Deep reinforcement learning-based memetic algorithm for energy-aware flexible job shop scheduling with multi-AGV

Abstract

The integration of manufacturing and logistics scheduling issues in shop operations has garnered considerable attention. Concurrently, escalating concerns about global warming have propelled the emergence of green manufacturing as a critical challenge. Notably, extant research in this domain lacks an incorporation of green metrics within the framework of manufacturing and logistics-integrated scheduling. Furthermore, the determination of a critical block remains a challenging aspect, with an absence of consideration for a neighborhood structure founded on the critical block. Moreover, prior endeavors have predominantly relied on Q-learning to augment evolutionary algorithms, a strategy criticized for its limited learning capacity. Consequently, this study addresses these gaps by presenting an energy-efficient flexible job Shop scheduling with multi-autonomous guided vehicles (EFJS-AGV). The primary objectives are the simultaneous minimization of makespan and total energy consumption. To tackle this NP-hard problem, a deep Q-network-based memetic algorithm is proposed. The devised algorithm incorporates several distinctive features. Firstly, the strength Pareto evolutionary algorithm (SPEA2) is employed to swiftly explore the objective space, enhancing convergence and diversity. Secondly, four distinct local search operators based on critical paths and blocks are devised to efficiently reduce makespan. Thirdly, deep reinforcement learning is harnessed to understand the interplay between solutions and action selection. This understanding aids the evolutionary algorithm in selecting the most optimal operator. The efficacy of the proposed algorithm is rigorously evaluated through a comparative analysis with five state-of-the-art algorithms. The assessment is conducted on two benchmark datasets encompassing 20 instances. The numerical experimental results affirm the effectiveness of the proposed enhancements and algorithms. Furthermore, the superior performance of the proposed algorithm in addressing the EFJS-AGV substantiates its robustness and applicability.