Wang Shuqiang, Zhang Xi
School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan, 430068, People's Republic of China.
Innovation Demonstration Base of Ecological Environment Geotechnical and Ecological Restoration of Rivers and Lakes, Hubei University of Technology, Wuhan, 430068, People's Republic of China.
Sci Rep. 2023 Sep 12;13(1):15094. doi: 10.1038/s41598-023-42374-w.
To address the processing scheduling problem involving multiple molds, components, and floors, we propose the Genetic Grey Wolf Optimizer (GGA) as a means to optimize the production scheduling of components in a production line. This approach combines the Grey Wolf algorithm with the genetic algorithm. Previous methods have overlooked the storage requirements arising from the delivery characteristics of prefabricated components, often resulting in unnecessary storage costs. Intelligent algorithms have been demonstrated to be effective in production scheduling, and thus, to enhance the efficiency of prefabricated component production scheduling, our study presents a model incorporating a production objective function. This model takes into account production resources and delivery characteristics constraints. Subsequently, we develop a hybrid algorithm, combining the grey wolf algorithm with the genetic algorithm, to search for the optimal solution with a minimal storage cost. We validate the model using a case study, and the experimental results demonstrate that GAGWO successfully identifies the best precast production schedule. Furthermore, the precast production plan, considering the delivery method, is found to be reasonable.
为解决涉及多个模具、组件和楼层的加工调度问题，我们提出遗传灰狼优化算法（GGA），作为优化生产线中组件生产调度的一种手段。这种方法将灰狼算法与遗传算法相结合。以往的方法忽略了预制构件交付特性所产生的存储需求，常常导致不必要的存储成本。智能算法已被证明在生产调度中是有效的，因此，为提高预制构件生产调度的效率，我们的研究提出了一个包含生产目标函数的模型。该模型考虑了生产资源和交付特性约束。随后，我们开发了一种将灰狼算法与遗传算法相结合的混合算法，以寻找存储成本最小的最优解。我们通过案例研究对模型进行了验证，实验结果表明GAGWO成功地确定了最佳的预制生产调度。此外，考虑交付方式的预制生产计划被认为是合理的。
