分时电价下多目标绿色可重入混合流水车间调度

针对多目标绿色可重入混合流水车间调度问题（RHFSP）的特点，在机器分配和工序排序的基础上引入分时电价机制，构建了以最小化最大完工时间、总能耗成本和碳排放为目标的绿色调度优化模型，提出了一种改进的多目标文化基因算法（MOMA）来求解该问题，通过数值实验验证了所设计的MOMA算法的可行性。实验结果表明MOMA算法在非劣解的收敛性、多样性和支配性指标方面都显著优于多目标蚁狮优化算法(MOALO)、多目标粒子群优化算法（MOPSO）和带精英策略的非支配排序遗传算法（NSGA-Ⅱ），四种算法的分布性指标无显著差异。所提出的模型能够使企业有效避开高电价时段作业，合理转移用电负荷，达到降低总用电成本和碳排放的目的。

Multi-objective Green Re-entrant Hybrid Flow Shop Scheduling under Time-of-use Electricity Tariffs

Aiming at the characteristics of multi-objective green RHFSP, based on the machine allocation and operation sequencing,the TOU electricity tariffs were introduced to establish a green scheduling optimization model aiming at minimizing the maximum completion time, total energy consumption cost and carbon emission herein.Then, an improved MOMA was proposed to solve the problems.Finally, numerical experimental results verified the feasibility of solving scheduling problems by the designed MOMA.Results show that MOMA is significantly better than multi-objective ant lion optimization algorithm (MOALO) , multi-objective particle swarm optimization algorithm (MOPSO)and elitist non-dominated sorting genetic algorithm (NSGA-Ⅱ) in the field of convergence，diversity and dominance of the solutions, but for the four algorithms there is no significant difference in the distribution metric.The proposed model may help enterprises to avoid high price period effectively, transfer power load reasonably and achieve the purpose of reducing the energy consumption costs and carbon emission.