The simultaneous integration of information from sensors with business data and how to acquire valuable information can be challenging. This paper proposes the simultaneous integration of information from sensors and business data. The proposal is supported by an industrial implementation, which integrates intelligent sensors and real-time decision-making, using a combination of PLC and PC Platforms in a three-level architecture: cloud-fog-edge. Automatic identification intelligent sensors are used to improve the decision-making of a dynamic scheduling tool. The proposed platform is applied to an industrial use-case in analytical Quality Control (QC) laboratories. The regulatory complexity, the personalized production, and traceability requirements make QC laboratories an interesting use case. We use intelligent sensors for automatic identification to improve the decision-making of a dynamic scheduling tool. Results show how the integration of intelligent sensors can improve the online scheduling of tasks. Estimations from system processing times decreased by over 30%. The proposed solution can be extended to other applications such as predictive maintenance, chemical industry, and other industries where scheduling and rescheduling are critical factors for the production.

In the last few decades, there has been a growing necessity for systems that handle market changes and personalized customer needs with near mass production efficiency, defined as the new mass customization paradigm. The Industry 5.0 vision further enhances the human-centricity aspect, in the necessity for manufacturing systems to cooperate with workers, taking advantage of their problem-solving capabilities, creativity, and expertise of the manufacturing process. A solution is to develop a flexible manufacturing system capable of handling different customer requests and real-time decisions from operators. This paper tackles these aspects by proposing a digital twin of a robotic system for solution preparation capable of making real-time scheduling decisions and forecasts using a simulation model while allowing human interventions. A discrete event simulation model was used to forecast possible system improvements. The simulation handles real-time scheduling considering the possibility of adding identical parallel machines. Results show that processing multiple jobs simultaneously with more than one machine on critical processes, increasing the robot speed, and using heuristics that emphasize the shortest transportation time can reduce the overall completion time by 82%. The simulation model has an animated visualization window for a deeper understanding of the system.

This paper presents a systematic review on reinforcement learning approaches for combinatorial optimization problems based on real-world industrial applications. While this topic is increasing in popularity, explicit implementation details are not always available in the literature. The main objective of this paper is characterizing the agent–environment interactions, namely, the state space representation, action space mapping and reward design. Also, the main limitations for practical implementation and the needed future developments are identified. The literature selected covers a wide range of industrial combinatorial optimization problems, found in the IEEE Xplore, Scopus and Web of Science databases. A total of 715 unique papers were extracted from the query. Then, out-of-scope applications, reviews, surveys and papers with insufficient implementation details were removed. This resulted in a total of 298 papers that align with the focus of the review with sufficient implementation details. The state space representation shows the most variety, while the reward design is based on combinations of different modules. The presented studies use a large variety of features and strategies. However, one of the main limitations is that even with state-of-the-art complex models the scalability issues of increasing problem complexity cannot be fully solved. No methods were used to assess risk of biases or automatically synthesize the results.