A reactive decentralized coordination algorithm for event-driven production planning and control: A cyber-physical production system prototype case study
Journal of Manufacturing Systems, ISSN: 0278-6125, Vol: 58, Page: 143-158
2021
- 22Citations
- 81Captures
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Concepts and expectations of cyber-physical production systems (CPPSs) underscore their advantages over the present bespoke manufacturing systems. However, transitioning from traditional manufacturing to its CPPS counterpart remains a challenge. Although a CPPS can be realized, a gap between theory and practice exists considering the response of a CPPS to system disturbances, such as machine breakdowns and change in customer orders. Furthermore, the CPPS environment is stochastic; therefore, planning and control algorithms to reduce the discrepancy between the planned and actual system levels in real time is essential. In this paper, we present a CPPS prototype and a reactive production planning algorithm. Consistent with the literature on CPPS, we developed a moderately automated CPPS and a decentralized coordination mechanism that harnesses the inherent decentralized structure of the CPPS. Moreover, we have described the design and technologies used in building this CPPS. Through a production planning mathematical model, we proposed a control mechanism and measured the efficiency of the proposed algorithm under dynamic events based on work-in-progress inventories, throughput, and delayed demand. The decentralized algorithm determines a plan prior to start of production, which is re-optimized in case any dynamic events occur subsequent to the course of the production horizon. The results of the numerical investigation indicated that the proposed decentralized coordination algorithm outperforms other centralized planning algorithms on more than 50 % of the performance measures considered.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0278612520301898; http://dx.doi.org/10.1016/j.jmsy.2020.11.002; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85097342209&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0278612520301898; https://api.elsevier.com/content/article/PII:S0278612520301898?httpAccept=text/xml; https://api.elsevier.com/content/article/PII:S0278612520301898?httpAccept=text/plain; https://dul.usage.elsevier.com/doi/; https://dx.doi.org/10.1016/j.jmsy.2020.11.002
Elsevier BV
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know