期刊
ENERGIES
卷 15, 期 5, 页码 -出版社
MDPI
DOI: 10.3390/en15051884
关键词
many-objective flexible job shop scheduling problem; memetic algorithm; production management; intelligent manufacturing; many-objective optimization
资金
- National Key R&D Program of China [2018AAA0101703]
With the rise of customized product requirements, manufacturing products are facing challenges of diversity and small-batch production. This study introduces a multi-objective flexible job shop scheduling model and optimization method SV-MA, which can improve production efficiency and reduce energy consumption.
With the increasingly customized product requirements of customers, the manufactured products have the characteristics of multi-variety and small-batch production. A high-quality production scheduling scheme can reduce energy consumption, improve production capacity and processing quality of the enterprise. The high-dimensional many-objective green flexible job shop scheduling problem (Ma-OFJSSP) urgently needs to be solved. However, the existing optimization method are difficult to effectively optimize the Ma-OFJSSP. This study proposes a many-objective flexible job shop scheduling model. An optimization method SV-MA is designed to effectively optimize the Ma-OFJSSP model. The SV-MA memetic algorithm combines an improved strength Pareto evolution method (SPEA2) and the variable neighborhood search method. To effectively distinguish the better solutions and increase the selection pressure of the non-dominated solutions, the fitness calculation method based on the shift-based density estimation strategy is adopted. The SV-MA algorithm designs the variable neighborhood strategy which combines with scheduling knowledge. Finally, in the workshop scheduling benchmarks and the machining workshop engineering case, the feasibility and effectiveness of the proposed model and SV-MA algorithm are verified by comparison with other methods. The production scheduling scheme obtained by the proposed model and SV-MA optimization algorithm can improve production efficiency and reduce energy consumption in the production process.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据