Handling integrated transportation and production scheduling via deep-Q-network-enhanced multi-objective quality–diversity algorithm

Quality–diversity (QD) optimization, as a paradigm of evolutionary algorithms, aims to generate diverse and high-performing solutions. However, existing QD algorithms fail to leverage historical and domain sequential scheduling knowledge effectively, making it challenging for them to handle large-scale discrete problems with conflicting objectives. This article proposes an extension of QD optimization algorithms, called the Deep Reinforcement Learning Enhanced Multi-Objective MAP-Elites (DMOME) algorithm, to solve a real-world combinatorial optimization problem. The DMOME algorithm seamlessly combines the feature diversity of solutions, domain knowledge heuristics and deep reinforcement learning techniques by adding Pareto fronts to different cells. The results on real-world mechanical processing factory instances demonstrate that the DMOME algorithm outperforms state-of-the-art algorithms, such as the enhanced genetic algorithm, achieving improvements of 25.6% to 59.4% in performance metrics.