Distributed assembly flexible job shop scheduling with dual-resource constraints via a deep Q-network based memetic algorithm

Abstract

The distributed flexible job shop scheduling problem (DFJSP) has garnered significant attention due to the shift of production paradigms. However, existing research of DFJSP primarily focuses on machine resources while neglecting worker resources, which play a crucial role in enhancing productivity. Furthermore, manufacturing processes often involve both processing and assembly stages. An integrated approach to scheduling the two stages can significantly enhance efficiency and reduce costs. This study addresses the distributed assembly flexible job shop scheduling problem with dual resource constraints (DAFJSP-DRC), aiming to minimize total product tardiness (TPT), total energy consumption (TEC), and total cost (TCO). To tackle this problem, we develop a mixed-integer programming (MIP) model and propose a deep Q-network-based memetic algorithm (DQNMA). In DQNMA, high-quality initial solutions are generated based on processing resources, and a two-stage decoding mechanism is designed to get efficient scheduling schemes. Then, crossover and mutation operators for critical factories are proposed, and a deep Q-network is designed to dynamically adjust the crossover and mutation rates. Furthermore, eight neighborhood structures are designed to enhance solution diversity, while a tabu search-based local search strategy improves the algorithm's exploration and exploitation capabilities. Eventually, extensive experimental results demonstrate the effectiveness of the proposed strategies in enhancing the performance of DQNMA. Comparative analysis against four state-of-the-art multi-objective algorithms validates the superiority and effectiveness of the designed algorithm.