks associated to its use in application scenarios marked by a considerable variation in demand or peaks in seasonality. Limitations of the investigation: As the application scenarios did not explore variations in the ordering and stock holding costs, further analysis including these kinds of variations is encouraged. Practical implications: The findings of this research enable the enhancement of the conscience of industrial practitioners, regarding the selection of best suited lot-sizing methods for being applied on each kind of manufacturing scenario, regarding MRP or JIT/ Kanban environments. Originality/Value: Given the diversity of the existing lot-sizing methods, for instance, the heuristic ones, authors can find it quite difficult to select appropriate methods for solving their problems for each kind of application scenario. Therefore, the present study can provide useful knowledge to better support decision making in the lot-sizing domain.

This paper proposes a novel and comprehensive framework for the integration of manufacturing management processes, spanning strategic and operational levels, within and across organizational boundaries. The framework combines a robust set of technologies—such as cyber-physical systems, digital twins, AI, and blockchain—designed to support real-time decision-making, interoperability, and collaboration in Industry 4.0 and 5.0 contexts. Implemented and validated in a Portuguese manufacturing group comprising three interoperating factories, the framework demonstrated its ability to improve agility, coordination, and stakeholder integration through a multi-layered architecture and modular software platform. Quantitative and qualitative feedback from 32 participants confirmed enhanced decision support, operational responsiveness, and external collaboration. While tailored to a specific industrial setting, the results highlight the framework’s scalability and adaptability, positioning it as a meaningful contribution toward sustainable, human-centric digital transformation in manufacturing environments.

Collaborative Manufacturing Scheduling (CMS) is not yet a properly explored decision making practice, although its potential for being currently explored, in the digital era, by combining efforts among a set of entities, either persons or machines, to jointly cooperate for solving some more or less complex scheduling problem, namely occurring in job shop manufacturing environments. In this paper, an interoperable scheduling system integrating a proposed scheduling model, along with varying kinds of solving algorithms, are put forward and analyzed through an industrial case study. The case study was decomposed in three application scenarios, for enabling the evaluation of the proposed scheduling model when envisioning the prioritization of internal–makespan-or external–number of tardy jobs-performance measures, along with a third scenario assigning a same importance or weight to both kinds of performance measures. The results obtained enabled us to realize that the weighted application scenario permitted reaching more balanced, thus a potentially more attractive global solution for the scheduling problem considered through the combination of different kinds of scheduling algorithms for the resolution of each underlying sub problem according to the proposed scheduling model. Besides, the decomposition of a global more complex scheduling problem into simpler sub-problems turns them easier to be solved through the different solving algorithms available, while further enabling to obtain a wider range of alternative schedules to be explored and evaluated. Thus, contributing to enriching the scheduling problem-solving process. A future exploration of the application in other types of manufacturing environments, namely occurring in the context of extended, networked, distributed or virtual production systems, integrating an increased and variable set of collaborating entities or factories, is also suggested.