Real-time multi-factory scheduling in Industry 4.0 with virtual alliances

Abstract

The ever-changing and dynamic market environment requires applying job shop systems based on real-time data. The establishment of physical-virtual systems in the production process has led to the emergence of intelligent factories. Compared with those employing traditional production methods, such factories manufacture products with higher quality, higher production speed, and other economic benefits. Regarding the virtual connections of factories, events such as the arrival of new jobs, and machine breakdowns, are identified by Radio Frequency Identification System between different production units, and related decisions are made quickly and carefully. In this intelligent job shop scheduling, it is assumed that independent factories create virtual production networks in which, each factory focuses on its own interests. Regarding the importance of this issue in today’s industry, this research explores the real-time scheduling problem in multi-agent production networks distributed in smart factories. Since this problem is a combination of static scheduling and real-time scheduling, a bi-objective model of mixed-integer linear programming is first developed. An approach is then proposed to solve the dynamic real-time scheduling problem. In addition, a learning-based memetic algorithm for solving large-size bi-objective instances is proposed due to the NP-hardness of the considered problem. Afterward, the results of the proposed algorithm are compared with the hybrid Pareto-based tabu search algorithm. The computational results show that in large-size instances, the proposed algorithm outperforms the competing algorithm.