Liang Pei, Fu Yaping, Ni Songyuan, Zheng Bing
School of Business, Qingdao University, Qingdao, 266071, China.
College of Civil Engineering and Architecture, Qingdao Agricultural University, Qingdao, 266109, China.
Environ Sci Pollut Res Int. 2021 May 20. doi: 10.1007/s11356-021-14124-w.
Nowadays, the reverse supply chain management receives much attention because of its critical role in environmental protection and economic development. Disassembly is very important in the reverse supply chain. It aims at dismantling valuable components from end-of-life products which are then remanufactured into like-new ones after reprocessing and reassembly operations. To efficiently organize and manage the remanufacturing process from the perspective of sustainable development, this work proposes a stochastic disassembly sequence planning problem with consideration of noise pollution and energy consumption to achieve disassembly profit maximization. A chance-constrained programming model is formulated to describe it mathematically. Then, a discrete marine predators algorithm combined with a stochastic simulation approach is specially designed. By conducting simulation experiments on some real-life instances and comparing the designed approach with two popularly known methods in literature, we mainly find that the proposed model and approach can make better disassembly plan for the investigated problem with maximal profit subject to the given noise pollution and energy consumption constraints. The results demonstrate that the proposed method can efficiently and effectively handle the considered problem, which contributes to reaching the highly reliable and environmentally sustainable disassembly process.
如今，逆向供应链管理因其在环境保护和经济发展中的关键作用而备受关注。拆卸在逆向供应链中非常重要。其目的是从报废产品中拆解出有价值的零部件，这些零部件经过再加工和重新组装后，再被制造成为全新的产品。为了从可持续发展的角度有效地组织和管理再制造过程，本文提出了一个考虑噪声污染和能源消耗的随机拆卸序列规划问题，以实现拆卸利润最大化。构建了一个机会约束规划模型对其进行数学描述。然后，专门设计了一种结合随机模拟方法的离散海洋捕食者算法。通过对一些实际案例进行模拟实验，并将所设计的方法与文献中两种知名方法进行比较，我们主要发现，所提出的模型和方法能够在给定的噪声污染和能源消耗约束条件下，为所研究的问题制定出具有最大利润的更好拆卸计划。结果表明，所提出的方法能够高效且有效地处理所考虑的问题，这有助于实现高度可靠且环境可持续的拆卸过程。
