EAAI Journal 2024 Journal Article
Evolutionary computation and reinforcement learning integrated algorithm for distributed heterogeneous flowshop scheduling
- Rui Li
- Ling Wang
- Wenyin Gong
- Jingfang Chen
- Zixiao Pan
- Yuting Wu
- Yang Yu
With the advancement of the global economy, there is a growing focus on distributed manufacturing. This study addresses the complex challenges posed by the distributed heterogeneous flow shop scheduling problem (DHFSP), wherein multiple machine processing speeds are taken into account. The primary objectives involve the simultaneous minimization of both makespan and total energy consumption. To tackle this intricate problem, we propose an evolutionary computation and reinforcement learning integrated algorithm (ECRLIA) approach. Initially, an optimization framework is meticulously crafted to synergistically integrate both evolutionary computation and reinforcement learning solvers. Subsequently, a multi-rule cooperation initialization is devised to expedite the pre-search process across all solvers. Following this, a competition-based cooperative evolutionary algorithm is introduced to conduct a global search, thereby providing an initial solution to the DHFSP. The interplay of competition and cooperation among individuals enhances convergence. Further, a Q-learning approach employing dual agents is designed to perform a local search, supplementing solutions that evolutionary algorithms may struggle to uncover. This learning method incorporates an auxiliary agent to evaluate the action predictions of the primary agent, ensuring more stable learning. The effectiveness of the proposed algorithm is assessed through numerical experiments, which validate the efficacy of the cooperation framework, initialization cooperation, and the enhanced Q-learning method. Furthermore, ECRLIA is benchmarked against five state-of-the-art algorithms for DHFSP, and the results affirm the significant superiority of the proposed ECRLIA in addressing DHFSP compared to other algorithms.