Zhang Wenyu, Wang Jun, Liu Xiangqi, Zhang Shuai
School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, 310018, China.
Hangzhou Dianzi University Information Engineering School, Hangzhou, 311305, China.
Environ Sci Pollut Res Int. 2023 May;30(22):62744-62761. doi: 10.1007/s11356-023-26219-7. Epub 2023 Mar 22.
As a resource-conserving and environmentally friendly manufacturing paradigm, remanufacturing with the potential to realize sustainability in production has been extensively investigated. Scheduling plays a significant role in achieving the remanufacturing benefits. However, the remanufacturing process involves intricate uncertainties because it takes end-of-life products with different qualities as workblanks, which increases the risk of rework and complicates remanufacturing scheduling. Though the traditional stochastic optimization methods or fuzzy theory have been employed to address uncertainties in the remanufacturing scheduling problem, they are constrained with the limited historical data which renders it difficult to describe uncertainties accurately and intuitively. Therefore, a new uncertain remanufacturing scheduling model with rework risk is proposed, in which the interval grey numbers are applied to describe the uncertainty clearly and consider the rework risk in remanufacturing process. To solve this model, a hybrid optimization algorithm that combines differential evolution and particle swarm optimization algorithms through an efficient representation scheme is proposed. Besides, this algorithm integrates multiple improvements to maintain the diversity of the population and enhance its performance. Simulation experiments are conducted on 18 sets of instances with different scales, and the results demonstrated that the proposed algorithm obtains a better optimal solution than other baseline algorithms on 17 sets of instances. The main finding of this study is providing a new method for solving uncertain remanufacturing scheduling problem with rework risk practically and effectively.
作为一种资源节约型和环境友好型制造模式，具有实现生产可持续性潜力的再制造已得到广泛研究。调度在实现再制造效益方面起着重要作用。然而，再制造过程涉及复杂的不确定性，因为它将不同质量的报废产品作为毛坯，这增加了返工风险并使再制造调度复杂化。尽管传统的随机优化方法或模糊理论已被用于解决再制造调度问题中的不确定性，但它们受到有限历史数据的限制，难以准确直观地描述不确定性。因此，提出了一种考虑返工风险的新型不确定再制造调度模型，其中应用区间灰数来清晰地描述不确定性并考虑再制造过程中的返工风险。为求解该模型，提出了一种通过有效表示方案将差分进化算法和粒子群优化算法相结合的混合优化算法。此外，该算法集成了多种改进措施以保持种群多样性并提高其性能。对18组不同规模的实例进行了仿真实验，结果表明所提算法在17组实例上比其他基准算法获得了更好的最优解。本研究的主要成果是为实际有效解决具有返工风险的不确定再制造调度问题提供了一种新方法。
