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Production scheduling of prefabricated components considering delivery methods
Shuqiang Wang1,2, Xi Zhang3,4
1School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan, 430068, People's Republic of China.
Scientific Reports
|September 12, 2023
Summary
This study introduces the Genetic Grey Wolf Optimizer (GGA) to optimize prefabricated component production scheduling. The hybrid algorithm minimizes storage costs by considering delivery characteristics and production resources.
Area of Science:
- Industrial Engineering
- Operations Research
- Computational Intelligence
Background:
- Production scheduling for prefabricated components faces challenges with multiple molds, components, and floors.
- Existing methods often neglect storage needs linked to delivery, increasing costs.
- Intelligent algorithms offer potential for enhancing production scheduling efficiency.
Purpose of the Study:
- To optimize the production scheduling of prefabricated components.
- To develop a model that minimizes storage costs by considering production resources and delivery constraints.
- To introduce a hybrid algorithm combining genetic and Grey Wolf algorithms for optimal scheduling.
Main Methods:
- Proposing the Genetic Grey Wolf Optimizer (GGA), a hybrid approach combining the Grey Wolf algorithm and the genetic algorithm.
- Developing a production objective function model that incorporates production resources and delivery characteristics.
- Utilizing a case study to validate the proposed model and algorithm.
Main Results:
- The Genetic Grey Wolf Optimizer (GGA) successfully identified the optimal precast production schedule.
- The proposed model effectively minimized storage costs associated with prefabricated component delivery.
- Experimental validation confirmed the efficiency and reasonableness of the developed scheduling plan.
Conclusions:
- The hybrid Genetic Grey Wolf Optimizer (GGA) is effective for optimizing prefabricated component production scheduling.
- Considering delivery characteristics in scheduling significantly reduces unnecessary storage costs.
- The developed model provides a practical and efficient approach for precast production planning.
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