The problem of production flow in manufacturing systems is analyzed. The machines can be operated by workers or by robots, since breakdowns and human factors destabilize the production processes that robots are preferred to perform. The problem is how to determine the real difference in work efficiency between humans and robots. We present an analysis of the production efficiency and reliability of the press shop lines operated by human operators or industrial robots. This is a problem from the field of Operations Research for which the Discrete Event Simulation (DES) method has been used. Three models have been developed, including the manufacturing line before and after robotization, taking into account stochastic parameters of availability and reliability of the machines, operators, and robots. We apply the OEE (Overall Equipment Effectiveness) indicator to present how the availability, reliability, and quality parameters influence the performance of the workstations, especially in the short run and in the long run. In addition, the stability of the simulation model was analyzed. This approach enables a better representation of real manufacturing processes.

The accuracy of prediction and detection capability have a strong influence over the efficiency of the bottleneck, all equipment and the production system. The function of predictive scheduling is to obtain stable and robust schedules for a shop floor. The first objective is to present an innovative maintenance planning and production scheduling method. The approach consists of four modules: a database to collect information about failure-free times, a prediction module of failure-free times, predictive scheduling and rescheduling module, a module for evaluating the accuracy of prediction and maintenance performance. The second objective is to apply the proposed methods for a job shop scheduling problem. Usually, researchers who are concerned about maintenance scheduling do not take unexpected disturbances into account. They assume that machines are always available for processing tasks during the future-planned production time. Moreover, researches use the criteria that are not effective to deal with the situation of unpredicted failures. In this paper, a method based on probability theory is proposed for maintenance scheduling. For unpredicted failures, a rescheduling method is also proposed. The evaluation module which gives information about the degradation of each performance measure and the stability of a schedule is proposed.

The integration of discrete simulations, artificial intelligence methods, and the theory of probability in order to obtain a high flexibility of the production system is crucial. In this paper, the concept of a smart factory operation is proposed along with the idea of data exchange architecture, simulation creation, performance optimization, and predictive analysis of the production process conditions. A Digital Twin for a hybrid flow shop from the automotive industry is presented as a case study. In the paper, the Ant Colony Optimization (ACO) algorithm is developed for multi-criteria scheduling problems in order to obtain a production plan without delays and maximum resource utilization. The ACO is compared to the immune algorithm and genetic algorithm. The best schedules are achieved with low computation time for the Digital Twin. By predicting the reliability parameters of the limited resources of the Digital Twin, stable deadlines for the implementation of production tasks are achieved. Mean Time To Failure and Mean Time of Repair are predicted for a real case study of an electric steering gear production line. The presented integration and data exchange between the elements of the smart factory: a Digital Twin, a computing module including an optimization, prediction, and simulation methods fills the gap between theory and practice for Industry 4.0. The paper presents measurable benefits of integration of discrete simulation tools, historical data analysis, and optimization methods.

The high productivity of a production process has a major impact on the reduction of the production cost and on a quick response to changing demands. Information about a failure-free machine operation time obtained in advance allows the users to plan preventive maintenance in order to keep the machine in a good operational condition. The introduction of maintenance work into a schedule reduces the frequency of unpredicted breaks caused by machine failures. It also results in higher productivity and in-time production. The foregoing of this constitutes the main idea of the predictive scheduling method proposed in the paper. Rescheduling of disrupted operations, with a minimal impact on the stability and robustness of a schedule, is the main idea of the reactive scheduling method proposed. The first objective of the paper is to present a hybrid multi-objective immune algorithm (H-MOIA) aided by heuristics: a minimal impact of disrupted operation on the schedule (MIDOS) for predictive scheduling and a minimal impact of rescheduled operation on the schedule (MIROS) for reactive scheduling. The second objective is to compare the H-MOIA with various methods for predictive and reactive scheduling. The H-MOIA + MIDOS is compared to two algorithms, identified in reference publications: (1) an algorithm based on priority rules: the least flexible job first (LFJ) and the longest processing time (LPT) (2) an Average Slack Method. The H-MOIA + MIROS is compared to: (1) an algorithm based on priority rules: the LFJ and LPT and (2) Shifted Gap-Reduction. This paper presents the research results and computer simulations.

Today, enterprises are multitasking, with branches set up all over the world. Virtual enterprises are created to make better use of existing resources, improve the quality of manufactured products and agilely respond to customer requirements. In order to fully meet the requirements of enterprises, a decentralized structure of data registration and transmission and authentication of network users is needed. The information collected via the Internet of Things and flowing based on the properties of the Blockchain (BC) network facilitates enterprise resource planning and enables the integration of internal processes, especially when planning, changing the current or introducing new production. The aim of this paper is to present the concept of using a common data register in BC technology, which enables a number of applications related to the automation of the process of selecting human resources for production tasks. The paper presents an analysis of the problem related to the integration of production scheduling and human resource management with blockchain technology. Also presented is a literature analysis on scheduling, blockchain technology and data storage in the blockchain network. The analysis presents how the blockchain network works and how exactly it fits into production engineering with its advantages and disadvantages. An employee evaluation method based on the resource work history and determination of its current value within individual competencies is presented. The integration of production scheduling and human resource management with the use of BC technology is simulated. The most important advantage is faster and more effective planning thanks to the elimination of all intermediary channels in the flow of production transactions. Production tasks are balanced with production capacity in entities belonging to the virtual enterprise in parallel. For future research, different online planning algorithms will be developed and compared to achieve consortium members’ consensus on production and human resources planning.