A cooperative iterated greedy algorithm for the distributed flowshop group robust scheduling problem with uncertain processing times

This paper studies a distributed flowshop group robust scheduling problem with uncertain processing times, which has great significance in actual production activities. First, we formulate the problem and establish a robust mathematical model with an expect-risk rule. Second, we develop an effective cooperative iterated greedy (CIG) algorithm to address the studied problem. In the CIG, we propose a heuristic method with two modified sequence rules to obtain an initial solution. We present a dummy scenario method to reduce the complexity of the multi-scenario environment and speed up the process of convergence. We utilize different iterated greedy processes to optimize the family scheduling sub-problem and job scheduling sub-problem respectively. In each iterated greedy process, we design the corresponding operators based on the problem-specific characteristics. We also propose a cooperation mechanism to link the iterated greedy processes to emphasize the coupling relationship between the two sub-problems. Finally, we conduct comparative and comprehensive evaluation experiments by comparing the CIG with six high-performing algorithms in the literature. The results indicate that the proposed CIG significantly outperforms the other competitors from the average relative deviation index.