Self-Adaptive Differential Evolution-Enhanced Quality-Diversity Optimization for Distributed Flexible Job-Shop Scheduling With Transportation

Distributed Flexible Job Shop Scheduling with Transportation Constraints (DFJSP-T) is widely used in manufacturing workshops across various industries, as it more accurately represents real-world production scenarios. However, many existing studies do not fully leverage critical problem features or domain knowledge, limiting the ability of current algorithms to find high-quality solutions for complex large-scale problems. To address these challenges, this paper proposes a Self-Adaptive Differential Evolution Enhanced Quality-Diversity Optimization (SADE-QD). The SADE-QD incorporates domain knowledge in three main ways: (1) it models the problem using machine idle and job transportation features, allowing the algorithm to retain solutions with diverse behaviors; (2) it introduces a knowledge-guided heuristic search strategy based on critical path to discover more high-quality solutions; and (3) it applies an self-adaptive differential evolution search method that leverages machine idle and transportation features to explore a broader range of solutions. This helps valuable information from the feature space contribute directly to the search process. Experiments on small, medium, and large scale benchmarks show that SADE-QD achieves an average makespan reduction of 12% compared to several recent state-of-the-art algorithms.