Dynamic Cascaded Flow-Shop Scheduling Using an Evolutionary Greedy Algorithm

Production processes are inherently complex, often involving multiple production phases from raw materials to finished products, making joint scheduling problems a focal point of research. This paper addresses the dynamic cascaded flowshop joint scheduling problem, which integrates a distributed permutation flowshop in Phase 1 and a hybrid flowshop in Phase 2. The challenge involves both initial scheduling and dynamic response mechanisms for new job insertions. We propose an evolutionary greedy algorithm (EGA) aimed at minimizing total flowtime. The EGA employs a multi-start cooperative framework tailored to problem characteristics, alternating between a population-based evolutionary greedy algorithm for Phase 1 and an elitist-based greedy algorithm for Phase 2 to generate a robust and complete schedule. Upon new job insertions, three heuristic-driven response strategies enhance solution stability and adaptability. Additionally, phase-specific hybrid local search operators and an adaptive insertion strategy, leveraging knowledge-based problem properties, further improve solution quality and search efficiency. The experimental results indicate that the EGA outperforms five state-of-the-art algorithms, achieving an average improvement of 39% in RPI values across 480 instances. Moreover, the proposed local search mechanisms and dynamic response strategies significantly enhance its performance. Thus, the EGA is well-suited for addressing the studied problem.