A New Uncertain Remanufacturing Scheduling Model with Rework Risk Using Hybrid Optimization Algorithm

As a resource-conserving and environmental-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 given, demonstrating that the proposed algorithm provides a better optimal solution than other baseline algorithms for solving the remanufacturing scheduling problem.

作为一种资源节约型与环境友好型的制造范式，具备实现生产可持续发展潜力的再制造（remanufacturing）已得到广泛研究。调度对于实现再制造效益具有重要作用。然而，再制造过程存在复杂的不确定性：其以不同品质的报废产品作为毛坯工件，这会提升返工风险并使再制造调度问题更为复杂。尽管传统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，但这类方法受限于有限的历史数据，难以精准且直观地刻画不确定性。为此，本文提出一种考虑返工风险的不确定再制造调度模型，该模型采用区间灰数（interval grey numbers）清晰刻画不确定性，并纳入再制造过程中的返工风险因素。针对该模型，本文提出一种通过高效编码方案融合差分进化（differential evolution）与粒子群优化（particle swarm optimization）算法的混合优化算法。此外，该算法集成了多项改进策略，以维持种群多样性并提升算法性能。仿真实验结果表明，相较于其他基准算法，所提算法在求解再制造调度问题时可获得更优的最优解。