Vol.26, No.4, 2020, pp.715-723, doi:10.32604/iasc.2020.010105
Dynamic Production Scheduling for Prefabricated Components Considering the Demand Fluctuation
  • Juan Du1,*, Peng Dong2, Vijayan Sugumaran3
1 SILC Business School, Shanghai University, Shanghai 201800, China, and Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW 2007, Australia.
2 SILC Business School, Shanghai University, Shanghai 201800, China.
3 School of Business Administration, Oakland University, Rochester, MI 48309 USA, and Center for Data Science and Big Data Analytics, Oakland University, Rochester, MI 48309, USA.
* Corresponding Author: Juan Du, ritadu@shu.edu.cn
A dynamic optimized production scheduling which takes into account demand fluctuation and uncertainty is very critical for the efficient performance of Prefabricated Component Supply Chain. Previous studies consider only the conditions in the production factory and develop corresponding models, ignoring the dynamic demand fluctuation that often occurs at the construction site and its impact on the entire lifecycle of prefabricated construction project. This paper proposes a dynamic flow shop scheduling model for prefabricated components production, which incorporates demand fluctuation such as the advance of due date, insertion of urgent component and order cancellation. An actual prefabrication construction project has been used to validate the proposed multi-objective genetic algorithm model. The experimental results show that the proposed model can achieve a cost saving of up to 43.2%, which shows that the proposed model can cope well with the occurrence of demand fluctuation. This research contributes to the dynamic decision support system for managing prefabricated components.
Production Scheduling, Demand fluctuation, Multi-objective genetic algorithm, Prefabricated components.
Cite This Article
J. Du, P. Dong and V. Sugumaran, "Dynamic production scheduling for prefabricated components considering the demand fluctuation," Intelligent Automation & Soft Computing, vol. 26, no.4, pp. 715–723, 2020.
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