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The efficient operation of logistic processes requires a wide range of design tasks to ensure efficient, flexible and reliable operation of connected production and service processes. Autonomous electric vehicles support the flexible in-plant supply of cyber-physical manufacturing systems. Within the frame of this article, the extension of the Two-Echelon Vehicle Routing Problem with recharge stations is analyzed. The objective function of the optimization problem is the minimization of operation costs. The extension of 2E-VRP means that the second level vehicles (electric vehicles, must be recharged) come from one recharge station, then pick up the products from the satellite, visit the customers and return to the recharge station from where it started. We solved the route planning problem with the application of construction heuristics and improvement heuristics. The test results indicate that the combination of this approach provides a superior efficiency.

Logistic process is a basic factor in the success of manufacturing plants’ operation and has a direct impact on its efficiency, flexibility and reliability. Today’s successful operation of manufacturing processes views logistics as a high priority to ensure maximum utilization of resources. The material supply of manufacturing processes in the automotive industry is usually based on supermarkets and milk runs. This paper proposes an integrated supply model of manufacturing processes, which includes facility location and assignment. After a careful literature review, this paper introduces a mathematical model to formulate the problem of supermarkets and milk run based supply of machines. The model seeks the optimal location of buffers as well as the optimal assignment of buffers and machines so as to minimize the material handling costs while taking into account order limits of machines and capacities of resources. Next, we demonstrate an enhanced black hole algorithm dealing with multi-objective supply chain model to find the optimal structure of the system. Numerical results demonstrate how the proposed model supports the efficiency, flexibility and reliability of the manufacturing process.

Each factory and manufacturing plant needs a flexible and reliable in-plant resource supply to serve production processes efficiently. Manufacturing systems are composed of several numbers of elements, workstations, machines and logistics resources. Production line is a complex system because of the complicated manufacturing process, multiple types, high machining difficulty and many special processes in it. In the Industry 4.0 based on smart manufacturing, it is essential to support the processes with intelligent sensor networks. In this article, we give a brief overview about sensors often used in manufacturing processes. Sensor networks generate a massive and increasing amount of data that needs to be processed. Computationally intensive algorithms are used for the data processing (image, voice and signal processing, different classification functions, numeric optimization routines). Finally, we discuss how GPGPU can improve the real-time processing of data generated by intelligent sensor networks.