Scheduling is significant in improving the production efficiency and reducing delivery delays for manufacturing enterprises. Unlike the flexible job-shop scheduling problem, two special constraints are encountered in real-world power supply manufacturing systems: 1) periodic maintenance and 2) mandatory outsourcing. As the characteristics of these constraints are not considered in existing scheduling algorithms, schedules generated by most existing approaches are not optimal or even conflict with these constraints. In this article, a self-organizing neural scheduler (SoNS) is proposed to overcome this limitation. A long short-term memory encoder is developed to transform the variable-length structural information into fixed-length feature vectors. Moreover, the reinforcement learning model is proposed to automatically select policies for improving candidate schedules. To validate the effectiveness of the proposed algorithm, extensive experiments are conducted on over 300 problem instances. The nonparametric Kruskal-Wallis tests confirm that the proposed algorithm outperforms several state-of-the-art methods in terms of effectiveness and robustness within a limited computational budget. It demonstrates that the proposed SoNS can solve scheduling problems with the periodic maintenance and mandatory outsourcing constraints effectively.

This paper presents a human-computer cooperative brain storm optimization algorithm, which is based on an improved brain storm optimization algorithm with human intelligence in computer game. In our algorithm, the initial population is provided with some better ideas obtained by computer game. Moreover, converging operation and diverging operation also employ the solutions from different players to generate ideas during evolution process. With the help of human-machine cooperation, our algorithm, integrating strategy development capabilities of players with brain storm optimization algorithm, is applied to solve some complex optimized problems. We apply the proposed method to two-echelon vehicle routing problem to verify its effectiveness and usefulness.