分布式工厂中微型制造单元多目标优化

Multi-objective optimization of minicells in distributed factories

由于各地区存在资源禀赋和产业政策差异,分布式生产对提升制造企业竞争力的作用非常重要,如何利用分布式生产增强大规模定制的柔性是提振消费信心需要解决的重要问题。结合微型制造单元的思想,在多市场多类型产品的分布式混流生产情景下,以最小化人工和转运等运营成本以及最大完工时间为目标,提出分布式工厂构建和生产调度集成模型,以求解微型单元构建、工人和机器配置和各批次产品的生产策略。所提模型能帮助企业实现产能快速释放和合理混流生产,从而实现满足多区域、多产品和差异化需求的分布式制造与销售,并在确保产量的同时降低制造过程中的运营成本。此外,设计多目标粒子群优化(MOPSO)算法求解模型,并将它与非支配排序遗传算法Ⅱ(NSGA-Ⅱ)和多目标模拟退火(MOSA)算法进行比较。大规模数值实验的结果表明,在相同的运行时间内,MOPSO算法在解集支配覆盖率(CM)、平均理想距离(MID)和最大分散度(MS)这3个指标上均优于NSGA-Ⅱ和MOSA算法。所提算法可以为微型化分布式生产系统提供高质量的生产运作决策方案。

Due to differences in resource endowments and industrial policies among different regions,the role of distributed production in improving the competitiveness of manufacturing enterprises is very important.How to use distributed production to enhance the flexibility of mass customization is an important problem to be solved to boost consumer confidence.Combined with the idea of minicells—small manufacturing cells,in the distributed mixed production scenario with the multi-market and multi-product characteristics,an integrated model of distributed factory construction and production scheduling was proposed with the objectives to minimize the operating costs(e.g.,labor and transportation costs)and minimize the makespan.By the proposed model,the minicell construction,worker and machine configuration,as well as production strategies for each batch of products were able to be solved.With the help of the proposed model,the enterprises were able to realize the quick release of production capacity and reasonable mixed flow production,so as to realize distributed manufacturing and sales that meet the multi-region,multi-product,and differentiated needs,and reduce the operating cost in the manufacturing process while guaranteeing the throughput.In addition,a Multi-Objective Particle Swarm Optimization(MOPSO)algorithm was designed to solve the proposed model,and was compared with Non-Dominated Sorting Genetic AlgorithmⅡ(NSGA-Ⅱ)and Multi-Objective Simulated Annealing(MOSA)algorithm.The results of extensive numerical experiments show that MOPSO algorithm outperforms NSGA-Ⅱand MOSA algorithm with the same running time in terms of three metrics:C-Metric(CM),Mean Ideal Distance(MID)and Maximum Spread(MS).The proposed algorithm can provide a high-quality decision-making scheme of production operation for the miniaturized distributed production system.