Simulation Diagnosis for the Bottleneck of Production Lines and Its Application
Jun Zhou, Zhan Qiang Liu, Pan Huang, Xing Ai, Jian Xin Deng
Abstract
Jun Zhou, Zhan Qiang Liu, Pan Huang, Xing Ai, Jian Xin Deng
Abstract
Traditional bottleneck diagnosis of production lines mainly relies on the designer’s experience, theoretical calculation or data analysis after practical system run. There are some disadvantages of the traditional bottleneck diagnosis for production lines. Using the virtual manufacturing technologies, a novel Simulation Diagnosis methodology about the Bottleneck of Production Lines (SDBPL) has been presented. With the proposed method, an existed production line is modeled, simulated and analyzed. Its bottleneck is quickly diagnosed by some ways such as visual simulation results, cycle time, machine utilization and labor utilization. Results reveal that SDBPL can find the location of bottleneck in the practical production lines without wasting practical resource because the entire course of the diagnosis for bottleneck is implemented in virtual environment, and SDBPL is quicker than that of traditional methods as well.
OpenAlex reports 5 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
Traditional bottleneck diagnosis of production lines mainly relies on the designer’s experience, theoretical calculation or data analysis after practical system run. There are some disadvantages of the traditional bottleneck diagnosis for production lines. Using the virtual manufacturing technologies, a novel Simulation Diagnosis methodology about the Bottleneck of Production Lines (SDBPL) has been presented. With the proposed method, an existed production line is modeled, simulated and analyzed. Its bottleneck is quickly diagnosed by some ways such as visual simulation results, cycle time, machine utilization and labor utilization. Results reveal that SDBPL can find the location of bottleneck in the practical production lines without wasting practical resource because the entire course of the diagnosis for bottleneck is implemented in virtual environment, and SDBPL is quicker than that of traditional methods as well.
Key concepts: Bottleneck, Production line, Production (economics), Computer science, Resource (disambiguation), Line (geometry), Industrial engineering, Real-time computing