考虑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 shop scheduling considering the total carbon emissions and total cost of trucks

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:配送环节是工业生产不可或缺的一部分，在配送环节中，合理的配送安排不仅能降低企业生产成本，还能降低货车总碳排放量，协助企业完成节能减排的任务。许多制造企业通过构建生产与配送一体化的运作模式，以达到减少工厂生产成本与工业碳排放的目的。因此本文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。使用了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以及全局领导者档案的维护方案。在仿真实验中，根据某生活用纸制造企业的真实数据生成多组算例，用于测试算法的性能。结果表明，在10个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约3.29%，平均货车总碳排放量平均降低了约11.1%。

Abstract:The distribution process is an indispensable part of industrial production. Reasonable distribution arrangements in the distribution process can not only reduce the production costs of enterprises, but also reduce the total carbon emissions of trucks, and assist enterprises in completing the task of energy conservation and emission reduction. Many manufacturing companies achieve the goal of reducing factory production costs and industrial carbon emissions by building an integrated operation mode of production and distribution. Therefore, this article constructs a distributed flexible flow workshop scheduling and logistics collaborative optimization model, with total cost and total carbon emissions of trucks as optimization objectives. We used multi-objective particle swarm optimization based framework to improve the global leader selection strategy and maintenance plan for the global leader profile. In the simulation experiment, multiple sets of examples are generated based on real data from a certain household paper manufacturing enterprise to test the performance of the algorithm. The results showed that compared with other particle swarm optimization algorithm in 10 cases, the improved multi-objective particle swarm optimization reduced the average total cost by about 3.29% and the average total carbon emissions of trucks by about 11.1%.