A Learning-Based Algorithm for Distributed Hybrid Flow Shop Scheduling
The distributed hybrid flow shop scheduling problem holds significant importance in modern manufacturing as it contributes to enhanced productivity and cost reduction.However,the intricate nature of the DHFSP poses a major challenge when seeking solutions.To address this issue,this paper proposes an adaptive learning algorithm based on iterative greedy that incorporates a learning mechanism with the objective of effectively minimizing the maximum completion time.The algorithm comprises three components:firstly,an initialization strategy is employed to construct the initial solution;secondly,the core of the algorithm lies in its dynamic learning mechanism that utilizes historical feedback to intelligently select perturbation strategies based on accumulated optimization experience,thereby progressively enhancing the quality of the solution;and lastly,a roulette selection strategy further enhances the algorithm's ability to explore the global optimal solution.The experimental results demonstrate that the algorithm proposed exhibits significant performance advantages over existing comparison algorithms in addressing the DHFSP.