期刊
IEEE ACCESS
卷 10, 期 -, 页码 74747-74764出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2022.3191465
关键词
Mass customization; process industry lot sizing and scheduling; uncertain market information; product switching
资金
- National Natural Science Foundation of China [71801160]
- Liaoning Provincial Department of Education Scientic Research Funding Project [WJGD2019002]
This paper proposes a new process industry multi-product switching production lot sizing and scheduling model under the background of mass customization, aiming to minimize the maximum completion time and total switching cost. By integrating factors such as equipment switching costs and material conversion rates, the model brings the production system closer to reality and provides a new concept for actual workshop scheduling management.
With the gradual diversification of customer demand, to improve the rapid response ability of enterprises, this paper fully considers uncertain market information under the background of mass customization and establishes a new process industry multi-product switching production lot sizing and scheduling model with the goal of minimizing the maximum completion time and total switching cost. Fuzzy chance-constrained programming is used to explicitly incorporate market demand with uncertain quantities into the model. Starting from reality, this paper considers the switching cost of equipment when processing multiple varieties of products and skillfully integrates the conversion rate of materials during processing into the novel model, making the entire production system closer to the real state. It provides a new concept to consider cost reduction for actual workshop scheduling management. In addition, this paper proposes an improved multi-objective genetic particle swarm optimization (SMOPSO-IIs) algorithm, and the basic parameters are tested by RSM method. The optimal parameters pc = 0.6, pm = 0.06, alpha = 0.25, beta = 4 are obtained. They are substituted into SMOPSO-IIs to simulate and solve the model. The operation results show that the Pareto solution obtained by the SMOPSO-IIs algorithm is better overall. Finally, the model is solved by example simulation, and the operation results are analyzed along with a scheduling Gantt chart to verify its applicability and effectiveness. The model presented in this paper can be used to further shorten the gap between production theory and practical application and improve the current workshop scheduling management system of the process industry.
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