Detailed scheduling has traditionally been optimized for the reduction of makespan and manufacturing costs. However, growing awareness of environmental concerns and increasingly stringent regulations are pushing manufacturing towards reducing the carbon footprint of its operations. Scope 2 emissions, which are the indirect emissions related to the production and consumption of grid electricity, are in fact estimated to be responsible for more than one-third of the global GHG emissions. In this context, carbon-aware scheduling can serve as a powerful way to reduce manufacturing's carbon footprint by considering the time-dependent carbon intensity of the grid and the availability of on-site renewable electricity. This study introduces a carbon-aware permutation flow-shop scheduling model designed to reduce scope 2 emissions. The model is formulated as a mixed-integer linear problem, taking into account the forecasted grid generation mix and available on-site renewable electricity, along with the set of jobs to be scheduled and their corresponding power requirements. The objective is to find an optimal day-ahead schedule that minimizes scope 2 emissions. The problem is addressed using a dedicated memetic algorithm, combining evolutionary strategy and local search. Results from computational experiments confirm that by considering the dynamic carbon intensity of the grid and on-site renewable electricity availability, substantial reductions in carbon emissions can be achieved.

翻译：传统上，详细调度一直以缩短制造周期和降低制造成本为优化目标。然而，日益增长的环境问题意识与日趋严格的法规正推动制造业减少其运营的碳足迹。事实上，范围2排放——即与电网电力生产和消耗相关的间接排放——估计占全球温室气体排放量的三分之一以上。在此背景下，碳感知调度通过考虑电网随时间变化的碳强度以及现场可再生电力的可用性，可以成为减少制造业碳足迹的有效途径。本研究引入了一种旨在减少范围2排放的碳感知置换流水车间调度模型。该模型被构建为一个混合整数线性规划问题，考虑了预测的电网发电结构、可用的现场可再生电力、待调度作业集合及其相应的电力需求。目标是找到一个最小化范围2排放的最优日前调度方案。该问题通过一种结合进化策略与局部搜索的专用模因算法进行求解。计算实验结果表明，通过考虑电网的动态碳强度与现场可再生电力的可用性，可以实现碳排放的显著降低。