基于货车总碳排放量与总成本的分布式柔性流水车间调度的多目标优化

Multi-objective Optimization of Distributed Flexible Flow Workshop Scheduling Based on the Total Carbon Emissions of Trucks and Total Cost

关键词:  生产调度  分布式柔性流水车间  货车总碳排放量  多目标粒子群算法

Key Words:production scheduling  distributed flexible flow workshop  total carbon emissions of trucks  multi-objective particle swarm optimization

摘要:本研究构建了分布式柔性流水车间调度与物流协同优化模型，将总成本和货车总碳排放量作为优化目标；使用基于多目标粒子群算法的框架，改进了全局领导者选择策略及全局领导者档案的维护方案；根据某生活用纸制造企业的真实数据进行仿真实验，生成多组算例，用于测试算法的性能。结果表明，上述2种改进方案均能够有效提升多目标粒子群算法寻找最优解的能力。在10组算例中，与现有的粒子群算法相比，改进后的多目标粒子群算法平均总成本平均降低了3.29%，平均货车总碳排放量平均降低了11.1%。

Abstract:In this study， a model of distributed flexible flow workshop scheduling and logistics collaborative optimization was constructed， with total cost and total carbon emissions of trucks as the optimization objectives. The multi-objective particle swarm optimization based framework was used to improve the global leader selection strategy and maintenance plan for the global leader profile. The simulation experiments were conducted， based on the real data of a tissue paper manufacturing enterprises， and multiple sets of examples were generated for testing the performance of the algorithm. The results showed that the above two improved strategies could effectively enhance the ability of multi-objective particle swarm optimization algorithm to find the optimal solution. Compared with other particle swarm optimization algorithm in 10 cases， the improved multi-objective particle swarm optimization reduced the average total cost by 3.29% and the average total carbon emissions of trucks by 11.1%.