Abstract: To achieve the green manufacturing transformation of enterprises,a mathematical model for the Distributed Assembly Permutation Flowshop Scheduling Problem with Renewable Energy (DAPFSP-RE) was constructed,aiming to minimize the maximum completion time and total carbon emissions while ensuring production efficiency.A Two-stage NSGA-Ⅱ and Simulated Annealing algorithm (TNSA) was proposed to solve the problem,which included a decoding method incorporating low-carbon factory selection strategies and energy scheduling.In the first stage,a reverse learning initialization method was adopted to generate a dual-population structure,and the solution space was searched through crossover,mutation and a binary tournament selection operation with adaptive elite retention.In the second stage,the AMOSA algorithm was used to perform simulated annealing operations on the non-dominated solution set obtained in the first stage to escape local optima.Through simulation experiments on cases of different scales,the important role of renewable energy in reducing carbon emissions during the production process and the effectiveness and competitiveness of the proposed algorithm for solving DAPFSP-RE were verified.

Key words: renewable energy, distributed assembly permutation flowshop scheduling, low-carbon scheduling, NSGA-Ⅱ, simulated annealing

摘要： 为实现企业的绿色制造转型,在保障生产效率的同时减少生产过程中的碳排放,以最小化最大完工时间和总碳排放量作为优化目标构建了考虑可再生能源的分布式装配置换流水车间调度问题(DAPFSP-RE)的数学模型。提出一种两阶段的NSGA-Ⅱ和模拟退火算法(TNSA)对其求解,其中设计了一种包含低碳工厂选择策略和能源调度的解码方法;第一阶段采用反向学习初始化生成双种群结构,通过交叉和变异、自适应精英保留的二元锦标赛选择操作对解空间进行搜索;第二阶段采用带档案的多目标模拟退火算法(AMOSA)对第一阶段求得的非支配解集进行模拟退火操作来跳出局部最优。最后,通过对不同规模的算例进行仿真实验,验证了可再生能源对生产过程碳减排的重要作用和所提算法求解DAPFSP -RE的有效性和竞争力。

关键词: 可再生能源, 分布式装配流水车间调度, 低碳调度, 带精英策略的非支配排序遗传算法, 模拟退火