Abstract Due to the increasing greenhouse gas emissions and the energy crisis, the manufacturing industry which is one of the most energy intensive sector is paying close attention to the improvement of environmental performance efficiency. Therefore, in this paper the automated assembly line is balanced in a sustainable way which aims to optimize a green manufacturing objective (the total energy consumption) and a productivity-related objective (similar working load) simultaneously. A comprehensive total energy consumption of each processing stage was analyzed and modeled. To make the model more practical, a sequence-based changeover time and robots with different efficiencies and energy consuming rates are considered and optimized. To properly solve the problem, the proposed novel optimal solution takes the well-known MOEA/D as a base and incorporates a well-designed coding scheme and a problem-specific local search mechanism. Computational experiments are conducted to evaluated each improving strategies of the algorithm and its superiority over two other high-performing multi-objective optimization methods. The model allows decision makers to select more sustainable assembly operations based on their decision impacts in both productivity and energy-saving.

中文翻译：

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基于分解的可持续机器人流水线平衡问题的双目标优化

摘要 由于不断增加的温室气体排放和能源危机，作为能源密集型行业之一的制造业正在密切关注环境绩效效率的提高。因此，在本文中，自动化装配线以可持续的方式平衡，旨在同时优化绿色制造目标（总能耗）和与生产力相关的目标（类似的工作负荷）。对每个加工阶段的综合总能耗进行了分析和建模。为了使模型更实用，考虑并优化了基于序列的转换时间和具有不同效率和能耗率的机器人。为妥善解决问题，所提出的新颖最优解以众所周知的 MOEA/D 为基础，并结合了精心设计的编码方案和特定于问题的局部搜索机制。进行计算实验以评估算法的每种改进策略及其相对于其他两种高性能多目标优化方法的优越性。该模型允许决策者根据他们在生产力和节能方面的决策影响来选择更可持续的装配操作。